def SolveDetModel(self, demandScenario):
def GetResults(self, DetModel, I, Z, Transshipment_X):
resultsdict = {}
plants = range(self.supplier)
items = range(self.holding_cost.shape[0])
resultsdict['# of Open DC'] = sum([Z[i].x for i in plants])
resultsdict['Open DC'] = [Z[i].x for i in plants]
resultsdict['Total Inventory'] = sum(
[I[k, i].x for k in items for i in plants])
resultsdict['Inventory'] = [[I[k, i].x for i in plants]
for k in items]
resultsdict['Fixed Cost'] = sum(
[Z[i].x * self.fixed_cost[i] for i in plants])
resultsdict['Holding Cost'] = [
sum([I[k, i].x * self.holding_cost[k, i] for i in plants])
for k in items
]
return resultsdict
DetModel = grb.Model('DetModel')
DetModel.setParam('OutputFlag', 0)
DetModel.modelSense = grb.GRB.MINIMIZE
plants = range(self.supplier)
demand = range(self.retailer)
items = range(self.lower_demand.shape[0])
I = DetModel.addVars(items, plants, vtype=grb.GRB.CONTINUOUS, name='I')
Z = DetModel.addVars(plants, vtype=grb.GRB.BINARY, name='Z')
Transshipment_X = DetModel.addVars(items,
plants,
demand,
vtype=grb.GRB.CONTINUOUS,
name='Transshipment_X')
objFunc_holding = sum(
[I[k, i] * self.holding_cost[k, i] for k in items for i in plants])
objFunc_fixed = sum([Z[i] * self.fixed_cost[i] for i in plants])
objFunc_transportation = grb.quicksum([
Transshipment_X[k, i, j] * self.transportation_cost[k, i, j]
for k in items for i in plants for j in demand
])
objFunc = objFunc_holding + objFunc_fixed + objFunc_transportation
DetModel.setObjective(objFunc)
#添加评估模型的约束
#约束1
for i in plants:
for k in items:
DetModel.addConstr(
grb.quicksum([Transshipment_X[k, i, j]
for j in demand]) <= I[k, i])
#约束2
for j in demand:
for k in items:
DetModel.addConstr(
grb.quicksum([Transshipment_X[k, i, j]
for i in plants]) == demandScenario[k, j])
#约束3 其实是paper中的4，gurobi自带正数约束
for i in plants:
for j in demand:
if round(self.graph[i, j]) == 0:
for k in items:
DetModel.addConstr(Transshipment_X[k, i, j] <= 0)
#约束4 I_i<=M*Z_i
for i in plants:
for k in items:
DetModel.addConstr(I[k, i] <= 100000 * Z[i])
#求解评估模型
DetModel.optimize()
return GetResults(self, DetModel, I, Z, Transshipment_X)
def learn_rule(self, domain, data, pos_uncovered_indices, neg_indices):
# Create a new model
m = milp.Model("kDNF")
print("Data")
for row, l in data:
print(*([
"{}: {:.2f}".format(v, Learner._convert(row[v]))
for v in domain.variables
] + [l]))
print()
# --- Computed constants
n_r = len(domain.real_vars)
n_b = len(domain.bool_vars)
n_h = self.max_hyperplanes_per_rule
n_e = len(data)
k = self.max_terms_per_rule
mu = 0.005
x_er = [[Learner._convert(row[v]) for v in domain.real_vars]
for row, _ in data]
x_eb = [[Learner._convert(row[v]) for v in domain.bool_vars]
for row, _ in data]
# --- Variables
# Inequality h is selected for the rule
s_h = [
m.addVar(vtype=milp.GRB.BINARY, name="s_h({h})".format(h=h))
for h in range(n_h)
]
# Example e covered by inequality h
c_eh = [[
m.addVar(vtype=milp.GRB.BINARY,
name="c_eh({e}, {h})".format(e=e, h=h))
for h in range(n_h)
] for e in range(n_e)]
# Example e covered by rule
c_e = [
m.addVar(vtype=milp.GRB.BINARY, name="c_e({e})".format(e=e))
for e in range(n_e)
