authors/glebbelov/modeling-tips/tip8_aircrew_trainees_seniority.ipynb

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    "# Aircrew trainee scheduling with seniority constraints\n",
    "[![tip8_aircrew_trainees_seniority.ipynb](https://img.shields.io/badge/github-%23121011.svg?logo=github)](https://github.com/ampl/colab.ampl.com/blob/master/authors/glebbelov/modeling-tips/tip8_aircrew_trainees_seniority.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ampl/colab.ampl.com/blob/master/authors/glebbelov/modeling-tips/tip8_aircrew_trainees_seniority.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/ampl/colab.ampl.com/blob/master/authors/glebbelov/modeling-tips/tip8_aircrew_trainees_seniority.ipynb) [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/ampl/colab.ampl.com/blob/master/authors/glebbelov/modeling-tips/tip8_aircrew_trainees_seniority.ipynb) [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ampl/colab.ampl.com/blob/master/authors/glebbelov/modeling-tips/tip8_aircrew_trainees_seniority.ipynb) [![Hits](https://h.ampl.com/https://github.com/ampl/colab.ampl.com/blob/master/authors/glebbelov/modeling-tips/tip8_aircrew_trainees_seniority.ipynb)](https://colab.ampl.com)\n",
    "\n",
    "Description: Aircrew trainee scheduling with simpler seniority modeling\n",
    "\n",
    "Tags: trainee scheduling, aircrew scheduling, employee scheduling, seniority constraints, seniority ranking, preferential bidding system, multiple objectives, lexicographic optimization, amplpy\n",
    "\n",
    "Notebook author: Gleb Belov <<gleb@ampl.com>>\n",
    "\n",
    "References:\n",
    "1. Kozanidis, G. (2017). Optimal assignment of aircrew trainees to simulator and classroom training sessions subject to seniority and preference restrictions. Journal of Air Transport Management 59, 143-154.\n",
    "2. Gamache, M., Soumis, F., Villeneuve, D., Desrosiers, J., & Gélinas, É. (1998). The preferential bidding system at Air Canada. Transportation Science, 32(3), 246-255.\n",
    "3. Achour, H., Gamache, M., Soumis, F., & Desaulniers, G. (2007). An exact solution approach for the preferential bidding system problem in the airline industry. Transportation Science, 41(3), 354-365."
   ]
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   "source": [
    "# Install dependencies\n",
    "%pip install -q amplpy pandas matplotlib tabulate"
   ]
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   "source": [
    "# Google Colab & Kaggle integration\n",
    "from amplpy import AMPL, ampl_notebook\n",
    "\n",
    "ampl = ampl_notebook(\n",
    "    modules=[\"highs\", \"gurobi\"],  # modules to install\n",
    "    license_uuid=\"default\",  # license to use\n",
    ")  # instantiate AMPL object and register magics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SOLVER = \"highs\"  # Select solver: highs, gurobi, copt, cplexmp, ...\n",
    "# When running locally and solver is installed,\n",
    "# don't need the \"modules\" and \"license_uuid\" in ampl_notebook"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem statement\n",
    "\n",
    "This notebook considers a realistic trainee scheduling problem from [1] involving classroom capacities, language and (reverse) seniority constraints, and seniority-ranked preferences. In our implementation, the seniority constraints are modeled simply and efficiently by Preferential Bidding System (PBS)-style secondary objective functions. An interactive app based on this notebook is available on [AMPL Streamlit](https://amplopt.streamlit.app/Aircrew_Training_Scheduling).\n",
    "\n",
    "In the next two sections we highlight the new approach and walk through a small example, followed by AMPL implementation.\n",
    "\n",
    "**Sets:**\n",
    "- $I$ - set of crew trainees,\n",
    "- $T$ - set of training sessions,\n",
    "- $V_i$ - set of valid sessions of trainee $i\\in I$,\n",
    "- $Pos$ - set of crew positions:  captain (CP), first officer (FO), purser (PU), or flight attendant (FA),\n",
    "- $Grp$ - set of trainee groups: all, cockpit (CK), cabin (CB).\n",
    "\n",
    "**Parameters:**\n",
    "- $P_i\\in Pos$ - position of trainee $i\\in I$,\n",
    "- $S_i$ - seniority. The smaller this number, the more senior the corresponding crew member,\n",
    "- $E_i$ - flight capability expiration. 0: end of the current month, 1: end of the next month, 2: end of the next to the following month,\n",
    "- $L_i$ - spoken language. 0: the trainee speaks both of the alternative languages, 1: only the first language, 2: only the second language,\n",
    "- $Pr_{it}$ - priority of trainee $i$ for valid session $t$. Priorities are non-negative integers. Higher priorities mean higher preference. Zero priority means that the trainee does not want to be assigned to this session. All positive priorities for the same trainee should be different, while zero priority can be expressed for multiple sessions,\n",
    "- $MAX_p$ - capacity of each training session for trainees of position $p\\in Pos$,\n",
    "- $MAX^+_g$ - capacity of each training session for trainees of group $g\\in Grp$.\n",
    "\n",
    "The aim is to find an optimal assignment of trainees to training sessions which minimizes the number of training slots that remain empty, while also satisfying a list of hard and soft requirements. The satisfaction of the hard restrictions is unnegotiable, whereas the soft ones must be satisfied to the greatest possible extent. The **hard restrictions** that must be satisfied are the following:\n",
    "- Capacity constraints for each training session,\n",
    "- One of the two alternative languages must be selected for each session,\n",
    "- Every crew member with expiration 0 must be assigned to one of his/her valid sessions,\n",
    "- Reverse expiration and reverse seniority restrictions. Suppose that there exists a slot in a training session, which is only valid to a specific subset of crew members, none of which has expressed positive preference for being assigned to this session. Since the minimization of the total number of empty slots is the model's primary objective, one of these crew members must eventually be assigned to this session. The reverse expiration restrictions, considered first, dictate that this should be a crew member with the lowest expiration (his/her flight capability should expire sooner) among them. Assume now, that two or more trainees have the lowest expiration. The reverse seniority restrictions, considered next, dictate that this should be the least senior trainee among all the trainees with the lowest expiration. Such restrictions can be called \"expiration- and seniority-ranked anti-preferences\".\n",
    "\n",
    "Additionally, the following **soft restrictions** must be satisfied:\n",
    "- The maximum possible preference satisfaction for each crew member must be attained, according to strict seniority. This means that no crew member's preference should be satisfied at the expense of the dissatisfaction of a more senior crew member's preference.\n",
    "- The maximum possible preference satisfaction of each individual crew member must be attained, according to the list of priorities that he/she has expressed."
   ]
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   "source": [
    "## The approach\n",
    "\n",
    "### Overview\n",
    "\n",
    "- **Phase 1.** Minimize the number of unassigned trainees.\n",
    "- **Phase 2.** Maximize trainee preferences, ranked by seniority. See objectives `PrefViolRanked` in the AMPL model. Difference to [1]: assignment of unwanted slots ($Pr_{it}=0$) is not penalized, so that the next phase can resolve reverse expiration/seniority conflicts.\n",
    "- **Phase 3.** Resolve reverse expiration/seniority conflicts as follows (objectives `ReverseSeniority`):\n",
    "  - Maximize assignments of trainees with expiration 1 month, ranked by reverse seniority.\n",
    "  - The same for trainees with expiration 2 months.\n",
    "- **Phase 4.** (*optional*) Minimize session load disbalances.\n",
    "\n",
    "### Differences to the original approach [1]\n",
    "\n",
    "- The reverse expiration and seniority constraints (\"anti-peferences\") are sublimated via Preferential Bidding System (PBS)-style secondary objective functions. PBS was applied in [1] only to satisfy the soft preferences. Minimizing the objective functions `ReverseSeniority` aims to satisfy constraints (8), (9), (22-25), and (26–29) from [1], as well as to resolve the ‘pitfall’ from Section 3.1.\n",
    "- The lexicographic approach for (anti-)preference ranking by expiration/seniority is implemented via multiple objectives submitted to the solver in a single model.\n",
    "No variables are fixed while optimizing the different objectives.\n",
    "- We also implement the original hard \"anti-preference\" constraints for comparison and improve them by considering language compatibility.\n",
    "\n",
    "### Correctness\n",
    "\n",
    "**Theorem.**\n",
    "*The new multi-objective approach satisfies the reverse expiration/seniority constraints and resolves the 'pitfall' from Section 3.1 [1]. The approach results in the same or better preference satisfaction.*\n",
    "\n",
    "**Proof sketch.**\n",
    "- In Phase 2 we don't penalize unwanted assignments. This allows Phase 3 to prioritize such assignments appropriately.\n",
    "- Phase 2 maximizes positive preferences equivalently to [1]. It can result in better satisfaction due to not fixing individual assignments, and due to Phase 3 being compatible with language assignments. See section Examples for discussion.\n",
    "\n",
    "\n",
    "### Decision variables\n",
    "\n",
    "Decision variables are the same as in [1]:\n",
    "- $x_{it}$ - binary decision variable, takes value $1$ *iff* trainee $i\\in I$ is assigned to session $t\\in V_t$,\n",
    "- $y_i$ - binary decision variable that takes the value 1 *iff* trainee i remains unassigned, $i\\in I$: $E_i = 1$ or $2$,\n",
    "- $u_t$: binary decision variable that takes the value 1 if language 1 is selected for training session t, and 0 if language 2 is selected instead, $t\\in T$,\n",
    "- $w$: total number of crew trainees that remain unassigned."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A step-by-step example\n",
    "\n",
    "Assume we have 3 trainees, 1 time slot, and the class capacity is 2. Thus, at most 2 trainees will be scheduled.\n",
    "\n",
    "Trainees' input data:\n",
    "|  Trainee   |   Position P[i] |   Seniority S[i] |   Language L[i] |   Expiration E[i] | Session priorities Pr[i,t]     | Feasible sessions V[i]    |\n",
    "|-----------:|------:|------:|------:|------:|:-------------------------|:-------------|\n",
