Iteration final - SOLVER_EXECUTION
Sequence: 6
Timestamp: 2025-07-28 00:07:24

Prompt:
Generated solver codes with templates and executed them with retry logic

Response:
{
  "gurobipy": {
    "solver_type": "gurobipy",
    "execution_time": 0.18386507034301758,
    "return_code": 1,
    "stdout": "Restricted license - for non-production use only - expires 2026-11-23\n",
    "stderr": "Traceback (most recent call last):\n  File \"/tmp/tmpy1ytfm8p.py\", line 61, in <module>\n    optimize_faculty_allocation()\n  File \"/tmp/tmpy1ytfm8p.py\", line 28, in optimize_faculty_allocation\n    assert all((i, j) in participation_scores for i in faculty_ids for j in activity_ids), \"Data inconsistency in participation scores\"\nAssertionError: Data inconsistency in participation scores\n",
    "status": "error",
    "optimal_value": null,
    "error_message": "Traceback (most recent call last):\n  File \"/tmp/tmpy1ytfm8p.py\", line 61, in <module>\n    optimize_faculty_allocation()\n  File \"/tmp/tmpy1ytfm8p.py\", line 28, in optimize_faculty_allocation\n    assert all((i, j) in participation_scores for i in faculty_ids for j in activity_ids), \"Data inconsistency in participation scores\"\nAssertionError: Data inconsistency in participation scores\n",
    "decision_variables": {}
  },
  "docplex": {
    "solver_type": "docplex",
    "execution_time": 1.3902015686035156,
    "return_code": 0,
    "stdout": "Optimal value: 70.5\nx[1,101] = 1.000\nx[1,102] = 1.000\nx[2,102] = 1.000\nx[2,103] = 1.000\nx[3,103] = 1.000\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 70.5,
    "error_message": null,
    "decision_variables": {}
  },
  "pyomo": {
    "solver_type": "pyomo",
    "execution_time": 1.1173362731933594,
    "return_code": 0,
    "stdout": "Read LP format model from file /tmp/tmpmd8q_mp6.pyomo.lp\nReading time = 0.00 seconds\nx1: 6 rows, 9 columns, 18 nonzeros\nSet parameter TimeLimit to value 300\nSet parameter MIPGap to value 0.01\nGurobi Optimizer version 12.0.2 build v12.0.2rc0 (linux64 - \"Red Hat Enterprise Linux 9.4 (Plow)\")\n\nCPU model: AMD EPYC 7513 32-Core Processor, instruction set [SSE2|AVX|AVX2]\nThread count: 64 physical cores, 128 logical processors, using up to 32 threads\n\nNon-default parameters:\nTimeLimit  300\nMIPGap  0.01\n\nOptimize a model with 6 rows, 9 columns and 18 nonzeros\nModel fingerprint: 0x881159b8\nVariable types: 0 continuous, 9 integer (9 binary)\nCoefficient statistics:\n  Matrix range     [1e+00, 1e+00]\n  Objective range  [1e+01, 2e+01]\n  Bounds range     [1e+00, 1e+00]\n  RHS range        [1e+00, 2e+00]\nFound heuristic solution: objective 70.5000000\n\nExplored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)\nThread count was 1 (of 128 available processors)\n\nSolution count 1: 70.5 \n\nOptimal solution found (tolerance 1.00e-02)\nBest objective 7.050000000000e+01, best bound 7.050000000000e+01, gap 0.0000%\nOptimal solution found!\nOptimal value: 70.5\n\nAssignment of faculty to activities:\nFaculty 1 assigned to Activity 101\nFaculty 1 assigned to Activity 102\nFaculty 2 assigned to Activity 102\nFaculty 2 assigned to Activity 103\nFaculty 3 assigned to Activity 103\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 70.5,
    "error_message": null,
    "decision_variables": {}
  }
}