]
# Boolean feature (or negation) selected
s_b = [
m.addVar(vtype=milp.GRB.BINARY, name="s_b({b})".format(b=b))
for b in range(2 * n_b)
]
# Coefficient of the r-th variable of inequality h
a_hr = [[
m.addVar(vtype=milp.GRB.CONTINUOUS,
lb=-1,
ub=1,
name="a_hr({h}, {r})".format(h=h, r=r))
for r in range(n_r)
] for h in range(n_h)]
# Offset of inequality h
b_h = [
m.addVar(vtype=milp.GRB.CONTINUOUS,
lb=-1,
ub=1,
name="b_h({h})".format(h=h)) for h in range(n_h)
]
b_abs_h = [
m.addVar(vtype=milp.GRB.CONTINUOUS,
lb=-1,
ub=1,
name="b_abs_h({h})".format(h=h)) for h in range(n_h)
]
# Auxiliary variable (c_eh AND s_h)
and_eh = [[
m.addVar(vtype=milp.GRB.BINARY,
name="and_eh({e}, {h})".format(e=e, h=h))
for h in range(n_h)
] for e in range(n_e)]
# Auxiliary variable (x_eb AND s_b)
and_eb = [[
m.addVar(vtype=milp.GRB.BINARY,
name="and_eb({e}, {b})".format(e=e, b=b))
for b in range(n_b)
] for e in range(n_e)]
print()
# Set objective
m.setObjective(sum(c_e[e] for e in range(n_e) if data[e][1]),
milp.GRB.MAXIMIZE)
# Add constraint: and_eh <= c_eh
for e in range(n_e):
if not data[e][1]:
for h in range(n_h):
name = "and_eh({e}, {h}) <= c_eh({e}, {h})".format(e=e,
h=h)
m.addConstr(and_eh[e][h] <= c_eh[e][h], name)
print(name)
print()
# Add constraint: and_eh <= s_h
for e in range(n_e):
if not data[e][1]:
for h in range(n_h):
name = "and_eh({e}, {h}) <= s_h({h})".format(e=e, h=h)
m.addConstr(and_eh[e][h] <= s_h[h], name)
print(name)
print()
# Add constraint: and_eh >= c_eh + s_h - 1
for e in range(n_e):
if not data[e][1]:
for h in range(n_h):
name = "and_eh({e}, {h}) >= c_eh({e}, {h}) + s_h({h}) - 1".format(
e=e, h=h)
m.addConstr(and_eh[e][h] >= c_eh[e][h] + s_h[h] - 1, name)
print(name)
print()
# Add constraint: and_eb <= x_eb
for e in range(n_e):
if not data[e][1]:
for b in range(2 * n_b):
name = "and_eb({e}, {b}) <= x_eb({e}, {b})".format(e=e,
b=b)
m.addConstr(
and_eb[e][b] <=
(x_eb[e][b] if b < n_b else 1 - x_eb[e][b]), name)
print(name)
print()
# Add constraint: and_eb <= s_h
for e in range(n_e):
if not data[e][1]:
for b in range(2 * n_b):
name = "and_eb({e}, {b}) <= s_b({b})".format(e=e, b=b)
m.addConstr(and_eb[e][b] <= s_b[b], name)
print(name)
print()
# Add constraint: and_eb >= x_eb + s_b - 1
for e in range(n_e):
if not data[e][1]:
for b in range(2 * n_b):
name = "and_eb({e}, {b}) >= x_eb({e}, {b}) + s_b({b}) - 1".format(
e=e, b=b)
m.addConstr(
and_eb[e][b] >=
(x_eb[e][b] if b < n_b else 1 - x_eb[e][b]) + s_b[b] -
1, name)
print(name)
print()
# Add constraint: SUM_(h = 1..n_h) and_eh - s_h + SUM_(b = 1..2 * n_b) and_eb - s_b <= -1
for e in range(n_e):
if not data[e][1]:
name = "SUM_(h = 1..n_h) and_eh({e}, h) - s_h(h) + SUM_(b = 1..2*n_b) and_eb({e}, b) - s_b(b) <= -1"\
.format(e=e)
m.addConstr(
sum(and_eh[e][h] - s_h[h]
for h in range(n_h)) + sum(and_eb[e][b] - s_b[b]
for b in range(2 * n_b)) <=
-1, name)
print(name)
# Add constraint: c_e <= c_eh + (1 - s_h)
for e in range(n_e):
if data[e][1]:
for h in range(n_h):
name = "c_e({e}) <= c_eh({e}, {h}) + (1 - s_h({h}))".format(
e=e, h=h)
m.addConstr(c_e[e] <= c_eh[e][h] + (1 - s_h[h]), name)
print(name)
# Add constraint: c_e <= x_eb + (1 - s_b)
for e in range(n_e):