    "|  1         |     1 |     5 |     0 |     1 | [0] | [1]       |\n",
    "|  2         |     1 |    63 |     0 |     0 | [1] | [1]       |\n",
    "|  3         |     1 |    89 |     0 |     2 | [0] | [1]       |\n",
    "\n",
    "I.e., all 3 trainees have the same position; they are ordered by falling seniority; all can speak both languages; Trainee 2's license expires this month, while others' later; Trainee 2 is ok with the single available session, while others don't want it. The reverse expiration constraints [1] dictate the assignment of Trainee 1 over Trainee 3, because both don't want the class, and Trainee 1's expiration is sooner.\n",
    "\n",
    "The multi-objective approach results in the following stepwise optimization process.\n",
    "\n",
    "- **Step 1.** Minimize the number of unassigned trainees, giving $w=1$.\n",
    "- **Step 2.** Maximize preferences of the most-senior Trainee 1. Because being not assigned is equally penalized as an unwanted assignment, there is no new information from this step.\n",
    "- **Step 3.** Maximize preferences of Trainee 2. They are assigned.\n",
    "- **Step 4.** Maximize preferences of Trainee 3. Similar to Trainee 1, no change.\n",
    "\n",
    "Next follow the new PBS objectives sublimating the strict reverse seniority constraints.\n",
    "\n",
    "- **Step 5.** For trainees expiring next month (Trainee 1), maximize the assignments in the increasing order of seniority. This results in the assignment, letting the longer-expiring Trainee 3 go free.\n",
    "- **Step 6.** Similar to Step 5, but for trainees expiring in 2 months. Makes no change in this example.\n",
    "- **Step 7.** (*optional*) Minimize session load disbalances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test instance generation\n",
    "\n",
    "Define class `Instance` holding a problem instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "class Instance:\n",
    "    def __init__(self):\n",
    "        ## Data arrays / matrices for all trainees\n",
    "        self.nT = 0\n",
    "        self.P = []\n",
    "        self.S = []\n",
    "        self.L = []\n",
    "        self.E = []\n",
    "        self.V = []\n",
    "        self.Pr = []\n",
    "        ## Capacity constraints\n",
    "        self.cap_kind = []\n",
    "        self.MAX = []\n",
    "        self.cap_plus_kind = []\n",
    "        self.cap_plus_which = []\n",
    "        self.MAXPlus = []\n",
    "\n",
    "    def ToPandas(self):\n",
    "        data = {\n",
    "            \"Pos\": self.P,\n",
    "            \"Sen\": self.S,\n",
    "            \"Lng\": self.L,\n",
    "            \"Exp\": self.E,\n",
    "            \"Pri\": [[round(pr) for pr in PrI] for PrI in self.Pr],\n",
    "            \"V\": [sorted([v - 1 for v in Vi]) for Vi in self.V],\n",
    "        }\n",
    "        df = pd.DataFrame(data)\n",
    "        return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define class `InstGenParams` with instance generation parameters according to [1], but with a configurable number of classes (originally 1/5 of the number of trainees.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InstGenParams:\n",
    "    def __init__(self, N, T):\n",
    "        self.N = N\n",
    "        self.T = T  # Set lower to have non-assignments\n",
    "        ## Ranges and probabilities\n",
    "        self.position_kind = [\"CP\", \"FO\", \"PU\", \"FA\"]\n",
    "        self.position_prob = [0.25, 0.25, 0.25, 0.25]\n",
    "        self.language_prob = [0.5, 0.25, 0.25]\n",
    "        self.expiration_prob = [0.5, 0.25, 0.25]\n",
    "        self.N_sess_rng = [1, math.floor(self.T / 2)]\n",
    "        self.pref_0 = 0.5\n",
    "        ## These are constant for all instances\n",
    "        self.cap_kind = [\"CP\", \"FO\", \"PU\", \"FA\"]\n",
    "        self.MAX = [4, 4, 4, 4]\n",
    "        self.cap_plus_kind = [\"All\", \"CK\", \"CB\"]\n",
    "        self.cap_plus_which = [[1, 2], [1, 2], [1, 3], [1, 3]]\n",
    "        self.MAXPlus = [10, 6, 6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to generate an instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GenInstance(genpar, rng):\n",
    "    inst = Instance()\n",
    "    inst.nT = genpar.T\n",
    "    inst.S = np.arange(genpar.N)  # Seniority 0..N-1\n",
    "    rng.shuffle(inst.S)\n",
    "    for itr in range(0, genpar.N):\n",
    "        inst.P.append(\n",
    "            rng.choice(\n",
    "                np.arange(1, len(genpar.position_prob) + 1),  # 1-based\n",
    "                p=genpar.position_prob,\n",
    "            )\n",
    "        )\n",
    "        inst.L.append(rng.choice(len(genpar.language_prob), p=genpar.language_prob))\n",
    "        inst.E.append(rng.choice(len(genpar.expiration_prob), p=genpar.expiration_prob))\n",
    "        N_feas_sess = rng.integers(genpar.N_sess_rng[0], genpar.N_sess_rng[1] + 1)\n",
    "        V = rng.choice(\n",
    "            np.arange(1, genpar.T + 1), size=N_feas_sess, replace=False  # 1-based\n",
    "        )\n",
    "        inst.V.append(V)\n",
    "        N_pr_0 = rng.binomial(N_feas_sess, genpar.pref_0)\n",
    "        PrByV = np.concatenate(\n",
    "            (np.zeros(N_pr_0), np.arange(1, N_feas_sess - N_pr_0 + 1))\n",
    "        )\n",
    "        assert N_feas_sess == len(PrByV)\n",
    "        rng.shuffle(PrByV)\n",
    "        Pr = np.zeros(genpar.T)\n",
    "        for k, t in enumerate(V):\n",
    "            Pr[t - 1] = PrByV[k]\n",
    "        inst.Pr.append(Pr)\n",
    "    # Capacities\n",
    "    inst.cap_kind = genpar.cap_kind\n",
    "    inst.MAX = genpar.MAX\n",
    "    inst.cap_plus_kind = genpar.cap_plus_kind\n",
    "    inst.cap_plus_which = genpar.cap_plus_which\n",
    "    inst.MAXPlus = genpar.MAXPlus\n",
    "    return inst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    |   Pos |   Sen |   Lng |   Exp | Pri                      | V            |\n",
      "|---:|------:|------:|------:|------:|:-------------------------|:-------------|\n",
      "|  0 |     3 |    11 |     1 |     0 | [0, 0, 0, 1, 0, 0, 0, 0] | [1, 3, 4]    |\n",
      "|  1 |     1 |    12 |     2 |     1 | [0, 0, 0, 1, 0, 0, 0, 0] | [3]          |\n",
      "|  2 |     4 |    95 |     0 |     0 | [0, 0, 2, 0, 1, 0, 0, 0] | [2, 4, 5]    |\n",
      "|  3 |     4 |    80 |     2 |     1 | [2, 0, 0, 0, 0, 0, 0, 1] | [0, 4, 7]    |\n",
      "|  4 |     4 |    24 |     0 |     2 | [0, 0, 0, 0, 0, 0, 0, 0] | [1, 4, 7]    |\n",
      "|  5 |     4 |    22 |     0 |     0 | [0, 0, 0, 2, 0, 0, 1, 0] | [3, 6, 7]    |\n",
      "|  6 |     3 |    94 |     0 |     0 | [2, 0, 0, 0, 1, 3, 0, 0] | [0, 4, 5]    |\n",
      "|  7 |     2 |    13 |     1 |     2 | [0, 0, 0, 0, 0, 0, 0, 0] | [0, 2, 4, 6] |\n",
      "|  8 |     4 |    28 |     1 |     2 | [0, 0, 0, 1, 0, 2, 0, 0] | [0, 3, 4, 5] |\n",
      "|  9 |     3 |    78 |     0 |     0 | [0, 1, 0, 0, 0, 2, 0, 0] | [0, 1, 5]    |\n",
      "| 10 |     1 |     5 |     2 |     0 | [1, 0, 0, 0, 0, 0, 2, 0] | [0, 6]       |\n",
      "| 11 |     2 |    77 |     1 |     0 | [0, 0, 2, 0, 1, 0, 0, 0] | [0, 2, 3, 4] |\n",
      "| 12 |     1 |    63 |     2 |     0 | [0, 0, 0, 0, 0, 1, 0, 0] | [2, 5]       |\n",
      "| 13 |     1 |    34 |     2 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [6]          |\n",
      "| 14 |     3 |    40 |     2 |     1 | [1, 0, 2, 0, 0, 0, 0, 0] | [0, 2, 4, 5] |\n",
      "| 15 |     1 |    18 |     2 |     2 | [0, 0, 0, 0, 0, 0, 1, 0] | [6]          |\n",
      "| 16 |     1 |    30 |     0 |     1 | [0, 0, 2, 3, 0, 0, 0, 1] | [2, 3, 5, 7] |\n",
      "| 17 |     4 |     4 |     1 |     1 | [0, 0, 0, 1, 0, 0, 0, 2] | [3, 4, 6, 7] |\n",
      "| 18 |     4 |    98 |     0 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [2]          |\n",
      "| 19 |     4 |    16 |     2 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [0, 4]       |\n",
      "| 20 |     3 |    92 |     2 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [3]          |\n",
      "| 21 |     3 |    54 |     2 |     0 | [0, 1, 0, 2, 0, 0, 0, 0] | [1, 3]       |\n",
      "| 22 |     4 |    85 |     2 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [4]          |\n",
      "| 23 |     1 |    99 |     0 |     0 | [1, 0, 0, 0, 0, 0, 0, 2] | [0, 7]       |\n",
      "| 24 |     2 |     8 |     0 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [3]          |\n",
      "| 25 |     3 |    45 |     1 |     2 | [0, 0, 0, 0, 1, 0, 0, 0] | [1, 3, 4]    |\n",
      "| 26 |     1 |    48 |     0 |     1 | [1, 3, 2, 0, 0, 0, 0, 0] | [0, 1, 2, 6] |\n",
      "| 27 |     2 |    90 |     2 |     0 | [0, 0, 0, 0, 0, 0, 1, 0] | [6]          |\n",
      "| 28 |     2 |    73 |     2 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [3]          |\n",
      "| 29 |     4 |    59 |     2 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [4, 6]       |\n",
      "| 30 |     4 |    14 |     1 |     0 | [0, 0, 0, 2, 1, 0, 0, 0] | [1, 3, 4, 7] |\n",
      "| 31 |     1 |    42 |     2 |     2 | [0, 0, 0, 0, 0, 0, 0, 0] | [0, 3]       |\n",
      "| 32 |     1 |    57 |     0 |     0 | [1, 0, 0, 0, 0, 0, 2, 0] | [0, 6]       |\n",
      "| 33 |     1 |     1 |     1 |     1 | [0, 0, 2, 0, 0, 0, 0, 1] | [2, 3, 7]    |\n",
      "| 34 |     4 |    55 |     2 |     1 | [0, 0, 0, 0, 0, 2, 0, 1] | [2, 4, 5, 7] |\n",
      "| 35 |     4 |    44 |     0 |     0 | [0, 3, 2, 0, 0, 1, 0, 0] | [1, 2, 5, 7] |\n",
      "| 36 |     3 |    68 |     2 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [4, 5]       |\n",
      "| 37 |     3 |    82 |     2 |     0 | [2, 0, 0, 0, 0, 0, 1, 0] | [0, 4, 6]    |\n",
      "| 38 |     3 |    86 |     2 |     1 | [0, 1, 0, 0, 0, 0, 0, 2] | [1, 4, 7]    |\n",
      "| 39 |     2 |    49 |     0 |     0 | [1, 0, 0, 0, 0, 0, 0, 0] | [0, 6, 7]    |\n",
      "| 40 |     4 |    64 |     0 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [4, 5, 6, 7] |\n",
      "| 41 |     3 |    91 |     0 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [4]          |\n",
      "| 42 |     3 |    79 |     1 |     0 | [0, 2, 0, 0, 1, 0, 0, 0] | [1, 3, 4, 5] |\n",
      "| 43 |     1 |    38 |     0 |     2 | [0, 0, 0, 2, 1, 0, 0, 0] | [3, 4, 5, 7] |\n",
      "| 44 |     2 |    21 |     2 |     2 | [0, 0, 1, 0, 0, 0, 0, 0] | [2, 3, 7]    |\n",
      "| 45 |     2 |    50 |     0 |     2 | [2, 0, 3, 0, 0, 0, 0, 1] | [0, 2, 6, 7] |\n",
      "| 46 |     4 |    96 |     0 |     0 | [0, 0, 0, 2, 0, 0, 1, 0] | [0, 3, 6, 7] |\n",
      "| 47 |     1 |    83 |     0 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [4]          |\n",
      "| 48 |     4 |    15 |     1 |     0 | [0, 0, 0, 0, 0, 0, 1, 0] | [1, 6, 7]    |\n",
