"""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",
]