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import time
import numpy as np
import gurobipy as grb
class MultistageOptimization():
"""
Standard version multistage problem.
"""
def __init__(self, instance, scenario_tree):
self.name = "multistage_optimization"
self.instance = instance
self.scenario_tree = scenario_tree
# Sets
self.items = range(instance.n_items)
self.nodes = range(self.scenario_tree.n_nodes)
self.machines = range(instance.n_machines)
# Model
self.model = grb.Model(self.name)
# State 1 if machine m is able to produce item i in node n
X = self.model.addVars(
instance.n_items, instance.n_machines, self.scenario_tree.n_nodes,
vtype=grb.GRB.BINARY,
name='X'
)
# Setup 1 if a pay the setup related to item i in machine m
D = self.model.addVars(
instance.n_items, instance.n_machines, self.scenario_tree.n_nodes,
vtype=grb.GRB.BINARY,
name='D'
)
# Inventory
I = self.model.addVars(
instance.n_items, self.scenario_tree.n_nodes,
vtype=grb.GRB.CONTINUOUS,
lb=0.0,
name='I'
)
# Lost Sales
Z = self.model.addVars(
instance.n_items, self.scenario_tree.n_nodes,
vtype=grb.GRB.CONTINUOUS,
lb=0.0,
name='Z'
)
obj_func = grb.quicksum(
self.scenario_tree.nodes[n]['prob'] * (
instance.lost_sales_costs[i] * Z[i, n] + instance.holding_costs[i] * I[i, n])
for i in self.items
for n in self.nodes
)
obj_func += grb.quicksum(
self.scenario_tree.nodes[n]['prob'] *
(instance.setup_costs[m][i] * D[i, m, n])
for i in self.items
for m in self.machines
for n in self.nodes
)
self.model.setObjective(obj_func, grb.GRB.MINIMIZE)
self.model.addConstrs(
(I[i, n] <= instance.max_inventory_level[i]
for i in self.items for n in self.nodes),
name='max_inventory'
)
# INITIAL STATE
for m in self.machines:
for i in self.items:
# if m in state i, then:
if i == instance.machine_initial_setup[m] - 1:
self.model.addConstr(
(D[i, m, 0] == X[i, m, 0] - 1),
name=f'initial_state_machine_{m}_{i}'
)
self.model.addConstr(
1 - X[i, m, 0] + 0.01 <= (1 + 0.01)*(1 - D[i, m, 0]),
name=f'initial_state_machine_{m}_{i}_part2'
)
else:
# if m not in state i, then, there is a change
self.model.addConstr(
# instance.machine_initial_setup[m] - 1
(D[i, m, 0] == X[i, m, 0]),
name=f'initial_state_machine_{m}_{i}'
)
self.model.addConstr(
0 - X[i, m, 0] + 0.01 <= (1 + 0.01)*(1 - D[i, m, 0]),
name=f'initial_state_machine_{m}_{i}_part2'
)
# EVOLUTION
self.model.addConstrs(
(I[i, 0] == instance.initial_inventory[i] for i in self.items),
name=f'initial_inventory'
)
for n in range(1, self.scenario_tree.n_nodes):
parent = list(self.scenario_tree.predecessors(n))[0]
self.model.addConstrs(
(I[i, n] - Z[i, n] == I[i, parent] + grb.quicksum(instance.machine_production_matrix[m][i] * X[i, m, parent] -
instance.setup_loss[m][i] * D[i, m, parent] for m in self.machines) - scenario_tree.nodes[n]['obs'][i] for i in self.items),
name=f'item_flow_{n}'
)
# Machine no multiple state
self.model.addConstrs(
(grb.quicksum(X[i, m, n] for i in self.items) <=
1 for m in self.machines for n in self.nodes),
name=f"no_more_setting_machine_node"
)
# avoid change to items with no production
for i in self.items:
for m in self.machines:
if instance.machine_production_matrix[m][i] == 0:
self.model.addConstrs(
(D[i, m, n] == 0 for n in self.nodes),
name=f'no_change_in_forbidden_state_{n}'
)
# LINK X D
for n in range(1, self.scenario_tree.n_nodes):