      "| 49 |     1 |    36 |     2 |     0 | [0, 0, 0, 0, 1, 0, 0, 0] | [4]          |\n",
      "| 50 |     1 |    52 |     0 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [3, 6]       |\n",
      "| 51 |     4 |    62 |     2 |     1 | [0, 0, 0, 1, 0, 0, 0, 0] | [3]          |\n",
      "| 52 |     3 |    72 |     0 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [2, 4, 6]    |\n",
      "| 53 |     2 |    27 |     0 |     2 | [0, 0, 0, 0, 0, 1, 0, 0] | [5]          |\n",
      "| 54 |     1 |    23 |     0 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [0, 6]       |\n",
      "| 55 |     2 |    35 |     1 |     1 | [0, 0, 0, 1, 0, 0, 2, 0] | [0, 3, 6, 7] |\n",
      "| 56 |     2 |    76 |     0 |     0 | [1, 0, 0, 0, 2, 0, 0, 0] | [0, 1, 4, 7] |\n",
      "| 57 |     2 |    69 |     2 |     1 | [0, 0, 0, 0, 0, 0, 1, 0] | [1, 3, 5, 6] |\n",
      "| 58 |     2 |    47 |     1 |     0 | [0, 1, 0, 2, 0, 0, 3, 0] | [1, 3, 6, 7] |\n",
      "| 59 |     3 |     9 |     2 |     2 | [0, 0, 0, 0, 0, 0, 0, 0] | [0]          |\n",
      "| 60 |     4 |    26 |     2 |     2 | [0, 0, 0, 0, 0, 0, 1, 0] | [1, 6, 7]    |\n",
      "| 61 |     3 |    41 |     2 |     0 | [0, 0, 0, 0, 0, 0, 1, 0] | [6, 7]       |\n",
      "| 62 |     1 |    93 |     2 |     2 | [0, 0, 1, 0, 0, 0, 0, 0] | [2, 3]       |\n",
      "| 63 |     3 |    10 |     0 |     0 | [0, 1, 0, 0, 0, 0, 0, 0] | [0, 1, 5, 7] |\n",
      "| 64 |     3 |     7 |     1 |     2 | [0, 0, 0, 0, 1, 0, 2, 0] | [4, 6]       |\n",
      "| 65 |     1 |    53 |     0 |     1 | [0, 1, 0, 0, 0, 0, 0, 0] | [1]          |\n",
      "| 66 |     2 |     0 |     0 |     0 | [0, 0, 0, 0, 1, 0, 0, 0] | [4, 5, 7]    |\n",
      "| 67 |     1 |    58 |     1 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [4, 7]       |\n",
      "| 68 |     3 |    37 |     0 |     0 | [0, 2, 0, 0, 0, 1, 0, 0] | [0, 1, 5]    |\n",
      "| 69 |     2 |    75 |     2 |     0 | [0, 1, 0, 0, 0, 0, 0, 0] | [1, 2, 7]    |\n",
      "| 70 |     3 |    25 |     0 |     2 | [0, 0, 0, 0, 0, 1, 0, 0] | [2, 4, 5]    |\n",
      "| 71 |     3 |    74 |     0 |     0 | [1, 2, 0, 0, 0, 0, 0, 0] | [0, 1, 6]    |\n",
      "| 72 |     2 |    31 |     0 |     2 | [0, 0, 3, 1, 0, 0, 0, 2] | [2, 3, 7]    |\n",
      "| 73 |     4 |    17 |     0 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [4]          |\n",
      "| 74 |     1 |    97 |     2 |     1 | [0, 0, 0, 0, 1, 0, 0, 0] | [3, 4, 6]    |\n",
      "| 75 |     3 |    29 |     0 |     2 | [1, 0, 0, 0, 0, 0, 0, 0] | [0, 3]       |\n",
      "| 76 |     2 |    87 |     0 |     0 | [2, 0, 0, 1, 3, 0, 0, 0] | [0, 3, 4]    |\n",
      "| 77 |     4 |     6 |     1 |     1 | [0, 0, 1, 0, 0, 0, 0, 0] | [2, 7]       |\n",
      "| 78 |     3 |    71 |     2 |     2 | [0, 0, 0, 2, 1, 0, 0, 0] | [2, 3, 4]    |\n",
      "| 79 |     2 |    65 |     0 |     1 | [1, 2, 0, 0, 0, 0, 0, 0] | [0, 1, 4, 7] |\n",
      "| 80 |     1 |     3 |     0 |     2 | [0, 0, 0, 1, 0, 2, 0, 3] | [3, 4, 5, 7] |\n",
      "| 81 |     1 |    70 |     0 |     0 | [0, 1, 0, 3, 0, 0, 2, 0] | [1, 3, 4, 6] |\n",
      "| 82 |     1 |    66 |     0 |     2 | [0, 0, 0, 2, 1, 0, 0, 3] | [3, 4, 6, 7] |\n",
      "| 83 |     2 |    56 |     0 |     1 | [0, 0, 0, 0, 1, 0, 0, 2] | [0, 4, 7]    |\n",
      "| 84 |     3 |    67 |     2 |     2 | [0, 0, 0, 0, 0, 0, 0, 0] | [5]          |\n",
      "| 85 |     3 |    61 |     0 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [0]          |\n",
      "| 86 |     3 |    89 |     2 |     0 | [0, 0, 2, 1, 0, 3, 0, 0] | [1, 2, 3, 5] |\n",
      "| 87 |     3 |    51 |     2 |     2 | [0, 0, 0, 0, 2, 1, 0, 0] | [2, 4, 5]    |\n",
      "| 88 |     2 |    60 |     0 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [2]          |\n",
      "| 89 |     3 |    81 |     2 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [1, 2, 5]    |\n",
      "| 90 |     4 |    39 |     0 |     1 | [1, 0, 0, 0, 2, 0, 0, 0] | [0, 4]       |\n",
      "| 91 |     2 |    43 |     0 |     1 | [2, 0, 0, 0, 1, 0, 0, 0] | [0, 4, 6]    |\n",
      "| 92 |     4 |    88 |     0 |     0 | [0, 0, 0, 2, 1, 0, 0, 0] | [2, 3, 4]    |\n",
      "| 93 |     3 |     2 |     1 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [1, 6]       |\n",
      "| 94 |     2 |    46 |     2 |     1 | [0, 0, 0, 0, 0, 0, 0, 0] | [1, 2, 3]    |\n",
      "| 95 |     4 |    19 |     1 |     2 | [0, 0, 0, 0, 1, 0, 0, 0] | [1, 3, 4, 5] |\n",
      "| 96 |     4 |    33 |     2 |     0 | [0, 0, 0, 0, 0, 0, 1, 0] | [6]          |\n",
      "| 97 |     2 |    32 |     1 |     2 | [0, 3, 1, 0, 0, 2, 0, 0] | [1, 2, 3, 5] |\n",
      "| 98 |     1 |    84 |     1 |     2 | [1, 0, 0, 2, 0, 0, 0, 0] | [0, 3, 7]    |\n",
      "| 99 |     4 |    20 |     2 |     0 | [0, 0, 0, 0, 0, 0, 0, 0] | [3]          |\n"
     ]
    }
   ],
   "source": [
    "rng = np.random.default_rng(1234)\n",
    "\n",
    "genpar = InstGenParams(\n",
    "    100, math.ceil(100 / 13)\n",
    ")  # With the Demo license, set up to 50 trainees\n",
    "inst = GenInstance(genpar, rng)\n",
    "inst = GenInstance(genpar, rng)\n",
    "inst = GenInstance(\n",
    "    genpar, rng\n",
    ")  # The 3rd instance needs the language compatibility adjustment\n",
    "print(inst.ToPandas().to_markdown())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AMPL model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start with the base part of the model. It is saved to file `airtrainee_base.mod`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting airtrainee_base.mod\n"
     ]
    }
   ],
   "source": [
    "%%writefile airtrainee_base.mod\n",
    "\n",
    "set I;              # Trainees\n",
    "set T;              # Training sessions\n",
    "set V{I} in T;      # Valid sessions for each trainee\n",
    "set Pos;            # Positons\n",
    "set PosPlus;        # Meta-positions (e.g, All, Cockpit, Cabin)\n",
    "set MAXPlusWhich{Pos} in PosPlus;\n",
    "\n",
    "param P{I} in Pos;  # Trainee's position\n",
    "param S{I};         # Seniority (smaller value <=> higher seniority)\n",
    "param L{I};         # Language (0 - both, 1 or 2 - one only)\n",
    "param E{I};         # Expiration: 0 - this month, 1 - next month, 2 - in 2 months\n",
    "param Pr{I, T};     # Priority: 0 - not wanted, larger value <=> higher preference\n",
    "param MAX{Pos};     # Position capacity\n",
    "param MAXPlus{PosPlus};  # Aggregated capacities (All, CK, CB)\n",
    "\n",
    "var x{i in I, t in T} binary <= if t in V[i] then 1 else 0;\n",
    "var y{i in I} binary <= if E[i]>0 then 1 else 0;      # Trainee i unassigned\n",
    "var u{T} binary;    # 1 <=> language 1, 0 <=> language 2\n",
    "var w >=0;          # Number of unassigned trainees\n",
    "\n",
    "s.t. Assign_E0 {i in I: E[i]==0}:\n",
    "  sum {t in V[i]} x[i, t] == 1;\n",
    "\n",
    "s.t. Unassigned_E12 {i in I: E[i]>0}:\n",
    "  sum {t in V[i]} x[i, t] + y[i] == 1;\n",
    "\n",
    "s.t. Sum_Unassigned: w == sum {i in I: E[i]>0} y[i];\n",
    "\n",
    "s.t. Language_1 {t in T}:\n",
    "  u[t]<0.5 ==> sum {i in I: L[i]==1 and t in V[i]} x[i, t] <= 0;\n",
    "\n",
    "s.t. Language_2 {t in T}:\n",
    "  u[t]>=0.5 ==> sum {i in I: L[i]==2 and t in V[i]} x[i, t] <= 0;\n",
    "\n",
    "s.t. Capacity {p in Pos, t in T}:\n",
    "  sum {i in I: p==P[i] and t in V[i]} x[i, t] <= MAX[p];\n",
    "\n",
    "s.t. Capacity_Meta {p in PosPlus, t in T}:\n",
    "  sum {i in I: p in MAXPlusWhich[P[i]] and t in V[i]} x[i, t] <= MAXPlus[p];\n",
    "\n",
    "suffix objpriority;\n",
    "param S_range := max {i in I} S[i] - min {i in I} S[i];\n",
    "\n",
    "# The primary objective\n",
    "minimize Total_Unassigned: w suffix objpriority 3*S_range + 1;\n",
    "# let Total_Unassigned.objpriority := card(I) + 3;\n",
    "\n",
    "# Trainee preferences, ranked by seniority\n",
    "set SenLevels := setof {i in I} S[i];\n",
    "param prefMax {i in I} := max {t in V[i]} Pr[i, t];\n",
    "minimize PrefViolRanked {s in SenLevels}:\n",
    "  sum {i in I: s==S[i]}\n",
    "    (1.0                                               # penalty 1 for non-assignment or unwanted assignment\n",
    "     - sum {t in V[i]: Pr[i, t]>0}\n",
    "       Pr[i, t] / prefMax[i]                           # normalize\n",
    "        * x[i, t])\n",
    "  suffix objpriority max {j in I} S[j] + 2*S_range + 1 - s;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next comes the new Reverse Seniority modeling as another set of lexicographically ranked PBS-style objective functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting airtrainee_seniority_reverse_PBS.mod\n"
     ]
    }
   ],
   "source": [
    "%%writefile airtrainee_seniority_reverse_PBS.mod\n",
    "\n",
    "# Reverse seniority constraints,\n",
    "# sublimated as post-processing objectives.\n",
    "# Optimize for all trainees with E[i]==1, ranked by reverse seniority,\n",
    "# then for all with E[i]==2.\n",
    "# In this special case the objectives can be aggregated for each value of e\n",
    "# (see Solve with aggregated preferences.)\n",
    "maximize ReverseSeniority {e in 1..2, i in I: E[i]==e}:\n",
    "  sum {t in V[i]: Pr[i, t]==0}\n",
    "    (S[i] - min {j in I} S[j] + 1) * x[i, t]\n",
    "  suffix objpriority (2-e)*S_range + 1 + S[i] - min {j in I} S[j];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to pass an instance to the AMPL model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeAMPLInstance(models, inst):\n",
    "    ampl = AMPL()\n",
    "    for mod in models:\n",
    "        ampl.read(mod)\n",
    "    # Fill data\n",
    "    ampl.set[\"I\"] = np.arange(1, len(inst.P) + 1)\n",
    "    ampl.set[\"T\"] = np.arange(1, inst.nT + 1)\n",
    "    for i, vi in enumerate(inst.V):\n",
    "        ampl.set[\"V\"][i + 1] = vi\n",
    "    ampl.set[\"Pos\"] = np.arange(1, len(genpar.position_prob) + 1)\n",
    "    ampl.set[\"PosPlus\"] = np.arange(1, len(genpar.MAXPlus) + 1)\n",
    "    for i, vi in enumerate(inst.cap_plus_which):\n",
    "        ampl.set[\"MAXPlusWhich\"][i + 1] = vi\n",
    "    ampl.param[\"P\"] = inst.P\n",
    "    ampl.param[\"S\"] = inst.S\n",
    "    ampl.param[\"L\"] = inst.L\n",
    "    ampl.param[\"E\"] = inst.E\n",
    "    ampl.param[\"Pr\"] = pd.DataFrame(\n",
    "        data=inst.Pr, index=range(1, len(inst.P) + 1), columns=range(1, inst.nT + 1)\n",
    "    )\n",
    "    ampl.param[\"MAX\"] = inst.MAX\n",
    "    ampl.param[\"MAXPlus\"] = inst.MAXPlus\n",
    "\n",
    "    return ampl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to check reverse seniority constraints according to [1].\n",
    "\n",
    "See also `airtrainee_seniority_reverse_constraints.mod` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CheckSenRev(ampl, inst):\n",