if data[e][1]:
for b in range(2 * n_b):
name = "c_e({e}) <= x_eb({e}, {b}) + (1 - s_b({b}))".format(
e=e, b=b)
m.addConstr(c_e[e] <= x_eb[e][b] + (1 - s_b[b]), name)
print(name)
# Add constraint: SUM_(r = 1..n_r) a_hr * x_er <= b_h + 2 * (1 - c_eh)
for e in range(n_e):
for h in range(n_h):
name = "SUM_(r = 1..n_r) a_hr({h}, r) * x_er({e}, r) <= b_h({h}) + 2 * (1 - c_eh({e}, {h}))"\
.format(e=e, h=h)
m.addConstr(
sum(a_hr[h][r] * x_er[e][r]
for r in range(n_r)) <= b_h[h] + 2 * (1 - c_eh[e][h]),
name)
print(name)
# Add constraint: SUM_(r = 1..n_r) a_hr * x_er >= b_h - mu - 2 * c_eh
for e in range(n_e):
for h in range(n_h):
name = "SUM_(r = 1..n_r) a_hr({h}, r) * x_er({e}, r) >= b_h({h}) - mu - 2 * c_eh({e}, {h})"\
.format(e=e, h=h)
m.addConstr(
sum(a_hr[h][r] * x_er[e][r]
for r in range(n_r)) >= b_h[h] + mu - 2 * c_eh[e][h],
name)
print(name)
# Add constraint: SUM_(h = 1..n_h) s_h + SUM_(b = 1..n_b) s_b <= k
name = "SUM_(h = 1..n_h) s_h(h) + SUM_(b = 1..n_b) s_b(b) <= k"
m.addConstr(
sum(s_h[h]
for h in range(n_h)) + sum(s_b[b]
for b in range(2 * n_b)) <= k, name)
print(name)
# Hack example
# m.addConstr(a[0][0] == 1)
# m.addConstr(a[0][1] == 0)
# m.addConstr(b[0] == 0.5)
#
# m.addConstr(a[1][0] == 0)
# m.addConstr(a[1][1] == 1)
# m.addConstr(b[1] == 0.5)
#
# m.addConstr(a[2][0] == -1)
# m.addConstr(a[2][1] == 0)
# m.addConstr(b[2] == -0.5)
#
# m.addConstr(a[3][0] == 0)
# m.addConstr(a[3][1] == -1)
# m.addConstr(b[3] == -0.5)
#
# m.addConstr(s_h[0] == 1)
# m.addConstr(s_h[1] == 1)
# m.addConstr(s_h[2] == 1)
# m.addConstr(s_h[3] == 1)
# m.addConstr(z_h[0][0] == 1)
# m.addConstr(z_h[1][0] == 1)
# m.addConstr(z_h[2][0] == 0)
# m.addConstr(z_h[3][0] == 0)
# m.addConstr(z_h[0][1] == 0)
# m.addConstr(z_h[1][1] == 0)
# m.addConstr(z_h[2][1] == 1)
# m.addConstr(z_h[3][1] == 1)
# m.addConstr(z_ih[0][0] == 1)
# m.addConstr(z_ih[1][0] == 0)
# m.addConstr(z_ih[2][0] == 1)
# m.addConstr(z_ih[3][0] == 0)
# m.addConstr(z_ih[0][1] == 1)
# m.addConstr(z_ih[1][1] == 0)
# m.addConstr(z_ih[2][1] == 0)
# m.addConstr(z_ih[3][1] == 1)
# m.addConstr(z_ih[0][2] == 0)
# m.addConstr(z_ih[1][2] == 1)
# m.addConstr(z_ih[2][2] == 0)
# m.addConstr(z_ih[3][2] == 1)
# m.addConstr(z_ih[0][3] == 0)
# m.addConstr(z_ih[1][3] == 1)
# m.addConstr(z_ih[2][3] == 1)
# m.addConstr(z_ih[3][3] == 0)
# m.addConstr(z_d[0][0] == 1)
# m.addConstr(z_d[1][0] == 0)
# m.addConstr(z_d[2][0] == 0)
# m.addConstr(z_d[3][0] == 0)
# m.addConstr(z_d[0][1] == 0)
# m.addConstr(z_d[1][1] == 0)
# m.addConstr(z_d[2][1] == 1)
# m.addConstr(z_d[3][1] == 0)
# Wrong solution
# m.addConstr(a[0][0] == 1)
# m.addConstr(a[0][1] == 0)
# m.addConstr(b[0] == 0.5)
#
# m.addConstr(a[1][0] == 0)
# m.addConstr(a[1][1] == 1)
# m.addConstr(b[1] == 0.5)
#
# m.addConstr(a[2][0] == -1)
# m.addConstr(a[2][1] == 0)
# m.addConstr(b[2] == -0.6)
#
# m.addConstr(a[3][0] == 0)
# m.addConstr(a[3][1] == -1)
# m.addConstr(b[3] == -0.6)
#
# m.addConstr(s_h[0] == 1)
# m.addConstr(s_h[1] == 1)
# m.addConstr(s_h[2] == 1)
# m.addConstr(s_h[3] == 1)
# m.addConstr(s_h[0] == 1)
# m.addConstr(s_h[1] == 1)
# m.addConstr(s_h[2] == 0)
# m.addConstr(s_h[3] == 0)
#
# m.addConstr(a_hr[0][0] == -1)
# m.addConstr(a_hr[0][1] == -0.5)
# m.addConstr(b_h[0] == -0.2)
#
# m.addConstr(a_hr[1][0] == 1)
# m.addConstr(a_hr[1][1] == 0)
# m.addConstr(b_h[1] == 0.75)
m.optimize()
