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a241478 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | # -*- coding: utf-8 -*-
import time
import numpy as np
import gurobipy as grb
from envs import *
class PerfectInfoOptimization():
def __init__(self, instance: SimplePlant):
self.name = "perfectInfoOptimization"
self.instance = instance
# Sets
self.items = range(instance.n_items)
self.machines = range(instance.n_machines)
self.time_steps = range(instance.T)
# Model
self.model = grb.Model(self.name)
# 1 if machine m is able to produce item i at time t
X = self.model.addVars(
instance.n_items, instance.n_machines, instance.T,
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, instance.T,
vtype=grb.GRB.BINARY,
name='D'
)
# Inventory
I = self.model.addVars(
instance.n_items, instance.T + 1,
vtype=grb.GRB.CONTINUOUS,
lb=0.0,
name='I'
)
# Lost Sales
Z = self.model.addVars(
instance.n_items, instance.T + 1,
vtype=grb.GRB.CONTINUOUS,
lb=0.0,
name='Z'
)
obj_func = grb.quicksum(
(instance.lost_sales_costs[i] * Z[i, t] + instance.holding_costs[i] * I[i, t])
for i in self.items
for t in range(1, instance.T + 1)
)
obj_func += grb.quicksum(
instance.setup_costs[m][i] * D[i, m, t]
for i in self.items
for m in self.machines
for t in self.time_steps
)
self.model.setObjective(obj_func, grb.GRB.MINIMIZE)
self.model.addConstrs(
(I[i, t] <= instance.max_inventory_level[i] for i in self.items for t in self.time_steps),
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}'
)
self.model.addConstr(
1 - X[i, m, 0] + 0.01 <= (1 + 0.01)*(1 - D[i, m, 0])
)
else:
# if m not in state i, then, there is a change
self.model.addConstr(
(D[i, m, 0] >= X[i, m, 0]), # instance.machine_initial_setup[m] - 1
name=f'initial_state_machine_{m}'
)
self.model.addConstr(
0 - X[i, m, 0] + 0.01 <= (1 + 0.01)*(1 - D[i, m, 0])
)
# EVOLUTION
self.model.addConstrs(
( I[i, 0] == instance.initial_inventory[i] for i in self.items),
name=f'initial_condition'
)
for t in range(1, self.instance.T + 1):
# print(self.instance.scenario_demand[:, t])
self.model.addConstrs(
(I[i, t] - Z[i, t] == I[i, t-1] + grb.quicksum( instance.machine_production_matrix[m][i] * X[i, m, t-1] - instance.setup_loss[m][i] * D[i, m, t-1] for m in self.machines ) - self.instance.scenario_demand[i, t-1] for i in self.items),
name=f'item_flow_{t}'
)
# Machine no multiple state
self.model.addConstrs(
(grb.quicksum(X[i, m, t] for i in self.items) <= 1 for m in self.machines for t in self.time_steps ),
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, t] == 0 for t in self.time_steps),
name=f'no_change_in_forbidden_state'
)
# LINK X D
for t in range(1, self.instance.T):
self.model.addConstrs(
D[i, m, t] >= X[i, m, t] - X[i, m, t-1]
for i in self.items for m in self.machines
)
self.model.addConstrs(
X[i, m, t-1] - X[i, m, t] + 0.01 <= (1 + 0.01)*(1 - D[i, m, t])
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 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('MIPgap', 0.05)
# self.model.setParam('OutputFlag', 1)
self.model.setParam('LogFile', './logs/gurobi.log')
if debug_model:
self.model.write(f"./logs/{self.name}.lp")
# self.model.write(f"./logs/perfectInfo.lp")
start = time.time()
self.model.optimize()
end = time.time()
comp_time = end - start
if self.model.status == grb.GRB.Status.OPTIMAL:
sol = np.zeros((self.instance.n_machines, self.instance.T))
for t in self.time_steps:
for m in self.machines:
sol[m,t] = sum([ (i+1) * self.X[i, m, t].X for i in self.items])
# for t in range(0, self.instance.T+1):
# # print(f"time: {t}")
# str_to_print = f"{t}] "
# for i in self.items:
# str_to_print += f"inv_{i}: {abs(self.I[i, t].X):.0f} \t"
# str_to_print += f"[ls: {grb.quicksum(self.instance.lost_sales_costs[i] * self.Z[i, t].X for i in self.items).getValue():.2f}]"
# str_to_print += f"[hc: {grb.quicksum(self.instance.holding_costs[i] * self.I[i, t].X for i in self.items).getValue():.2f}]"
# print("\t ", str_to_print)
# for t in range(self.instance.T):
# a = grb.quicksum(
# self.instance.setup_costs[m][i] * self.D[i, m, t]
# for i in self.items
# for m in self.machines
# ).getValue()
# print(f"{t}] setup: {a}")
# for t in range(self.instance.T):
# for m in self.machines:
# for i in self.items:
# # if self.X[i, m, t].X > 0.5:
# # print(f"X[{i}, {m}, {t}]")
# if self.D[i, m, t].X > 0.5:
# if t >= 1:
# print(f"D[{i}, {m}, {t}] {self.D[i, m, t].X} >= {self.X[i, m, t].X} - {self.X[i, m, t-1].X} ")
# else:
# print(f"D[{i}, {m}, {t}] >= {self.X[i, m, t].X} ")
# print(">>> OF [PI model]: ", self.model.getObjective().getValue())
# the of is different due to the first time step, but for the initial condition this is fixed
return self.model.getObjective().getValue(), sol, comp_time
else:
print("MODEL INFEASIBLE OR UNBOUNDED")
return -1, [], comp_time
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