    "    I = np.arange(len(inst.P))\n",
    "    T = np.arange(inst.nT)\n",
    "    V = [{t - 1 for t in Vi} for Vi in inst.V]  # back to 0-based\n",
    "    P = [p - 1 for p in inst.P]\n",
    "    S = inst.S\n",
    "    L = inst.L\n",
    "    E = inst.E\n",
    "    Pr = inst.Pr\n",
    "    MAX = inst.MAX\n",
    "    MAXPlus = inst.MAXPlus\n",
    "    # param G {i in I} := max {pp in MAXPlusWhich[P[i]]} pp;      # Group: CK or CB\n",
    "    GC = [max(w) - 1 for w in inst.cap_plus_which]\n",
    "    G = [GC[P[i]] for i in I]\n",
    "\n",
    "    x = ampl.get_variable(\"x\").get_values().to_pandas().unstack()\n",
    "    y = [1 - x.iloc[i].sum(axis=0) for i in I]  ## y[i]==1 means i not scheduled\n",
    "    u = ampl.get_variable(\"u\").get_values().to_pandas().unstack()\n",
    "    nViol = 0\n",
    "    result_msg = \"\"\n",
    "\n",
    "    for i in I:\n",
    "        for t in V[i]:\n",
    "            if (\n",
    "                E[i] > 0\n",
    "                and Pr[i][t] == 0\n",
    "                and round(y[i]) == 1  ## This trainee not scheduled at all\n",
    "                and round(u.iloc[t]) + 1 != L[i]  ## != means compatible language\n",
    "            ):\n",
    "                JP = {\n",
    "                    j\n",
    "                    for j in I  ## Dominated (non-preferred) trainees with same position\n",
    "                    if (\n",
    "                        P[j] == P[i]\n",
    "                        and t in V[j]\n",
    "                        and Pr[j][t] == 0\n",
    "                        and ((E[j] == E[i] and S[j] < S[i]) or E[j] > E[i])\n",
    "                    )\n",
    "                }\n",
    "                JG = {\n",
    "                    j\n",
    "                    for j in I  ## Dominated trainees in the same group but diff position\n",
    "                    if (\n",
    "                        P[j] != P[i]\n",
    "                        and G[j] == G[i]\n",
    "                        and t in V[j]\n",
    "                        and Pr[j][t] == 0\n",
    "                        and ((E[j] == E[i] and S[j] < S[i]) or E[j] > E[i])\n",
    "                    )\n",
    "                }\n",
    "                JA = {\n",
    "                    j\n",
    "                    for j in I  ## Dominated trainees in the other group\n",
    "                    if (\n",
    "                        G[j] != G[i]\n",
    "                        and t in V[j]\n",
    "                        and Pr[j][t] == 0\n",
    "                        and ((E[j] == E[i] and S[j] < S[i]) or E[j] > E[i])\n",
    "                    )\n",
    "                }\n",
    "                ## Now: dominating (preferred for this slot) sets\n",
    "                DJP = {\n",
    "                    j\n",
    "                    for j in I  ## Dominating trainees with same position\n",
    "                    if (\n",
    "                        P[j] == P[i]\n",
    "                        and t in V[j]\n",
    "                        and (\n",
    "                            E[j] == 0\n",
    "                            or (E[j] == E[i] and (S[j] > S[i] or Pr[j][t] > 0))\n",
    "                            or (E[j] > E[i] and Pr[j][t] > 0)\n",
    "                        )\n",
    "                    )\n",
    "                }\n",
    "                DJG = {\n",
    "                    j\n",
    "                    for j in I  ## Dominating trainees in the same group\n",
    "                    if (\n",
    "                        G[j] == G[i]\n",
    "                        and t in V[j]\n",
    "                        and (\n",
    "                            E[j] == 0\n",
    "                            or (E[j] == E[i] and (S[j] > S[i] or Pr[j][t] > 0))\n",
    "                            or (E[j] > E[i] and Pr[j][t] > 0)\n",
    "                        )\n",
    "                    )\n",
    "                }\n",
    "                kind = [\"same_pos\", \"same_group_diff_pos\", \"diff_group\"]\n",
    "                z_it = round(sum([x.iloc[j, t] for j in DJP])) >= MAX[P[i]]\n",
    "                u2_it = round(sum([x.iloc[j, t] for j in DJG])) >= MAXPlus[G[i]]\n",
    "                setU = [JP, JG, JA]\n",
    "                setD = [[], [DJP], [DJP, DJG]]\n",
    "                flgD = [[], [z_it], [z_it, u2_it]]\n",
    "                for iDom in range(3):\n",
    "                    # There are non-preferred trainees scheduled\n",
    "                    if sum([round(x.iloc[j, t]) for j in setU[iDom]]) >= 1 and (\n",
    "                        len(flgD[iDom]) == 0\n",
    "                        or max(flgD[iDom]) == 0  ## Relevant capacity free\n",
    "                    ):\n",
    "                        nViol += 1\n",
    "                        result_msg = (\n",
    "                            result_msg\n",
    "                            + \"\\n    RevSen VIOLATION: kind={}, [i, t]=[{}, {}], S[i]={}, E[i]={}, L[i]={}, NonPref={}, Preferred={}, flgD={}\".format(\n",
    "                                kind[iDom],\n",
    "                                i,\n",
    "                                t,\n",
    "                                S[i],\n",
    "                                E[i],\n",
    "                                L[i],\n",
    "                                setU[iDom],\n",
    "                                setD[iDom],\n",
    "                                flgD[iDom],\n",
    "                            )\n",
    "                        )\n",
    "\n",
    "    result_msg = (\n",
    "        result_msg\n",
    "        + \"\\n     ---- \"\n",
    "        + str(nViol)\n",
    "        + \" original reverse seniority violations (with language compatibility).\"\n",
    "    )\n",
    "    return nViol == 0, result_msg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solution statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SolveStats:\n",
    "    def __init__(self):\n",
    "        self.solve_result_ = []\n",
    "        self.time_ = []\n",
    "        self.if_viol_sen_reverse_ = []\n",
    "        self.viol_pref_ = []\n",
    "        self.viol_balance_ = []\n",
    "\n",
    "\n",
    "def AddStats(ampl, inst, stats):\n",
    "    stats.solve_result_.append(ampl.solve_result)\n",
    "    prefs = ampl.get_objective(\"PrefViolRanked\").get_values().to_pandas()\n",
    "    pref_ranks = ampl.get_data(\"PrefViolRanked.objpriority\").to_pandas()\n",
    "    pref_sorted = dict()  ## For lexicographic comparison of the preference violations\n",
    "    assert prefs.size == pref_ranks.size\n",
    "    for i in range(prefs.size):\n",
    "        key = pref_ranks.iloc[i, 0]\n",
    "        if key not in pref_sorted:\n",
    "            pref_sorted[key] = 0\n",
    "        pref_sorted[key] += prefs.iloc[i, 0]\n",
    "    prefViolAve = prefs.sum().sum() / len(prefs.index)\n",
    "    stats.viol_pref_.append(prefViolAve)\n",
    "    status, msg = CheckSenRev(ampl, inst)\n",
    "    stats.if_viol_sen_reverse_.append(not status)\n",
    "    stats.viol_balance_.append(\n",
    "        float(\n",
    "            ampl.get_value(\n",
    "                \"sum {t in T} abs( sum {i in I: t in V[i]} x[i, t] - sum {i in I, t1 in V[i]} x[i, t1] / card(T) ) / card(T)\"\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "    return msg, prefs, pref_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to display solution characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def PresentSolution(ampl, inst):\n",
    "    if ampl.solve_result != \"solved\":\n",
    "        print(\"        !!!!!! solve_result NOT OPTIMAL.\")\n",
    "    stats = SolveStats()\n",
    "    msg, prefs, pref_sorted = AddStats(ampl, inst, stats)\n",
    "    prefViolAve = stats.viol_pref_[0]\n",
    "    print(\"AVERAGE NORMALIZED PREFERENCE VIOLATION = {:.4}\".format(prefViolAve))\n",
    "    # Seniority as objectives\n",
    "    print(\"REVERSE SENIORITY:\")\n",
    "    print(\"   ---->> AS PBS OBJECTIVES:\")\n",
    "    ampl.eval(\n",
    "        \"\"\"\n",
    "        display {e in 1..2}\n",
    "          sum {i in I, t in T: E[i]==e and t in V[i] and Pr[i, t]==0}\n",
    "            S[i] * x[i, t];\n",
    "        \"\"\"\n",
    "    )\n",
    "    print(\n",
    "        \"   ---->> AS DOMINANCE CONSTRAINTS FROM [1]: {}; {}\".format(\n",
    "            \"Violated\" if stats.if_viol_sen_reverse_[0] else \"OK\", msg\n",
    "        )\n",
    "    )\n",
    "    print(\"AVERAGE SESSION LOAD IMBALANCE = {:.3}\\n\".format(stats.viol_balance_[0]))\n",
    "    for obj in ampl.get_objectives():\n",
    "        print(\"{:20} = {}\".format(obj[0], obj[1].get_values().to_pandas().T))\n",
    "    # Preferences\n",
    "    plot = prefs.plot(\n",
    "        title=\"Ranked normalized preference violations\", xlabel=\"Seniority\"\n",
    "    )\n",
    "    plot.hlines(\n",
    "        prefViolAve, 0, len(prefs.index), label=\"Average\", linestyle=\"--\", color=\"pink\"\n",
    "    )\n",
    "    plt.legend((\"PrefViolRanked\", \"Average\"))\n",
    "    plt.show()\n",
    "    # Solution as a heat map\n",
    "    x = ampl.get_variable(\"x\").get_values().to_pandas().unstack().T\n",
    "    plt.imshow(x)\n",
    "    plt.title(\"Schedule\")\n",
    "    plt.xlabel(\"Trainees\")\n",
    "    plt.ylabel(\"Classes\")\n",
    "    plt.show()\n",
    "    # Free capacity\n",
    "    cap = ampl.get_data(\"Capacity.slack\").to_pandas().unstack().T\n",
    "    cap.columns = inst.cap_kind\n",
    "    cap.index = np.arange(1, inst.nT + 1)\n",
    "    plot = cap.plot(\n",
    "        title=\"Free capacity by position\", xlabel=\"Class slot\", kind=\"bar\", stacked=True\n",
    "    )\n",
    "    plt.show()\n",
    "    # Free group capacity\n",
    "    cap = ampl.get_data(\"Capacity_Meta.slack\").to_pandas().unstack().T\n",
    "    cap.columns = inst.cap_plus_kind\n",
    "    cap.index = np.arange(1, inst.nT + 1)\n",
    "    plot = cap.plot(\n",
    "        title=\"Free group capacity\", xlabel=\"Class slot\", kind=\"bar\", stacked=True\n",
    "    )\n",
    "    plt.show()\n",
    "    # Session language\n",
    "    print(\"Session language\")\n",
    "    print(ampl.get_variable(\"u\").get_values().to_pandas().T)\n",
    "    # Minimal value of the logical constraints (1 when all true)\n",
    "    if (\n",
    "        0 == ampl.get_data(\"Language_1\").to_pandas().min().min()\n",
    "        or 0 == ampl.get_data(\"Language_2\").to_pandas().min().min()\n",
    "    ):\n",