for v in m.getVars():
print(v.varName, v.x)
print('Obj:', m.objVal)
print()
# Lets extract the rule formulation
rule = []
from pysmt.shortcuts import Real, LE, Plus, Times, Not
for h in range(n_h):
if int(s_h[h].x) == 1:
coefficients = [Real(float(a_hr[h][r].x)) for r in range(n_r)]
constant = Real(float(b_h[h].x))
linear_sum = Plus([
Times(c, domain.get_symbol(v))
for c, v in zip(coefficients, domain.real_vars)
])
rule.append(LE(linear_sum, constant))
for b in range(2 * n_b):
if int(s_b[b].x) == 1:
var = domain.get_symbol(domain.bool_vars[b if b < n_b else b -
n_b])
rule.append(var if b < n_b else Not(var))
print("Features", x_er[4][0], x_er[4][1])
print(sum(a_hr[1][r].x * x_er[4][r] for r in range(n_r)), "<=",
b_h[1].x + 2 * (1 - c_eh[4][1].x))
print(sum(a_hr[1][r].x * x_er[4][r] for r in range(n_r)), ">=",
b_h[1].x + mu - 2 * c_eh[4][1].x)
return rule
def SolveStoModel(self, STOMODELCONFIGURE):
def GetResults(self, I, Z, Transshipment_X, N):
plants = range(self.supplier)
items = range(self.lower_demand.shape[0])
objVal_holding = sum([
I[k, i].x * self.holding_cost[k, i] for k in items
for i in plants
])
objVal_fixed = sum([Z[i].x * self.fixed_cost[i] for i in plants])
resultsdict = {}
plants = range(self.supplier)
resultsdict['# of Open DC'] = sum([Z[i].x for i in plants])
resultsdict['Open DC'] = [Z[i].x for i in plants]
resultsdict['Total Inventory'] = [
sum([I[k, i].x for i in plants]) for k in items
]
resultsdict['Inventory'] = [[I[k, i].x for i in plants]
for k in items]
resultsdict['Fixed Cost'] = objVal_fixed
resultsdict['Holding Cost'] = [
sum([I[k, i].x * self.holding_cost[k, i] for i in plants])
for k in items
]
return resultsdict
N = STOMODELCONFIGURE['#']
demandScenario = STOMODELCONFIGURE['demandScenario']
StoModel = grb.Model('StoModel')
StoModel.setParam('OutputFlag', 0)
StoModel.modelSense = grb.GRB.MINIMIZE
items = range(demandScenario.shape[0])
plants = range(self.supplier)
demand = range(self.retailer)
I = StoModel.addVars(items, plants, vtype=grb.GRB.CONTINUOUS, name='I')
Z = StoModel.addVars(plants, vtype=grb.GRB.BINARY, name='Z')
Transshipment_X = StoModel.addVars(items,
range(N),
plants,
demand,
vtype=grb.GRB.CONTINUOUS,
name='Transshipment_X')
objFunc_holding = grb.quicksum(
[I[k, i] * self.holding_cost[k, i] for k in items for i in plants])
objFunc_fixed = grb.quicksum(
[Z[i] * self.fixed_cost[i] for i in plants])
objFunc_penalty = grb.quicksum([
grb.quicksum([
self.penalty_cost[k, j] *
(demandScenario[k, scenario, j] - grb.quicksum(
[Transshipment_X[k, scenario, i, j] for i in plants]))
for k in items for j in demand
]) for scenario in range(N)
])
objFunc_trans = grb.quicksum([
Transshipment_X[k, scenario, i, j] *
self.transportation_cost[k, i, j] for scenario in range(N)
for k in items for i in plants for j in demand
]) #N个Scenario的transportation cost总和
objFunc = objFunc_holding + objFunc_fixed + (objFunc_penalty +
objFunc_trans) / N
StoModel.setObjective(objFunc)