parent = list(self.scenario_tree.predecessors(n))[0]
self.model.addConstrs(
D[i, m, n] >= X[i, m, n] - X[i, m, parent]
for i in self.items for m in self.machines
)
self.model.addConstrs(
X[i, m, parent] - X[i, m, n] +
0.01 <= (1 + 0.01)*(1 - D[i, m, n])
for i in self.items for m in self.machines
)
self.model.update()
self.X = X
self.D = D
self.I = I
self.Z = Z
self.obj_func = obj_func
def update_data(self, obs):
# 1. UPDATE INVENTORY
for i in self.items:
self.model.setAttr(
"RHS",
self.model.getConstrByName(f"initial_inventory[{i}]"),
# self.instance.initial_inventory[i]
obs['inventory_level'][i]
)
# 2. UPDATE STATE
for m in self.machines:
for i in self.items:
self.model.remove(
self.model.getConstrByName(
f'initial_state_machine_{m}_{i}')
)
self.model.remove(
self.model.getConstrByName(
f'initial_state_machine_{m}_{i}_part2')
)
# if m in state i, then:
if i == self.instance.machine_initial_setup[m] - 1:
self.model.addConstr(
(self.D[i, m, 0] >= self.X[i, m, 0] - 1),
name=f'initial_state_machine_{m}_{i}'
)
self.model.addConstr(
1 - self.X[i, m, 0] +
0.01 <= (1 + 0.01)*(1 - self.D[i, m, 0]),
name=f'initial_state_machine_{m}_{i}_part2'
)
else:
# if m not in state i, then, there is a change
self.model.addConstr(
# instance.machine_initial_setup[m] - 1
(self.D[i, m, 0] >= self.X[i, m, 0]),
name=f'initial_state_machine_{m}_{i}'
)
self.model.addConstr(
0 - self.X[i, m, 0] +
0.01 <= (1 + 0.01)*(1 - self.D[i, m, 0]),
name=f'initial_state_machine_{m}_{i}_part2'
)
# 3. UPDATE STATE DEMAND
# for i in self.items:
# for n in range(1, self.scenario_tree.n_nodes):
# self.model.setAttr(
# "RHS",
# self.model.getConstrByName(f"item_flow_{n}[{i}]"),
# - new_scenario_tree.nodes[n]['obs'][i]
# )
self.default_state = obs['machine_setup']
self.model.update()
def solve(
self, time_limit=None,
gap=None, verbose=False, debug_model=False
):
if gap:
self.model.setParam('MIPgap', gap)
if time_limit:
self.model.setParam(grb.GRB.Param.TimeLimit, time_limit)
if verbose:
self.model.setParam('OutputFlag', 1)
else:
self.model.setParam('OutputFlag', 0)
self.model.setParam('LogFile', './logs/gurobi.log')
if debug_model:
self.model.write(
f"./logs/{self.name}_{self.instance.current_step}.lp")
# from datetime import datetime
# self.model.write(f"./logs/RP_{ datetime.now() }.lp")
# self.model.write(f"./logs/RP.lp")
self.model.setParam('MIPgap', 0.10)
# self.model.setParam('OutputFlag', 1)
start = time.time()
self.model.optimize()
end = time.time()
comp_time = end - start
if self.model.status == grb.GRB.Status.OPTIMAL:
sol = [0] * self.instance.n_machines
for m in self.machines:
sol[m] = int(
sum([(i+1) * self.X[i, m, 0].X for i in self.items]))
# for i in self.items:
# if round(self.D[i,m,0].X, 0) > 0.5:
# sol[m] = i + 1 # 0 means idle
# if sol[m] == 0:
# # NB: the variable D has an interpretation in terms of production changes
# # D[i,m,0] = 0 means same setting or idle:
# if self.default_state[m] == 0:
# sol[m] = self.default_state[m]
# else:
# if sum([self.X[self.default_state[m] - 1,m,n].X for n in self.scenario_tree.successors(0)]) == 0:
# sol[m] = 0
# else:
# sol[m] = self.default_state[m]
return self.model.getObjective().getValue(), np.array(sol), comp_time
else:
print("MODEL INFEASIBLE OR UNBOUNDED")
return -1, [], comp_time
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