    "        print(\"WARNING: language constraints seem violated (can be rounding errors)\")\n",
    "    return pref_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples\n",
    "\n",
    "We start with the new model in several modifications, then compare it to the original model from [1]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ranked preferences\n",
    "\n",
    "To solve with properly ranked preferences as in [1], we submit the model as-is. The solver uses the `.objpriority` suffixes for ranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "MODEL_FILES_NEW = [\"airtrainee_base.mod\", \"airtrainee_seniority_reverse_PBS.mod\"]\n",
    "\n",
    "ampl = MakeAMPLInstance(MODEL_FILES_NEW, inst)\n",
    "\n",
    "ampl.export_data(\"airtrainee.dat\")  # Can generate a .dat file\n",
    "ampl.eval(\n",
    "    \"option \" + SOLVER + \"_auxfiles rc;\"\n",
    ")  # Pass names for exporting the model by writeprob\n",
    "ampl.solve(\n",
    "    solver=SOLVER,\n",
    "    mp_options=\"outlev=0 multiobj=1 tech:timing=1\",  # mp_options for MP-based solvers\n",
    "    verbose=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SOLVER: highs, solve_result = solved, time: 1.67s\n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"SOLVER: {}, solve_result = {}, time: {:.3}s\\n------------------------------------------------------------\".format(\n",
    "        SOLVER, ampl.solve_result, float(ampl.get_value(\"_total_solve_time\"))\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   SOLUTION x[i, t]\n",
      "\n",
      "|    |   0 |   1 |   2 |   3 |   4 |   5 |   6 |   7 |\n",
      "|---:|----:|----:|----:|----:|----:|----:|----:|----:|\n",
      "|  0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "|  1 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "|  2 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "|  3 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "|  4 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "|  5 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "|  6 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "|  7 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "|  8 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "|  9 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "| 10 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 11 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 12 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 13 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 14 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 15 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 16 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "| 17 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 18 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 19 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 20 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 21 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "| 22 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 23 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 24 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 25 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 26 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 27 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 28 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "| 29 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 30 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 31 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 32 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 33 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 34 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 35 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 36 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 37 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 38 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 39 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 40 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "| 41 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 42 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "| 43 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "| 44 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 45 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 46 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 47 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 48 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 49 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 50 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 51 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "| 52 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 53 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "| 54 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 55 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 56 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 57 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 58 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 59 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 60 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 61 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 62 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 63 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 64 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 65 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 66 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 67 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 68 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 69 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 70 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "| 71 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 72 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 73 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 74 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 75 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 76 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "| 77 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 78 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "| 79 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 80 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 81 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "| 82 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 83 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |\n",
      "| 84 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 85 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 86 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 87 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 88 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 89 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 90 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |\n",
      "| 91 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 92 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n",
      "| 93 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 94 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 95 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 96 |   0 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |\n",
      "| 97 |   0 |   0 |   0 |   0 |   0 |   1 |   0 |   0 |\n",
      "| 98 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
      "| 99 |   0 |   0 |   0 |   1 |   0 |   0 |   0 |   0 |\n"
     ]
    }
   ],
   "source": [
    "x = ampl.get_variable(\"x\").get_values().to_pandas().unstack()\n",
    "x = x.set_axis([i for i in range(len(inst.P))], axis=0)  ## Set index 0..NI-1\n",
    "x = x.set_axis([t for t in range(inst.nT)], axis=1)\n",
    "print(\"   SOLUTION x[i, t]\\n\")\n",
    "print(x.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AVERAGE NORMALIZED PREFERENCE VIOLATION = 0.5183\n",
      "REVERSE SENIORITY:\n",
      "   ---->> AS PBS OBJECTIVES:\n",
      "sum{i in I, t in T: E[i] == e && t in  V[i] && Pr[i,t] == 0} S[i]*x[i,t] [*] :=\n",
      "1  132\n",
      "2   62\n",
      ";\n",
      "\n",
      "   ---->> AS DOMINANCE CONSTRAINTS FROM [1]: OK; \n",
      "     ---- 0 original reverse seniority violations (with language compatibility).\n",
      "AVERAGE SESSION LOAD IMBALANCE = 0.0\n",
      "\n",
      "PrefViolRanked       =                  0    1    2    3    4    5    6    7    8    9   ...   90  \\\n",
      "PrefViolRanked  0.0  0.5  1.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0  ...  0.0   \n",
      "\n",
      "                 91   92   93   94   95   96   97   98   99  \n",
      "PrefViolRanked  1.0  1.0  0.0  0.0  0.0  1.0  1.0  1.0  0.5  \n",
      "\n",
      "[1 rows x 100 columns]\n",
      "ReverseSeniority     = index0            1                              ...  2                       \\\n",
      "index1           2  4  15  17 18 19 21 23 25 27  ... 73 76 79 81 83 85 88 96   \n",
      "ReverseSeniority  0  0  0  31  0  0  0  0  0  0  ...  0  0  0  0  0  0  0  0   \n",
      "\n",
      "index0                  \n",
      "index1           98 99  \n",
      "ReverseSeniority  0  0  \n",
      "\n",
      "[1 rows x 54 columns]\n",
      "Total_Unassigned     =                    0\n",
      "Total_Unassigned  20\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session language\n",
      "       1  2  3  4  5  6  7  8\n",
      "u.val  1  1  0  0  0  1  0  1\n"
     ]
    }
   ],
   "source": [
    "pref_sorted_run1 = PresentSolution(\n",
    "    ampl, inst\n",
    ")  # We'll investigate reverse seniority violations below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Averaged preferences\n",
    "\n",
    "To reduce the average preference violation and improve average \"anti-preference\", we could sacrifice strict seniority ranking\n",
    "and minimize the total violation in one go. For this we assign equal\n",
    "`.objpriority` suffixes for the `PrefViolRanked` and `ReverseSeniority` objectives. In practice, the sum can\n",
    "be weighted to keep seniority aspirations, for example by the `.objweight` suffixes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ampl.eval(\"let {s in SenLevels} PrefViolRanked[s].objpriority := 3;\")\n",
    "ampl.eval(\"let {e in 1..2, i in I: E[i]==e} ReverseSeniority[e, i].objpriority := 3-e;\")\n",
    "\n",
    "ampl.solve(\n",
    "    solver=SOLVER,\n",
    "    mp_options=\"outlev=0 multiobj=1 tech:timing=1\",\n",
    "    verbose=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solve result = solved, time: 0.0555s\n",
      "--------------------------------\n",
      "AVERAGE NORMALIZED PREFERENCE VIOLATION = 0.4767\n",
      "REVERSE SENIORITY:\n",
      "   ---->> AS PBS OBJECTIVES:\n",