'''
TODO
'''
#约束1
for i in plants:
for k in items:
StoModel.addConstrs((grb.quicksum(
[Transshipment_X[k, scenario, i, j]
for j in demand]) <= I[k, i] for scenario in range(N)))
#约束2
for j in demand:
for k in items:
StoModel.addConstrs((grb.quicksum(
[Transshipment_X[k, scenario, i, j]
for i in plants]) <= demandScenario[k, scenario, j]
for scenario in range(N)))
#约束3
for i in plants:
for j in demand:
for k in items:
if round(self.graph[i, j]) == 0:
StoModel.addConstrs(
(Transshipment_X[k, scenario, i, j] <= 0
for scenario in range(N)))
#约束4 I_i<=M*Z_i
for i in plants:
for k in items:
StoModel.addConstr(I[k, i] <= 100000 * Z[i])
#求解评估模型
StoModel.optimize()
return GetResults(self, I, Z, Transshipment_X, N)
b = np.asarray([[
0, 0, -SCENARIOS[n][0], SCENARIOS[n][0] * 0.8, -SCENARIOS[n][1],
SCENARIOS[n][1] * 0.8
] for n in range(k)])
'''
Step 1:
1.0. establish the master problem
1.1. add initial constraints
1.2. solve master problem
1.3. get solutions(x,\ita) for the subproblem
'''
# --- 1.0. establish master Model with scenarios
#Build Model
masterProblem = grb.Model('MasterProblem')
masterProblem.modelSense = grb.GRB.MINIMIZE
masterProblem.setParam('OutputFlag', 0)
#Create Variables
#First-stage Variables
X = masterProblem.addVars([1, 2],
vtype=grb.GRB.CONTINUOUS,
lb=[0, 0],
obj=[3, 2],
name='X')
#Second-stage recourse value variables
ITA = masterProblem.addVars(
range(1, k + 1),
vtype=grb.GRB.CONTINUOUS,
def Calculate_Upper_bound(self,customer_sequence):
#TODO check compatibility and correctness
#Input: List customer_sequence: 每个元素是客户类对象
#Output:
#通过解Paat Lemma1 中的LP得到Upper Bound
from gurobipy import gurobipy as grb
customer_sequence = [customer.type for customer in customer_sequence]
#--- 申明问题
primal_problem = grb.Model()
primal_problem.setParam('OutputFlag',0)
primal_problem.modelSense = grb.GRB.MAXIMIZE
#--- 申明变量
num_col = len(customer_sequence)
num_row = int(len(self.possible_offer_set)/len(self.customer))
Y = primal_problem.addVars(range(num_row),range(1,num_col+1), #行：set集合，index从0开始； 列：时间T，index从1开始
vtype=grb.GRB.CONTINUOUS, lb=0,ub=1,
name='y')
#--- 设置objective function
obj = 0
for t,customer_type in enumerate(customer_sequence):
t = t+1 #时间变到正常的从1 开始 到 T
for s,offer_set in enumerate(self.possible_offer_set[:num_row]):
key = json.dumps({
'customer_type':customer_type,
'offered_set':offer_set
})
item_prob = self.PROBABILITY[key]
for item_id in offer_set: # 不在offer_set里面无收益
obj += item_prob[item_id]*self.item[item_id]['price']*Y[s,t]
primal_problem.setObjective(obj)
#--- 加入constraints
#capacity 限制
for item_id in list(self.item.keys())[1:]:
possible_buy_number = 0
for t,customer_type in enumerate(customer_sequence):
t = t+1 #时间变到正常的从1 开始 到 T
for s,offer_set in enumerate(self.possible_offer_set[:num_row]):
if item_id in offer_set:
key = json.dumps({
'customer_type':customer_type,
'offered_set':offer_set
})
item_prob = self.PROBABILITY[key]
possible_buy_number += item_prob[item_id]*Y[s,t]
primal_problem.addConstr(possible_buy_number<=self.item[item_id]['initial_inventory']+self.item[item_id]['extra_inventory'])
#possibility 约束
for t in range(1,len(customer_sequence)+1): #1-->T
