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import time
from dataclasses import dataclass
from ortools.sat.python import cp_model
from src.domain import (
AssignmentInstance,
AssignmentRow,
Family,
InventoryInstance,
InventoryOrder,
ScheduleInstance,
ScheduleStart,
Solution,
SolveStatus,
parse_instance,
)
@dataclass
class SolverOutcome:
solution: Solution
seconds: float
solver_name: str
message: str
def _status(model_status: int) -> SolveStatus:
if model_status == cp_model.OPTIMAL:
return SolveStatus.OPTIMAL
if model_status == cp_model.FEASIBLE:
return SolveStatus.FEASIBLE
if model_status == cp_model.INFEASIBLE:
return SolveStatus.INFEASIBLE
if model_status in {cp_model.UNKNOWN, cp_model.MODEL_INVALID}:
return SolveStatus.TIMEOUT
return SolveStatus.ERROR
def _status_name(model_status: int) -> str:
names = {
cp_model.OPTIMAL: "OPTIMAL",
cp_model.FEASIBLE: "FEASIBLE",
cp_model.INFEASIBLE: "INFEASIBLE",
cp_model.UNKNOWN: "UNKNOWN",
cp_model.MODEL_INVALID: "MODEL_INVALID",
}
return names.get(model_status, str(model_status))
def solve_inventory(instance: InventoryInstance, seconds: float, threads: int = 1) -> SolverOutcome:
started = time.perf_counter()
model = cp_model.CpModel()
periods = instance.periods
skus = instance.skus
order: dict[tuple[str, int], cp_model.IntVar] = {}
inv: dict[tuple[str, int], cp_model.IntVar] = {}
short: dict[tuple[str, int], cp_model.IntVar] = {}
sold: dict[tuple[str, int], cp_model.IntVar] = {}
max_demand = max((max(sku.demand) for sku in skus), default=1)
max_pack = max((sku.pack_size for sku in skus), default=1)
max_order = max(max_demand * periods + max_pack * 4, 1)
for sku in skus:
for t in range(1, periods + 1):
packs = model.NewIntVar(0, max_order // sku.pack_size + 1, f"packs_{sku.sku_id}_{t}")
qty = model.NewIntVar(0, max_order, f"ord_{sku.sku_id}_{t}")
model.Add(qty == packs * sku.pack_size)
order[(sku.sku_id, t)] = qty
inv[(sku.sku_id, t)] = model.NewIntVar(0, instance.warehouse_capacity, f"inv_{sku.sku_id}_{t}")
demand = int(sku.demand[t - 1])
short[(sku.sku_id, t)] = model.NewIntVar(0, demand, f"sh_{sku.sku_id}_{t}")
sold[(sku.sku_id, t)] = model.NewIntVar(0, demand, f"sd_{sku.sku_id}_{t}")
prev = sku.initial_stock if t == 1 else inv[(sku.sku_id, t - 1)]
model.Add(inv[(sku.sku_id, t)] + sold[(sku.sku_id, t)] == prev + qty)
model.Add(sold[(sku.sku_id, t)] + short[(sku.sku_id, t)] == demand)
if instance.cover_required:
model.Add(short[(sku.sku_id, t)] == 0)
for t in range(1, periods + 1):
model.Add(sum(inv[(sku.sku_id, t)] for sku in skus) <= instance.warehouse_capacity)
model.Add(
sum(sku.unit_cost * order[(sku.sku_id, t)] for sku in skus for t in range(1, periods + 1)) <= instance.budget
)
objective = []
for sku in skus:
for t in range(1, periods + 1):
objective.append(sku.unit_cost * order[(sku.sku_id, t)])
objective.append(sku.holding_cost * inv[(sku.sku_id, t)])
objective.append(sku.shortage_cost * short[(sku.sku_id, t)])
model.Minimize(sum(objective))
solver = cp_model.CpSolver()
solver.parameters.max_time_in_seconds = float(seconds)
solver.parameters.num_search_workers = int(threads)
raw = solver.Solve(model)
status = _status(raw)
elapsed = time.perf_counter() - started
message = _status_name(raw)
if status in {SolveStatus.OPTIMAL, SolveStatus.FEASIBLE}:
orders = [
InventoryOrder(sku_id=sku.sku_id, period=t, qty=int(solver.Value(order[(sku.sku_id, t)])))
for sku in skus
for t in range(1, periods + 1)
]
obj = float(solver.ObjectiveValue())
bound = float(solver.BestObjectiveBound()) if status == SolveStatus.FEASIBLE else obj
return SolverOutcome(
Solution(
family=Family.INVENTORY,
status=status,
objective=obj,
bound=bound,
orders=orders,
message=message,
),
elapsed,
"ortools-cpsat",
message,
)
return SolverOutcome(
Solution(family=Family.INVENTORY, status=status, message=message),
elapsed,
"ortools-cpsat",
message,
)
def solve_assignment(instance: AssignmentInstance, seconds: float, threads: int = 1) -> SolverOutcome:
started = time.perf_counter()
model = cp_model.CpModel()
x: dict[tuple[str, str], cp_model.IntVar] = {}
costs = instance.cost_map()
for task in instance.tasks:
for agent in instance.agents:
if instance.eligible(task, agent) and (task.task_id, agent.agent_id) in costs:
x[(task.task_id, agent.agent_id)] = model.NewBoolVar(f"x_{task.task_id}_{agent.agent_id}")
unassigned: dict[str, cp_model.IntVar] = {}
for task in instance.tasks:
assigned = [x[(task.task_id, agent.agent_id)] for agent in instance.agents if (task.task_id, agent.agent_id) in x]
miss = model.NewBoolVar(f"u_{task.task_id}")
unassigned[task.task_id] = miss
model.Add(sum(assigned) + miss == 1)
if task.required:
model.Add(miss == 0)
for agent in instance.agents:
load = [
task.size * x[(task.task_id, agent.agent_id)]
for task in instance.tasks
if (task.task_id, agent.agent_id) in x
]
if load:
model.Add(sum(load) <= agent.capacity)
obj_terms = []
for (task_id, agent_id), var in x.items():
obj_terms.append(costs[(task_id, agent_id)] * var)
for task in instance.tasks:
obj_terms.append(task.penalty * unassigned[task.task_id])
model.Minimize(sum(obj_terms) if obj_terms else 0)
solver = cp_model.CpSolver()
solver.parameters.max_time_in_seconds = float(seconds)
solver.parameters.num_search_workers = int(threads)
raw = solver.Solve(model)
status = _status(raw)
elapsed = time.perf_counter() - started
message = _status_name(raw)
if status in {SolveStatus.OPTIMAL, SolveStatus.FEASIBLE}:
rows = [
AssignmentRow(task_id=task_id, agent_id=agent_id)
for (task_id, agent_id), var in x.items()
if solver.Value(var)
]
leftover = [task_id for task_id, var in unassigned.items() if solver.Value(var)]
obj = float(solver.ObjectiveValue())
bound = float(solver.BestObjectiveBound()) if status == SolveStatus.FEASIBLE else obj
return SolverOutcome(
Solution(
family=Family.ASSIGNMENT,
status=status,
objective=obj,
bound=bound,
assignments=rows,
unassigned=leftover,
message=message,
),
elapsed,
"ortools-cpsat",
message,
)
return SolverOutcome(
Solution(family=Family.ASSIGNMENT, status=status, message=message),
elapsed,
"ortools-cpsat",
message,
)
def solve_scheduling(instance: ScheduleInstance, seconds: float, threads: int = 1) -> SolverOutcome:
started = time.perf_counter()
model = cp_model.CpModel()
horizon = max(instance.horizon, sum(job.duration for job in instance.jobs) + max((job.release for job in instance.jobs), default=0) + 2)
intervals_by_machine: dict[str, list] = {machine: [] for machine in instance.machines}
start: dict[str, cp_model.IntVar] = {}
end: dict[str, cp_model.IntVar] = {}
tardy: dict[str, cp_model.IntVar] = {}
assign: dict[tuple[str, str], cp_model.IntVar] = {}
for job in instance.jobs:
start[job.job_id] = model.NewIntVar(job.release, horizon, f"s_{job.job_id}")
end[job.job_id] = model.NewIntVar(job.release + job.duration, horizon + job.duration, f"e_{job.job_id}")
model.Add(end[job.job_id] == start[job.job_id] + job.duration)
tardy[job.job_id] = model.NewIntVar(0, horizon + job.duration, f"tard_{job.job_id}")
model.Add(tardy[job.job_id] >= end[job.job_id] - job.due)
if job.hard_due:
model.Add(end[job.job_id] <= job.due)
eligible = [machine for machine in instance.machines if machine in set(job.machine_eligible)]
if not eligible:
eligible = list(instance.machines)
machine_bools = []
for machine in eligible:
flag = model.NewBoolVar(f"m_{job.job_id}_{machine}")
assign[(job.job_id, machine)] = flag
interval = model.NewOptionalIntervalVar(start[job.job_id], job.duration, end[job.job_id], flag, f"iv_{job.job_id}_{machine}")
intervals_by_machine[machine].append(interval)
machine_bools.append(flag)
model.AddExactlyOne(machine_bools)
for machine, intervals in intervals_by_machine.items():
if intervals:
model.AddNoOverlap(intervals)
job_ids = {job.job_id for job in instance.jobs}
for edge in instance.precedences:
if edge.before in job_ids and edge.after in job_ids:
model.Add(end[edge.before] <= start[edge.after])
makespan = model.NewIntVar(0, horizon + 8, "makespan")
for job in instance.jobs:
model.Add(makespan >= end[job.job_id])
model.Minimize(sum(job.weight * tardy[job.job_id] for job in instance.jobs) * 100 + makespan)
solver = cp_model.CpSolver()
solver.parameters.max_time_in_seconds = float(seconds)
solver.parameters.num_search_workers = int(threads)
raw = solver.Solve(model)
status = _status(raw)
elapsed = time.perf_counter() - started
message = _status_name(raw)
if status in {SolveStatus.OPTIMAL, SolveStatus.FEASIBLE}:
starts = []
for job in instance.jobs:
machine_id = next(machine for machine in instance.machines if (job.job_id, machine) in assign and solver.Value(assign[(job.job_id, machine)]))
starts.append(
ScheduleStart(
job_id=job.job_id,
machine_id=machine_id,
start=int(solver.Value(start[job.job_id])),
end=int(solver.Value(end[job.job_id])),
)
)
weighted = sum(job.weight * max(0, int(solver.Value(end[job.job_id])) - job.due) for job in instance.jobs)
obj = float(weighted)
return SolverOutcome(
Solution(
family=Family.SCHEDULING,
status=status,
objective=obj,
bound=obj if status == SolveStatus.OPTIMAL else None,
starts=starts,
message=message,
),
elapsed,
"ortools-cpsat",
message,
)
return SolverOutcome(
Solution(family=Family.SCHEDULING, status=status, message=message),
elapsed,
"ortools-cpsat",
message,
)
def solve_instance(family: Family | str, payload: dict, seconds: float, threads: int = 1) -> SolverOutcome:
family = Family(family)
instance = parse_instance(family, payload)
if family == Family.INVENTORY:
return solve_inventory(instance, seconds, threads)
if family == Family.ASSIGNMENT:
return solve_assignment(instance, seconds, threads)
return solve_scheduling(instance, seconds, threads)
|