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ab849c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """Configuration constants for the Optimization Operating System."""
from __future__ import annotations
ENGINE_VERSION = "1.0.0"
PRODUCT_NAME = "Optimization Operating System"
PROBLEM_TYPES = {
"scheduling": {
"label": "Job Shop Scheduling",
"category": "scheduling",
"description": "Assign operations to machines minimizing makespan under precedence constraints.",
"default_size": "medium",
"objective": "minimize",
},
"routing": {
"label": "Vehicle Routing (VRP)",
"category": "routing",
"description": "Route a fleet from depot to customers with capacity and distance minimization.",
"default_size": "medium",
"objective": "minimize",
},
"assignment": {
"label": "Assignment Problem",
"category": "assignment",
"description": "Assign agents to tasks minimizing total assignment cost.",
"default_size": "medium",
"objective": "minimize",
},
"inventory": {
"label": "Inventory Replenishment",
"category": "inventory",
"description": "Determine order quantities minimizing holding + stockout cost over a horizon.",
"default_size": "medium",
"objective": "minimize",
},
"facility_location": {
"label": "Facility Location",
"category": "network",
"description": "Open facilities and assign customers minimizing fixed + transport cost.",
"default_size": "medium",
"objective": "minimize",
},
"packing": {
"label": "Bin Packing",
"category": "packing",
"description": "Pack items into minimum bins without exceeding capacity.",
"default_size": "medium",
"objective": "minimize",
},
}
SIZE_PRESETS = {
"small": {"scale": 0.55, "time_limit_sec": 5, "label": "Small"},
"medium": {"scale": 1.0, "time_limit_sec": 12, "label": "Medium"},
"large": {"scale": 1.6, "time_limit_sec": 25, "label": "Large"},
"dynamic": {"scale": 1.0, "time_limit_sec": 15, "label": "Dynamic"},
"stochastic": {"scale": 1.0, "time_limit_sec": 15, "label": "Stochastic"},
}
METHOD_CATEGORIES = {
"baseline": {
"label": "Baseline Heuristic",
"description": "Simple rule-based methods (FCFS, greedy, nearest, EDD).",
},
"exact": {
"label": "Exact Optimization",
"description": "MIP, CP-SAT, or convex formulations with optimality guarantees.",
},
"scalable": {
"label": "Scalable Metaheuristic",
"description": "Local search, ALNS, or genetic algorithms for larger instances.",
},
"robust": {
"label": "Robust / Dynamic",
"description": "Rolling horizon, scenario optimization, or simulation-based planning.",
},
}
METHODS = {
"scheduling": {
"baseline": {"id": "spt_baseline", "label": "Shortest Processing Time (SPT)"},
"exact": {"id": "cp_sat_scheduling", "label": "CP-SAT Job Shop"},
"scalable": {"id": "ga_scheduling", "label": "Genetic Algorithm"},
"robust": {"id": "rolling_horizon_scheduling", "label": "Rolling Horizon"},
},
"routing": {
"baseline": {"id": "nearest_depot", "label": "Nearest Warehouse Greedy"},
"exact": {"id": "cp_sat_routing", "label": "CP-SAT Routing"},
"scalable": {"id": "alns_routing", "label": "ALNS Routing"},
"robust": {"id": "scenario_routing", "label": "Scenario Robust Routing"},
},
"assignment": {
"baseline": {"id": "greedy_assignment", "label": "Greedy Assignment"},
"exact": {"id": "highs_assignment", "label": "HiGHS MIP Assignment"},
"scalable": {"id": "local_search_assignment", "label": "Local Search"},
"robust": {"id": "stochastic_assignment", "label": "Stochastic Assignment"},
},
"inventory": {
"baseline": {"id": "reorder_point", "label": "Reorder Point Heuristic"},
"exact": {"id": "cp_sat_inventory", "label": "CP-SAT Inventory MIP"},
"scalable": {"id": "decomposition_inventory", "label": "Rolling Decomposition"},
"robust": {"id": "simulation_inventory", "label": "Simulation-Based Optimization"},
},
"facility_location": {
"baseline": {"id": "nearest_facility", "label": "Nearest Facility Greedy"},
"exact": {"id": "cbc_facility", "label": "CBC Facility MIP"},
"scalable": {"id": "ga_facility", "label": "GA Facility Selection"},
"robust": {"id": "scenario_facility", "label": "Scenario Robust Location"},
},
"packing": {
"baseline": {"id": "first_fit_decreasing", "label": "First Fit Decreasing"},
"exact": {"id": "cp_sat_packing", "label": "CP-SAT Bin Packing"},
"scalable": {"id": "alns_packing", "label": "ALNS Packing"},
"robust": {"id": "dynamic_packing", "label": "Dynamic Item Arrival"},
},
}
SOLVERS = {
"highs": {
"label": "HiGHS",
"engine": "highspy",
"available": True,
"license": "MIT",
"strengths": ["LP/MIP", "fast LP root", "open source"],
},
"cbc": {
"label": "CBC (PuLP)",
"engine": "pulp",
"available": True,
"license": "EPL",
"strengths": ["MIP", "general purpose"],
},
"cp_sat": {
"label": "OR-Tools CP-SAT",
"engine": "ortools",
"available": True,
"license": "Apache-2.0",
"strengths": ["CP", "scheduling", "routing"],
},
"scip": {
"label": "SCIP",
"engine": "scip",
"available": False,
"license": "Academic/Commercial",
"strengths": ["MIP", "branch-and-cut"],
},
"gurobi": {
"label": "Gurobi",
"engine": "gurobi",
"available": False,
"license": "Commercial",
"strengths": ["MIP", "industrial speed"],
},
"heuristic": {
"label": "Heuristic Engine",
"engine": "native",
"available": True,
"license": "MIT",
"strengths": ["fast", "scalable", "anytime"],
},
}
SOLVER_CONFIGS = {
"highs": {"presolve": "on", "threads": 2, "mip_rel_gap": 0.02},
"cbc": {"presolve": "on", "cuts": "on", "heuristics": "on"},
"cp_sat": {"num_search_workers": 4, "log_search_progress": False},
"scip": {"presolving": True, "separating": True},
"gurobi": {"Presolve": 2, "MIPFocus": 1},
"heuristic": {"max_iterations": 500, "seed": 42},
}
SCENARIO_TYPES = {
"capacity_change": {"label": "Capacity Change", "factor_range": (0.6, 1.4)},
"demand_shift": {"label": "Demand Shift", "factor_range": (0.7, 1.5)},
"resource_removal": {"label": "Resource Removal", "pct_range": (0.05, 0.25)},
"cost_increase": {"label": "Cost Increase", "factor_range": (1.1, 2.0)},
"network_disruption": {"label": "Network Disruption", "pct_range": (0.1, 0.3)},
}
METRICS = [
"objective_value",
"best_bound",
"optimality_gap",
"elapsed_time_sec",
"iterations",
"constraint_violations",
"feasible",
"status",
]
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