possibility = grb.quicksum([Y[s,t] for s in range(len(self.possible_offer_set[:num_row]))])
primal_problem.addConstr(possibility==1)
primal_problem.write('11.lp')
primal_problem.optimize()
if primal_problem.status == grb.GRB.Status.OPTIMAL:
print('successfully get hindsight upper bound')
return primal_problem.ObjVal
elif primal_problem.status == grb.GRB.Status.INFEASIBLE:
print('upper bound model is infeasible.')
else:
print('something wrong with solving upper bound problem.')
def simulationWithCost_Multi(a,
demand,
Z,
I,
num_plants,
num_demand,
tranposrtation_cost,
penalty_cost,
cishu=500):
print('--------Mixed_distribution_simulation----------')
#lower_demand = demand[0]
#mean_demand = demand[1]
#upper_demand = demand[2]
realized_demand = np.zeros(shape=(cishu, num_demand))
realized_demand = demand
items = range(realized_demand.shape[0])
plants = range(num_plants)
demand = range(num_demand)
results = []
trans_cost_array = np.zeros(shape=(cishu, realized_demand.shape[0]))
penal_cost_array = np.zeros(shape=(cishu, realized_demand.shape[0]))
for n in range(cishu):
oneDemandScenario = realized_demand[:, n, :]
#申明Fillrate中Z(w)值的那个模型
evaluation_problem = grb.Model('evaluation_problem')
evaluation_problem.setParam('OutputFlag', 0)
evaluation_problem.modelSense = grb.GRB.MINIMIZE
evaluation_X = evaluation_problem.addVars(items,
plants,
demand,
vtype=grb.GRB.CONTINUOUS,
name='evaluation_X')
objFunc_penal = grb.quicksum([
penalty_cost[k, j] *
(oneDemandScenario[k, j] -
grb.quicksum([evaluation_X[k, i, j] for i in plants]))
for k in items for j in demand
])
objFunc_trans = grb.quicksum(
evaluation_X[k, i, j] * tranposrtation_cost[k, i, j] for k in items
for i in plants for j in demand)
evaluation_problem.setObjective(objFunc_penal + objFunc_trans)
#添加评估模型的约束
#约束1
for k in items:
for i in plants:
evaluation_problem.addConstr(
grb.quicksum([evaluation_X[k, i, j]
for j in demand]) <= I[k, i])
#约束2
for k in items:
for j in demand:
evaluation_problem.addConstr(
grb.quicksum([evaluation_X[k, i, j]
for i in plants]) <= oneDemandScenario[k, j])
#约束3 其实是paper中的4，gurobi自带正数约束
for i in plants:
for j in demand:
if round(a[i, j] * Z[i]) == 0:
for k in items:
evaluation_problem.addConstr(
evaluation_X[k, i, j] <= 0)
#求解评估模型，并求出fillrate 还要输出
evaluation_problem.optimize()
#transportation
trans = np.zeros(shape=(realized_demand.shape[0], num_plants,
num_demand))
for k in items:
for i in plants:
for j in demand:
trans[k, i, j] = evaluation_X[k, i, j].x
type1ServiceLevel = (np.abs(trans.sum(axis=1) - oneDemandScenario) <=
0.0005).sum() / (num_demand *
realized_demand.shape[0])
type2ServiceLevel = trans.sum() / oneDemandScenario.sum()
trans_cost = (trans * tranposrtation_cost).sum(axis=1).sum(axis=1)
penal_cost = ((oneDemandScenario - trans.sum(axis=1)) *
penalty_cost).sum(axis=1)
results.append([
type1ServiceLevel, type2ServiceLevel,
trans_cost.sum(),
penal_cost.sum()
])
trans_cost_array[n, :] = trans_cost
penal_cost_array[n, :] = penal_cost
type1ServiceLevel_mean, type2ServiceLevel_mean, trans_cost_mean, penal_cost_mean = np.mean(
np.asarray(results), axis=0)
trans_cost_mean = [cost for cost in np.mean(trans_cost_array, axis=0)]