      "sum{i in I, t in T: E[i] == e && t in  V[i] && Pr[i,t] == 0} S[i]*x[i,t] [*] :=\n",
      "1  159\n",
      "2    0\n",
      ";\n",
      "\n",
      "   ---->> AS DOMINANCE CONSTRAINTS FROM [1]: OK; \n",
      "     ---- 0 original reverse seniority violations (with language compatibility).\n",
      "AVERAGE SESSION LOAD IMBALANCE = 0.0\n",
      "\n",
      "PrefViolRanked       =                  0    1    2         3    4    5    6    7    8    9   ...  \\\n",
      "PrefViolRanked  0.0  0.5  1.0  0.333333  0.0  0.0  1.0  1.0  1.0  1.0  ...   \n",
      "\n",
      "                 90   91   92   93        94   95   96   97   98   99  \n",
      "PrefViolRanked  0.0  1.0  1.0  0.0  0.333333  0.0  0.0  0.0  1.0  0.0  \n",
      "\n",
      "[1 rows x 100 columns]\n",
      "ReverseSeniority     = index0            1                             ...  2                       \\\n",
      "index1           2  4  15 17 18 19 21 23 25 27  ... 73 76 79 81 83 85 88 96   \n",
      "ReverseSeniority  0  0  0  0  0  0  0  0  0  0  ...  0  0  0  0  0  0  0  0   \n",
      "\n",
      "index0                  \n",
      "index1           98 99  \n",
      "ReverseSeniority  0  0  \n",
      "\n",
      "[1 rows x 54 columns]\n",
      "Total_Unassigned     =                    0\n",
      "Total_Unassigned  20\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session language\n",
      "       1  2  3  4  5  6  7  8\n",
      "u.val  1  1  0  0  0  0  0  1\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Solve result = {}, time: {:.3}s\\n--------------------------------\".format(\n",
    "        ampl.solve_result, ampl.get_value(\"Initial.time\")\n",
    "    )\n",
    ")\n",
    "pref_sorted_run2 = PresentSolution(ampl, inst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session load balancing\n",
    "\n",
    "Similar to the last model extension in [1], we post-process the solution\n",
    "to minimize session load imbalance. We only tackle the imbalance in overall class loads.\n",
    "For that we add yet another objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting airtrainee_load_balancing.mod\n"
     ]
    }
   ],
   "source": [
    "%%writefile airtrainee_load_balancing.mod\n",
    "\n",
    "# Minimize overall session load imbalance\n",
    "minimize LoadImbalance:\n",
    "  sum {t in T} abs( sum {i in I: t in V[i]} x[i, t] - sum {i in I, t1 in V[i]} x[i, t1] / card(T) )\n",
    "  suffix objpriority 0;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "MODEL_FILE_MIN_LOAD_IMB = \"airtrainee_load_balancing.mod\"\n",
    "\n",
    "ampl.read(MODEL_FILE_MIN_LOAD_IMB)\n",
    "ampl.solve(\n",
    "    solver=SOLVER,\n",
    "    mp_options=\"outlev=0 multiobj=1 tech:timing=1\",\n",
    "    gurobi_options=\"acc:abs=0\",  # force linearization of abs() for Gurobi 11.0.2\n",
    "    verbose=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solve result = solved, time: 0.113s\n",
      "--------------------------------\n",
      "AVERAGE NORMALIZED PREFERENCE VIOLATION = 0.4767\n",
      "REVERSE SENIORITY:\n",
      "   ---->> AS PBS OBJECTIVES:\n",
      "sum{i in I, t in T: E[i] == e && t in  V[i] && Pr[i,t] == 0} S[i]*x[i,t] [*] :=\n",
      "1  159\n",
      "2    0\n",
      ";\n",
      "\n",
      "   ---->> AS DOMINANCE CONSTRAINTS FROM [1]: OK; \n",
      "     ---- 0 original reverse seniority violations (with language compatibility).\n",
      "AVERAGE SESSION LOAD IMBALANCE = 0.0\n",
      "\n",
      "LoadImbalance        =                0\n",
      "LoadImbalance  0\n",
      "PrefViolRanked       =                  0    1    2         3    4    5    6    7    8    9   ...  \\\n",
      "PrefViolRanked  0.0  0.5  1.0  0.333333  0.0  0.0  1.0  1.0  1.0  1.0  ...   \n",
      "\n",
      "                 90   91   92   93        94   95   96   97   98   99  \n",
      "PrefViolRanked  0.0  1.0  1.0  0.0  0.333333  0.0  0.0  0.0  1.0  0.0  \n",
      "\n",
      "[1 rows x 100 columns]\n",
      "ReverseSeniority     = index0            1                             ...  2                       \\\n",
      "index1           2  4  15 17 18 19 21 23 25 27  ... 73 76 79 81 83 85 88 96   \n",
      "ReverseSeniority  0  0  0  0  0  0  0  0  0  0  ...  0  0  0  0  0  0  0  0   \n",
      "\n",
      "index0                  \n",
      "index1           98 99  \n",
      "ReverseSeniority  0  0  \n",
      "\n",
      "[1 rows x 54 columns]\n",
      "Total_Unassigned     =                    0\n",
      "Total_Unassigned  20\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session language\n",
      "       1  2  3  4  5  6  7  8\n",
      "u.val  1  1  0  0  0  0  0  1\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Solve result = {}, time: {:.3}s\\n--------------------------------\".format(\n",
    "        ampl.solve_result, ampl.get_value(\"Initial.time\")\n",
    "    )\n",
    ")\n",
    "pref_sorted_run3 = PresentSolution(ampl, inst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Original modeling of reverse seniority\n",
    "\n",
    "The model in [1] explicitly constrains reverse seniority using $O( |I| |T|)$ additional variables and inequalities, as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting airtrainee_seniority_reverse_constraints.mod\n"
     ]
    }
   ],
   "source": [
    "%%writefile airtrainee_seniority_reverse_constraints.mod\n",
    "\n",
    "# Enforce that trainees with expiration E[i]==1 month\n",
    "# and slot priority Pr[i, t]==0 (don't want this slot)\n",
    "# and the same position P[i]\n",
    "# are chosen with the smallest seniority for a given slot,\n",
    "# and preferred to those with expiration E[i]==2.\n",
    "s.t. ConOrig_8 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  y[i]>=0.5 ==>\n",
    "    sum {j in I: E[j]==1 && S[j]<S[i] && P[j]==P[i] && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    + sum {j in I: E[j]==2 && P[j]==P[i] && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    <= 0;\n",
    "\n",
    "# Similar dominance among those with E[i]==2.\n",
    "s.t. ConOrig_9 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  y[i]>=0.5 ==>\n",
    "    sum {j in I: E[j]==2 && S[j]<S[i] && P[j]==P[i] && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    <= 0;\n",
    "\n",
    "# Auxiliary variables for more dominance relations\n",
    "var z {i in I, t in T} binary <= if t in V[i] && E[i]>0 && Pr[i, t]==0 then 1 else 0;\n",
    "var u2 {i in I, t in T} binary <= if t in V[i] && E[i]>0 && Pr[i, t]==0 then 1 else 0;\n",
    "\n",
    "param G {i in I} := max {pp in MAXPlusWhich[P[i]]} pp;      # Group: CK or CB\n",
    "\n",
    "# Dominance to the other position in the same group.\n",
    "# Only necessary when z[i, t]==0 which means that there are free\n",
    "# higher-priority places for this position (see ConOrig_23.)\n",
    "# Constraints (22)-(25) are an extension of (8).\n",
    "s.t. ConOrig_22 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  (z[i, t]<0.5 && y[i]>=0.5) ==>\n",
    "    sum {j in I: E[j]==1 && S[j]<S[i] && P[j]!=P[i]\n",
    "         && G[j]==G[i]      # Same group\n",
    "         && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    + sum {j in I: E[j]==2 && P[j]!=P[i]\n",
    "           && G[j]==G[i] && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    <= 0;\n",
    "\n",
    "# Constrain z for E[i]==1\n",
    "s.t. ConOrig_23 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  z[i, t]>=0.5 ==>\n",
    "    sum {j in I: E[j]==0 && P[j]==P[i] && t in V[j]} x[j, t]\n",
    "    + sum {j in I: E[j]==1 && (S[j]>S[i] || Pr[j, t]==0) && P[j]==P[i] && t in V[j] } x[j, t]\n",
    "    + sum {j in I: E[j]==2 && P[j]==P[i] && t in V[j] && Pr[j, t]>0} x[j, t]\n",
    "    >= MAX[P[i]];\n",
    "\n",
    "# Dominance to the other group.\n",
    "# Only necessary when z[i, t]==0 and u2[i, t] which means that there are free\n",
    "# higher-priority places for this position and group (see ConOrig_25.)\n",
    "s.t. ConOrig_24 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  (u2[i, t]<0.5 && z[i, t]<0.5 && y[i]>=0.5) ==>\n",
    "    sum {j in I: E[j]==1 && S[j]<S[i] && G[j]!=G[i]\n",
    "         && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    + sum {j in I: E[j]==2 && G[j]!=G[i]\n",
    "           && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    <= 0;\n",
    "\n",
    "# Constrain u2 for E[i]==1\n",
    "s.t. ConOrig_25 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  u2[i, t]>=0.5 ==>\n",
    "    sum {j in I: E[j]==0 && G[j]==G[i] && t in V[j]} x[j, t]\n",
    "    + sum {j in I: E[j]==1 && (S[j]>S[i] || Pr[j, t]==0) && G[j]==G[i] && t in V[j] } x[j, t]\n",
    "    + sum {j in I: E[j]==2 && G[j]==G[i] && t in V[j] && Pr[j, t]>0} x[j, t]\n",
    "    >= MAXPlus[G[i]];\n",
    "\n",
    "## The below constraints (26)-(29) are an extension of (9),\n",
    "## similar to (22)-(25).\n",
    "# Dominance to the other position in the same group for E[i]==2.\n",
    "# Only necessary when z[i, t]==0 which means that there are free\n",
    "# higher-priority places for this position (see ConOrig_27.)\n",
    "s.t. ConOrig_26 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  (z[i, t]<0.5 && y[i]>=0.5) ==>\n",
    "    sum {j in I: E[j]==2 && S[j]<S[i] && P[j]!=P[i]\n",
    "         && G[j]==G[i]      # Same group\n",
    "         && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    <= 0;\n",
    "\n",
    "# Constrain z for E[i]==2\n",
    "s.t. ConOrig_27 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  z[i, t]>=0.5 ==>\n",
    "    sum {j in I: E[j]<2 && P[j]==P[i] && t in V[j]} x[j, t]\n",
    "    + sum {j in I: E[j]==2 && (S[j]>S[i] || Pr[j, t]==0) && P[j]==P[i] && t in V[j] } x[j, t]\n",
    "    >= MAX[P[i]];\n",
    "\n",
    "# Dominance to the other group for E[i]==2.\n",
    "# Only necessary when z[i, t]==0 and u2[i, t] which means that there are free\n",
    "# higher-priority places for this position and group (see ConOrig_29.)\n",
    "s.t. ConOrig_28 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  (u2[i, t]<0.5 && z[i, t]<0.5 && y[i]>=0.5) ==>\n",
    "    sum {j in I: E[j]==2 && S[j]<S[i] && G[j]!=G[i]\n",
    "         && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    <= 0;\n",
    "\n",
    "# Constrain u2 for E[i]==2\n",
    "s.t. ConOrig_29 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  u2[i, t]>=0.5 ==>\n",
    "    sum {j in I: E[j]<2 && G[j]==G[i] && t in V[j]} x[j, t]\n",
    "    + sum {j in I: E[j]==2 && (S[j]>S[i] || Pr[j, t]==0) && G[j]==G[i] && t in V[j] } x[j, t]\n",
    "    >= MAXPlus[G[i]];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solve the old model from scratch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "MODEL_FILES_OLD__NO_LANG_COMP = [\n",
