Iteration final - SOLVER_EXECUTION
Sequence: 8
Timestamp: 2025-07-27 22:02:29

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

Response:
{
  "gurobipy": {
    "solver_type": "gurobipy",
    "execution_time": 0.5062024593353271,
    "return_code": 0,
    "stdout": "Restricted license - for non-production use only - expires 2026-11-23\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\nOptimize a model with 12 rows, 9 columns and 18 nonzeros\nModel fingerprint: 0xa9ea3afa\nVariable types: 0 continuous, 9 integer (0 binary)\nCoefficient statistics:\n  Matrix range     [1e+00, 2e+02]\n  Objective range  [1e+01, 2e+01]\n  Bounds range     [0e+00, 0e+00]\n  RHS range        [5e+00, 2e+02]\nFound heuristic solution: objective 88.0000000\nPresolve removed 12 rows and 9 columns\nPresolve time: 0.00s\nPresolve: All rows and columns removed\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: 88 \n\nOptimal solution found (tolerance 1.00e-04)\nBest objective 8.800000000000e+01, best bound 8.800000000000e+01, gap 0.0000%\nOptimal value: 88.0\nx[1, 101] = 1.000\nx[2, 101] = 1.000\nx[2, 102] = 1.000\nx[3, 101] = 2.000\nx[3, 102] = 1.000\nx[3, 103] = 1.000\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 88.0,
    "error_message": null,
    "decision_variables": {}
  },
  "docplex": {
    "solver_type": "docplex",
    "execution_time": 7.739530563354492,
    "return_code": 0,
    "stdout": "Optimal value: 12.0\nx[101] = 1.000\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 12.0,
    "error_message": null,
    "decision_variables": {}
  },
  "pyomo": {
    "solver_type": "pyomo",
    "execution_time": 4.287486553192139,
    "return_code": 0,
    "stdout": "Read LP format model from file /tmp/tmpr31cjv9m.pyomo.lp\nReading time = 0.00 seconds\nx1: 12 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 12 rows, 9 columns and 18 nonzeros\nModel fingerprint: 0xa9ea3afa\nVariable types: 0 continuous, 9 integer (0 binary)\nCoefficient statistics:\n  Matrix range     [1e+00, 2e+02]\n  Objective range  [1e+01, 2e+01]\n  Bounds range     [0e+00, 0e+00]\n  RHS range        [5e+00, 2e+02]\nFound heuristic solution: objective 88.0000000\nPresolve removed 12 rows and 9 columns\nPresolve time: 0.00s\nPresolve: All rows and columns removed\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: 88 \n\nOptimal solution found (tolerance 1.00e-02)\nBest objective 8.800000000000e+01, best bound 8.800000000000e+01, gap 0.0000%\nOptimal solution found!\nOptimal value: 88.0\n\nScreening schedule:\nCinema 1, Film 101: 1.0 screenings\nCinema 2, Film 101: 1.0 screenings\nCinema 2, Film 102: 1.0 screenings\nCinema 3, Film 101: 2.0 screenings\nCinema 3, Film 102: 1.0 screenings\nCinema 3, Film 103: 1.0 screenings\n",
    "stderr": "",
    "status": "optimal",
    "optimal_value": 88.0,
    "error_message": null,
    "decision_variables": {}
  }
}