penal_cost_mean = [cost for cost in np.mean(penal_cost_array, axis=0)]
type1ServiceLevel_worst, type2ServiceLevel_worst, trans_cost_best, penal_cost_best = np.amin(
np.asarray(results), axis=0)
return type1ServiceLevel_mean, type2ServiceLevel_mean, type1ServiceLevel_worst, type2ServiceLevel_worst, trans_cost_mean, penal_cost_mean
def triangular_simulation(a,
demand,
Z,
I,
mode,
num_plants,
num_demand,
cishu=500):
print('--------triangular_simulation----------')
lower_demand = demand[0]
mean_demand = demand[1]
upper_demand = demand[2]
total_CC = 0
total_fillrate = 0
total_arcs = 0
plants = range(num_plants)
demand = range(num_demand)
for n in range(cishu):
realized_demand = [0] * len(lower_demand)
for k in range(len(realized_demand)):
realized_demand[k] = round(
random.triangular(lower_demand[k], upper_demand[k],
mean_demand[k] * mode))
#申明Fillrate中Z(w)值的那个模型
evaluation_problem = grb.Model('evaluation_problem')
evaluation_problem.setParam('OutputFlag', 0)
evaluation_problem.modelSense = grb.GRB.MAXIMIZE
#申明评估模型的变量
evaluation_X = evaluation_problem.addVars(plants,
demand,
obj=[[1 for j in demand]
for i in plants],
vtype=grb.GRB.INTEGER,
name='evaluation_X')
#添加评估模型的约束
#约束1
for i in plants:
expr = 0
for j in demand:
expr = expr + evaluation_X[i, j]
evaluation_problem.addConstr(expr <= I[i])
del expr
#约束2
for j in demand:
expr = 0
for i in plants:
expr = expr + evaluation_X[i, j]
evaluation_problem.addConstr(expr <= realized_demand[j])
del expr
#约束3 其实是paper中的4，gurobi自带正数约束
for i in plants:
for j in demand:
if round(a[i, j] * Z[i]) == 0:
evaluation_problem.addConstr(evaluation_X[i, j] <= 0)
#求解评估模型，并求出fillrate 还要输出
evaluation_problem.optimize()
#########################运输完成了
#########################计算用上的道路arcs
arcs = 0
for i in plants:
for j in demand:
arcs = (round(evaluation_X[i, j].x) > 0.1) + arcs
########################CC的值##########################
##################来看是否满足cutting plane的LP
#申明CC问题
CC_problem = grb.Model('CC_problem')
CC_problem.setParam('OutputFlag', 0)
#申明CC问题的变量
CC_x = CC_problem.addVars(demand, vtype=grb.GRB.BINARY, name='CC_x')
CC_y = CC_problem.addVars(plants, vtype=grb.GRB.BINARY, name='CC_y')
#设置目标函数
expr1 = 0
expr2 = 0
for i in plants:
expr1 = expr1 + CC_y[i] * I[i]
for j in demand:
expr2 = expr2 + realized_demand[j] * CC_x[j]
CC_problem.setObjective(expr1 - expr2, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入CC问题的约束
#约束1
for i in plants:
for j in demand:
if round(a[i, j] * round(Z[i])) == 1:
CC_problem.addConstr(CC_y[i] >= CC_x[j])
#解CC问题
CC_problem.optimize()
total_CC += (CC_problem.ObjVal >= 0)
total_fillrate += evaluation_problem.ObjVal / sum(realized_demand)
total_arcs += arcs
if n % 100 == 0:
print(n)
eva_X = np.zeros(shape=(num_plants, num_demand))
for i in plants:
for i in demand:
eva_X[i, j] = round(evaluation_X[i, j].x)
return eva_X, total_arcs / cishu, total_CC / cishu, total_fillrate / cishu
def uniform_simulation(a, demand, Z, I, num_plants, num_demand, cishu=500):
print('--------uniform_simulation----------')
lower_demand = demand[0]
upper_demand = demand[2]
plants = range(num_plants)
demand = range(num_demand)
results = []