    "    \"airtrainee_base.mod\",\n",
    "    \"airtrainee_seniority_reverse_constraints.mod\",\n",
    "    #        \"airtrainee_seniority_reverse_PBS.mod\",\n",
    "]\n",
    "\n",
    "ampl = MakeAMPLInstance(MODEL_FILES_OLD__NO_LANG_COMP, inst)\n",
    "\n",
    "ampl.solve(\n",
    "    solver=SOLVER,\n",
    "    mp_options=\"outlev=0 multiobj=1 tech:timing=1\",\n",
    "    verbose=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solve result = solved, time: 4.07s\n",
      "--------------------------------\n",
      "AVERAGE NORMALIZED PREFERENCE VIOLATION = 0.4967\n",
      "REVERSE SENIORITY:\n",
      "   ---->> AS PBS OBJECTIVES:\n",
      "sum{i in I, t in T: E[i] == e && t in  V[i] && Pr[i,t] == 0} S[i]*x[i,t] [*] :=\n",
      "1  208\n",
      "2  105\n",
      ";\n",
      "\n",
      "   ---->> AS DOMINANCE CONSTRAINTS FROM [1]: OK; \n",
      "     ---- 0 original reverse seniority violations (with language compatibility).\n",
      "AVERAGE SESSION LOAD IMBALANCE = 0.0\n",
      "\n",
      "PrefViolRanked       =                  0    1    2    3    4    5    6    7    8    9   ...   90  \\\n",
      "PrefViolRanked  0.0  0.5  1.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0  ...  0.0   \n",
      "\n",
      "                 91   92   93        94   95   96   97   98   99  \n",
      "PrefViolRanked  1.0  1.0  0.0  0.333333  0.0  1.0  1.0  1.0  0.5  \n",
      "\n",
      "[1 rows x 100 columns]\n",
      "Total_Unassigned     =                    0\n",
      "Total_Unassigned  20\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session language\n",
      "       1  2  3  4  5  6  7  8\n",
      "u.val  1  1  0  0  0  0  0  1\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Solve result = {}, time: {:.3}s\\n--------------------------------\".format(\n",
    "        ampl.solve_result, ampl.get_value(\"Initial.time\")\n",
    "    )\n",
    ")\n",
    "pref_sorted_run4 = PresentSolution(ampl, inst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Language compatibility\n",
    "\n",
    "Let us modify the original reverse seniority constraints to consider language assignments, and not require seniority dominance when it cannot be satisfied due to incompatible languages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting airtrainee_seniority_reverse_constraints_language.mod\n"
     ]
    }
   ],
   "source": [
    "%%writefile airtrainee_seniority_reverse_constraints_language.mod\n",
    "\n",
    "# Enforce that trainees with expiration E[i]==1 month\n",
    "# and slot priority Pr[i, t]==0 (don't want this slot)\n",
    "# and the same position P[i]\n",
    "# are chosen with the smallest seniority for a given slot,\n",
    "# and preferred to those with expiration E[i]==2.\n",
    "# Consider language compatibility.\n",
    "s.t. ConOrig_8 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  y[i]<0.5 || (if L[i]>0 then u[t]==L[i]-1)\n",
    "    || sum {j in I: E[j]==1 && S[j]<S[i] && P[j]==P[i] && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "        + sum {j in I: E[j]==2 && P[j]==P[i] && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "        <= 0;\n",
    "\n",
    "# Similar dominance among those with E[i]==2.\n",
    "s.t. ConOrig_9 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  y[i]<0.5 || (if L[i]>0 then u[t]==L[i]-1)\n",
    "    || sum {j in I: E[j]==2 && S[j]<S[i] && P[j]==P[i] && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    <= 0;\n",
    "\n",
    "# Auxiliary variables for more dominance relations\n",
    "var z {i in I, t in T} binary <= if t in V[i] && E[i]>0 && Pr[i, t]==0 then 1 else 0;\n",
    "var u2 {i in I, t in T} binary <= if t in V[i] && E[i]>0 && Pr[i, t]==0 then 1 else 0;\n",
    "\n",
    "param G {i in I} := max {pp in MAXPlusWhich[P[i]]} pp;      # Group: CK or CB\n",
    "\n",
    "# Dominance to the other position in the same group.\n",
    "# Only necessary when z[i, t]==0 which means that there are free\n",
    "# higher-priority places for this position (see ConOrig_23.)\n",
    "# Constraints (22)-(25) are an extension of (8).\n",
    "s.t. ConOrig_22 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  z[i, t]>=0.5 || y[i]<0.5 || (if L[i]>0 then u[t]==L[i]-1)\n",
    "    || sum {j in I: E[j]==1 && S[j]<S[i] && P[j]!=P[i]\n",
    "         && G[j]==G[i]      # Same group\n",
    "         && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "        + sum {j in I: E[j]==2 && P[j]!=P[i]\n",
    "           && G[j]==G[i] && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "        <= 0;\n",
    "\n",
    "# Constrain z for E[i]==1\n",
    "s.t. ConOrig_23 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  z[i, t]>=0.5 ==>\n",
    "    sum {j in I: E[j]==0 && P[j]==P[i] && t in V[j]} x[j, t]\n",
    "    + sum {j in I: E[j]==1 && (S[j]>S[i] || Pr[j, t]==0) && P[j]==P[i] && t in V[j] } x[j, t]\n",
    "    + sum {j in I: E[j]==2 && P[j]==P[i] && t in V[j] && Pr[j, t]>0} x[j, t]\n",
    "    >= MAX[P[i]];\n",
    "\n",
    "# Dominance to the other group.\n",
    "# Only necessary when z[i, t]==0 and u2[i, t] which means that there are free\n",
    "# higher-priority places for this position and group (see ConOrig_25.)\n",
    "s.t. ConOrig_24 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  u2[i, t]>=0.5 || z[i, t]>=0.5 || y[i]<0.5\n",
    "    || (if L[i]>0 then u[t]==L[i]-1)\n",
    "    || sum {j in I: E[j]==1 && S[j]<S[i] && G[j]!=G[i]\n",
    "         && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "        + sum {j in I: E[j]==2 && G[j]!=G[i]\n",
    "           && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "        <= 0;\n",
    "\n",
    "# Constrain u2 for E[i]==1\n",
    "s.t. ConOrig_25 {i in I, t in V[i]: E[i]==1 && Pr[i, t]==0}:\n",
    "  u2[i, t]>=0.5 ==>\n",
    "    sum {j in I: E[j]==0 && G[j]==G[i] && t in V[j]} x[j, t]\n",
    "    + sum {j in I: E[j]==1 && (S[j]>S[i] || Pr[j, t]==0) && G[j]==G[i] && t in V[j] } x[j, t]\n",
    "    + sum {j in I: E[j]==2 && G[j]==G[i] && t in V[j] && Pr[j, t]>0} x[j, t]\n",
    "    >= MAXPlus[G[i]];\n",
    "\n",
    "## The below constraints (26)-(29) are an extension of (9),\n",
    "## similar to (22)-(25).\n",
    "# Dominance to the other position in the same group for E[i]==2.\n",
    "# Only necessary when z[i, t]==0 which means that there are free\n",
    "# higher-priority places for this position (see ConOrig_27.)\n",
    "s.t. ConOrig_26 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  z[i, t]>=0.5 || y[i]<0.5 || (if L[i]>0 then u[t]==L[i]-1)\n",
    "    || sum {j in I: E[j]==2 && S[j]<S[i] && P[j]!=P[i]\n",
    "         && G[j]==G[i]      # Same group\n",
    "         && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "    <= 0;\n",
    "\n",
    "# Constrain z for E[i]==2\n",
    "s.t. ConOrig_27 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  z[i, t]>=0.5 ==>\n",
    "    sum {j in I: E[j]<2 && P[j]==P[i] && t in V[j]} x[j, t]\n",
    "    + sum {j in I: E[j]==2 && (S[j]>S[i] || Pr[j, t]==0) && P[j]==P[i] && t in V[j] } x[j, t]\n",
    "    >= MAX[P[i]];\n",
    "\n",
    "# Dominance to the other group for E[i]==2.\n",
    "# Only necessary when z[i, t]==0 and u2[i, t] which means that there are free\n",
    "# higher-priority places for this position and group (see ConOrig_29.)\n",
    "s.t. ConOrig_28 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  u2[i, t]>=0.5 || z[i, t]>=0.5 || y[i]<0.5\n",
    "    || (if L[i]>0 then u[t]==L[i]-1)\n",
    "    || sum {j in I: E[j]==2 && S[j]<S[i] && G[j]!=G[i]\n",
    "         && t in V[j] && Pr[j, t]==0} x[j, t]\n",
    "        <= 0;\n",
    "\n",
    "# Constrain u2 for E[i]==2\n",
    "s.t. ConOrig_29 {i in I, t in V[i]: E[i]==2 && Pr[i, t]==0}:\n",
    "  u2[i, t]>=0.5 ==>\n",
    "    sum {j in I: E[j]<2 && G[j]==G[i] && t in V[j]} x[j, t]\n",
    "    + sum {j in I: E[j]==2 && (S[j]>S[i] || Pr[j, t]==0) && G[j]==G[i] && t in V[j] } x[j, t]\n",
    "    >= MAXPlus[G[i]];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "MODEL_FILES_OLD__LANG = [\n",
    "    \"airtrainee_base.mod\",\n",
    "    \"airtrainee_seniority_reverse_constraints_language.mod\",\n",
    "    #        \"airtrainee_seniority_reverse_PBS.mod\",                    ## To also resolve the 'pitfall'\n",
    "]\n",
    "\n",
    "ampl = MakeAMPLInstance(MODEL_FILES_OLD__LANG, inst)\n",
    "\n",
    "ampl.solve(\n",
    "    solver=SOLVER,\n",
    "    mp_options=\"outlev=0 multiobj=1 tech:timing=1\",\n",
    "    verbose=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solve result = solved, time: 3.39s\n",
      "--------------------------------\n",
      "AVERAGE NORMALIZED PREFERENCE VIOLATION = 0.5183\n",
      "REVERSE SENIORITY:\n",
      "   ---->> AS PBS OBJECTIVES:\n",
      "sum{i in I, t in T: E[i] == e && t in  V[i] && Pr[i,t] == 0} S[i]*x[i,t] [*] :=\n",
      "1  132\n",
      "2   57\n",
      ";\n",
      "\n",
      "   ---->> AS DOMINANCE CONSTRAINTS FROM [1]: OK; \n",
      "     ---- 0 original reverse seniority violations (with language compatibility).\n",
      "AVERAGE SESSION LOAD IMBALANCE = 0.0\n",
      "\n",
      "PrefViolRanked       =                  0    1    2    3    4    5    6    7    8    9   ...   90  \\\n",
      "PrefViolRanked  0.0  0.5  1.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0  ...  0.0   \n",
      "\n",
      "                 91   92   93   94   95   96   97   98   99  \n",
      "PrefViolRanked  1.0  1.0  0.0  0.0  0.0  1.0  1.0  1.0  0.5  \n",
      "\n",
      "[1 rows x 100 columns]\n",
      "Total_Unassigned     =                    0\n",
      "Total_Unassigned  20\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session language\n",
      "       1  2  3  4  5  6  7  8\n",
      "u.val  1  1  0  0  0  1  0  1\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Solve result = {}, time: {:.3}s\\n--------------------------------\".format(\n",
    "        ampl.solve_result, ampl.get_value(\"Initial.time\")\n",
    "    )\n",
    ")\n",
    "pref_sorted_run5 = PresentSolution(ampl, inst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "For some examples we can observe that the original reverse seniority constraints without language compatibility are too strict. Such instance is the 3rd generated from `seed=1234`, `N=100`, `T=8`. Let's lexicographically compare the objective value vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranked preference violations for runs 1 and 4:\n",
      "  Priority 298:     0.0 vs 0.0\n",
      "  Priority 297:     0.5 vs 0.5\n",
      "  Priority 296:     1.0 vs 1.0\n",
      "  Priority 295:     0.0 vs 0.0\n",
      "  Priority 294:     0.0 vs 0.0\n",
      "  Priority 293:     0.0 vs 0.0\n",
      "  Priority 292:     1.0 vs 1.0\n",
      "  Priority 291:     1.0 vs 1.0\n",
      "  Priority 290:     1.0 vs 1.0\n",
      "  Priority 289:     1.0 vs 1.0\n",
      "  Priority 288:     0.0 vs 0.0\n",
      "  Priority 287:     1.0 vs 1.0\n",
      "  Priority 286:     0.0 vs 0.0\n",
      "  Priority 285:     1.0 vs 1.0\n",
      "  Priority 284:     1.0 vs 1.0\n",
      "  Priority 283:     1.0 vs 1.0\n",
      "  Priority 282:     1.0 vs 1.0\n",
      "  Priority 281:     1.0 vs 1.0\n",
      "  Priority 280:     0.0 vs 0.0\n",
      "  Priority 279:     1.0 vs 1.0\n",
      "  Priority 278:     1.0 vs 1.0\n",
      "  Priority 277:     0.0 vs 0.0\n",
      "  Priority 276:     0.0 vs 0.0\n",
      "  Priority 275:     1.0 vs 1.0\n",
      "  Priority 274:     1.0 vs 1.0\n",
      "  Priority 273:     0.0 vs 0.0\n",
      "  Priority 272:     0.0 vs 0.0\n",
      "  Priority 271:     0.0 vs 0.0\n",
      "  Priority 270:     0.0 vs 1.0\n",
      "Ranking 1 is lexicographically smaller.\n"
     ]
    }
   ],
   "source": [
    "def LexCmp(dict1, dict2):\n",
    "    list1 = sorted(list(dict1.items()), key=lambda x: x[0], reverse=True)\n",
    "    list2 = sorted(list(dict2.items()), key=lambda x: x[0], reverse=True)\n",
    "    assert len(list1) == len(list2)  ## Assume equal sizes\n",
    "    for i in range(len(list1)):\n",
    "        assert list1[i][0] == list2[i][0]  ## Equal keys\n",
    "        print(\n",
    "            \"  Priority {}:     {} vs {}\".format(list1[i][0], list1[i][1], list2[i][1])\n",
    "        )\n",
    "        if list1[i][1] < list2[i][1] - 1e-5:\n",
    "            print(\"Ranking 1 is lexicographically smaller.\")\n",
    "            return\n",
    "        if list1[i][1] > list2[i][1] + 1e-5:\n",
    "            print(\"Ranking 1 is lexicographically larger.\")\n",
    "            return\n",
    "    print(\"Both rankings are equal.\")\n",
    "\n",
    "\n",
    "print(\"Ranked preference violations for runs 1 and 4:\")\n",
    "LexCmp(pref_sorted_run1, pref_sorted_run4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the ranked vector of preference violations for the new model is lexicographically smaller, this means that it improves satisfaction of the trainee preferences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment\n",
    "\n",
    "We solve pseudo-random instances of different sizes, aiming to validate our approach. We aim to produce results corresponding to Tables 7 and 8 in [1]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Experiment:\n",
    "    def __init__(self, n_first, n_last, n_step, T_factor, N_repeats):\n",
    "        self.n_first = n_first\n",
    "        self.n_last = n_last\n",
    "        self.n_step = n_step\n",
    "        self.T_factor = T_factor\n",
    "        self.N_repeats = N_repeats  # Repeats for each n\n",
    "\n",
    "    def SolveAndPrint(self, inst, files, stats):\n",
    "        ampl = MakeAMPLInstance(files, inst)\n",
    "        # ampl.export_data(\"airtrainee_infeas_200.dat\")\n",
    "        tm = timeit.timeit(\n",
    "            lambda: ampl.solve(\n",
    "                solver=SOLVER,\n",
    "                mp_options=\"outlev=0 multiobj=1\",  # mp_options for MP-based solvers\n",
    "                gurobi_options=\"acc:abs=0\",  # force linearization of abs() for Gurobi 11.0.2\n",
    "                verbose=False,\n",
    "            ),\n",
    "            number=1,\n",
    "        )\n",
    "        stats.time_.append(tm)\n",
    "        AddStats(ampl, inst, stats)\n",
    "        if ampl.solve_result != \"solved\":\n",
    "            print(\"SOLVE RESULT:\", ampl.solve_result, end=\" !!\\t\")\n",
    "        print(\n",
    "            \"{:#.2f},  {},  {:#.3f},  {:#.3f}\".format(\n",
    "                tm,\n",
    "                \"Violated\" if stats.if_viol_sen_reverse_[-1] else \"OK\",\n",
    "                stats.viol_pref_[-1],\n",
    "                stats.viol_balance_[-1],\n",
    "            ),\n",
    "            end=\"\",\n",
    "        )\n",
    "\n",
    "    def Run(self, seed, extra_files=[]):\n",
    "        print(\n",
    "            \"==========================================================================================\"\n",
    "        )\n",
    "        print(\n",
    "            \"========== Experiment for n in range({}, {}, {}); {} repeats; SOLVER: {} ==========\".format(\n",
    "                self.n_first, self.n_last + 1, self.n_step, self.N_repeats, SOLVER\n",
    "            )\n",
    "        )\n",
    "        print(\n",
    "            \"==========================================================================================\"\n",
    "        )\n",
    "        stats = []\n",
    "        mfiles_new = MODEL_FILES_NEW + extra_files\n",
    "        mfiles_old = MODEL_FILES_OLD__LANG + extra_files\n",
    "        for n in range(self.n_first, self.n_last + 1, self.n_step):\n",
    "            rng = np.random.default_rng(seed)\n",
    "            genpar = InstGenParams(\n",
    "                n, math.floor(n * self.T_factor)\n",
    "            )  # With the Demo license, set up to 50 trainees\n",
    "            print(\"\\n=================================================================\")\n",
    "            print(\n",
    "                \"================== Running for n = {}, T = {} ===================\".format(\n",
    "                    n, genpar.T\n",
    "                )\n",
    "            )\n",
    "            print(\n",
    "                \"====== Instance\\t TIME, REV_SEN_CHECK, VIOL_PREF, VIOL_BALANCE ======\"\n",
    "            )\n",
    "            stats.append([SolveStats(), SolveStats()])\n",
    "            for i_repeat in range(self.N_repeats):\n",
    "                inst = GenInstance(genpar, rng)\n",
    "                print(\"       {}. \\t\".format(i_repeat + 1), end=\"\")\n",
    "                print(\"NEW MODEL:\\t\", end=\"\", flush=True)\n",
    "                self.SolveAndPrint(inst, mfiles_new, stats[-1][0])\n",
    "                print(\"\\t\\tOLD MODEL:\\t\", end=\"\", flush=True)\n",
    "                self.SolveAndPrint(inst, mfiles_old, stats[-1][1])\n",
    "                print(\"\")"
   ]
  },
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   "metadata": {},
   "source": [
    "### Default setting\n",
    "\n",
    "The first run of the experiment aims at Table 7 in [1], which benchmarks the default setting."
   ]
  },
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    "seed = 1234\n",
    "\n",
    "exper = Experiment(100, 200, 100, 1 / 12, 3)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "========== Experiment for n in range(100, 201, 100); 3 repeats; SOLVER: highs ==========\n",
      "==========================================================================================\n",
      "\n",
      "=================================================================\n",
      "================== Running for n = 100, T = 8 ===================\n",
      "====== Instance\t TIME, REV_SEN_CHECK, VIOL_PREF, VIOL_BALANCE ======\n",
      "       1. \tNEW MODEL:\t0.58,  OK,  0.465,  0.000\t\tOLD MODEL:\t2.88,  OK,  0.465,  0.000\n",
      "       2. \tNEW MODEL:\tSOLVE RESULT: infeasible !!\t0.05,  OK,  0.940,  0.875\t\tOLD MODEL:\tSOLVE RESULT: infeasible !!\t0.09,  OK,  0.940,  0.875\n",
      "       3. \tNEW MODEL:\t0.87,  OK,  0.518,  0.000\t\tOLD MODEL:\t3.29,  OK,  0.518,  0.000\n",
      "\n",
      "=================================================================\n",
      "================== Running for n = 200, T = 16 ===================\n",
      "====== Instance\t TIME, REV_SEN_CHECK, VIOL_PREF, VIOL_BALANCE ======\n",
      "       1. \tNEW MODEL:\t6.54,  OK,  0.401,  0.000\t\tOLD MODEL:\t57.05,  OK,  0.401,  0.000\n",
      "       2. \tNEW MODEL:\tSOLVE RESULT: infeasible !!\t0.06,  OK,  0.970,  0.875\t\tOLD MODEL:\tSOLVE RESULT: infeasible !!\t0.21,  OK,  0.970,  0.875\n",
      "       3. \tNEW MODEL:\tSOLVE RESULT: infeasible !!\t0.06,  OK,  0.975,  0.812\t\tOLD MODEL:\tSOLVE RESULT: infeasible !!\t0.18,  OK,  0.975,  0.812\n"
     ]
    }
   ],
   "source": [
    "exper.Run(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session load balancing\n",
    "\n",
    "This run additionally minimizes session load imbalance, corresponding to Table 8 in [1]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "========== Experiment for n in range(100, 501, 400); 3 repeats; SOLVER: gurobi ==========\n",
      "==========================================================================================\n",
      "\n",
      "=================================================================\n",
      "================== Running for n = 100, T = 8 ===================\n",
      "====== Instance\t TIME, REV_SEN_CHECK, VIOL_PREF, VIOL_BALANCE ======\n",
      "       1. \tNEW MODEL:\t0.43,  OK,  0.465,  0.000\t\tOLD MODEL:\t0.74,  OK,  0.465,  0.000\n",
      "       2. \tNEW MODEL:\tSOLVE RESULT: infeasible !!\t0.16,  OK,  0.940,  0.875\t\tOLD MODEL:\tSOLVE RESULT: infeasible !!\t0.20,  OK,  0.940,  0.875\n",
      "       3. \tNEW MODEL:\t0.43,  OK,  0.518,  0.000\t\tOLD MODEL:\t0.65,  OK,  0.518,  0.000\n",
      "\n",
      "=================================================================\n",
      "================== Running for n = 500, T = 41 ===================\n",
      "====== Instance\t TIME, REV_SEN_CHECK, VIOL_PREF, VIOL_BALANCE ======\n",
      "       1. \tNEW MODEL:\t103.19,  OK,  0.356,  0.000\t\tOLD MODEL:\t201.40,  OK,  0.356,  0.000\n",
      "       2. \tNEW MODEL:\t89.76,  OK,  0.361,  0.000\t\tOLD MODEL:\t162.09,  OK,  0.361,  0.000\n",
      "       3. \tNEW MODEL:\t93.91,  OK,  0.331,  0.000\t\tOLD MODEL:\t194.22,  OK,  0.331,  0.000\n"
     ]
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   ],
   "source": [
    "exper = Experiment(100, 500, 400, 1 / 12, 3)\n",
    "SOLVER = \"gurobi\"\n",
    "\n",
    "exper.Run(seed, [MODEL_FILE_MIN_LOAD_IMB])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "We studied an aircrew trainee scheduling problem from [1] with seniority-ranked preferences and seniority- and expiration-ranked \"anti-preferences\" for class assignments. We proposed a Priority Bidding System-style modeling for the \"anti-preferences\" part. \n",
    "The new approach is simpler to model (auxiliary lexicographic objective functions) and more efficient to solve.\n",
    "\n",
    "### Acknowledgement\n",
    "\n",
    "We thank George Kozanidis for valuable advice which greatly improved the findings of this project."
   ]
  }
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