for n in range(cishu):
realized_demand = [0] * len(lower_demand)
for k in range(len(realized_demand)):
realized_demand[k] = round(
random.randint(lower_demand[k], upper_demand[k]))
#申明Fillrate中Z(w)值的那个模型
evaluation_problem = grb.Model('evaluation_problem')
evaluation_problem.setParam('OutputFlag', 0)
evaluation_problem.modelSense = grb.GRB.MAXIMIZE
evaluation_X = evaluation_problem.addVars(plants,
demand,
obj=[[1 for j in demand]
for i in plants],
vtype=grb.GRB.INTEGER,
name='evaluation_X')
#添加评估模型的约束
#约束1
for i in plants:
expr = 0
for j in demand:
expr = expr + evaluation_X[i, j]
evaluation_problem.addConstr(expr <= I[i])
del expr
#约束2
for j in demand:
expr = 0
for i in plants:
expr = expr + evaluation_X[i, j]
evaluation_problem.addConstr(expr <= realized_demand[j])
del expr
#约束3 其实是paper中的4，gurobi自带正数约束
for i in plants:
for j in demand:
if round(a[i, j] * Z[i]) == 0:
evaluation_problem.addConstr(evaluation_X[i, j] <= 0)
#求解评估模型，并求出fillrate 还要输出
evaluation_problem.optimize()
type1ServiceLevel = sum([
np.abs(
sum([evaluation_X[i, j].x
for i in plants]) - realized_demand[j]) <= 0.005
for j in demand
]) / num_demand
type2ServiceLevel = sum(
[sum([evaluation_X[i, j].x for i in plants])
for j in demand]) / sum(realized_demand)
results.append([type1ServiceLevel, type2ServiceLevel])
return np.mean(np.asarray(results), axis=0)
def simulationWithCost(a,
demand,
Z,
I,
num_plants,
num_demand,
tranposrtation_cost,
penalty_cost,
cishu=500):
print('--------Mixed_distribution_simulation----------')
#lower_demand = demand[0]
#mean_demand = demand[1]
#upper_demand = demand[2]
realized_demand = np.zeros(shape=(cishu, num_demand))
realized_demand = demand
plants = range(num_plants)
demand = range(num_demand)
results = []
for n in range(cishu):
oneDemandScenario = realized_demand[n]
#申明Fillrate中Z(w)值的那个模型
evaluation_problem = grb.Model('evaluation_problem')
evaluation_problem.setParam('OutputFlag', 0)
evaluation_problem.modelSense = grb.GRB.MINIMIZE
evaluation_X = evaluation_problem.addVars(plants,
demand,
vtype=grb.GRB.CONTINUOUS,
name='evaluation_X')
objFunc_penal = grb.quicksum([
penalty_cost[j] *
(oneDemandScenario[j] -
grb.quicksum([evaluation_X[i, j] for i in plants]))
for j in demand
])
objFunc_trans = grb.quicksum(evaluation_X[i, j] *
tranposrtation_cost[i, j] for i in plants
for j in demand)
evaluation_problem.setObjective(objFunc_penal + objFunc_trans)
#添加评估模型的约束
#约束1
for i in plants:
expr = 0
for j in demand:
expr = expr + evaluation_X[i, j]
evaluation_problem.addConstr(expr <= I[i])
del expr
#约束2
for j in demand:
expr = 0
for i in plants:
expr = expr + evaluation_X[i, j]
evaluation_problem.addConstr(expr <= oneDemandScenario[j])
del expr
#约束3 其实是paper中的4，gurobi自带正数约束
for i in plants:
for j in demand:
if round(a[i, j] * Z[i]) == 0:
evaluation_problem.addConstr(evaluation_X[i, j] <= 0)
#求解评估模型，并求出fillrate 还要输出
evaluation_problem.optimize()
#transportation
trans = np.zeros(shape=(num_plants, num_demand))
for i in plants:
for j in demand:
trans[i, j] = evaluation_X[i, j].x
type1ServiceLevel = sum(
np.abs(trans.sum(axis=0) -
oneDemandScenario) <= 0.0005) / num_demand
type2ServiceLevel = trans.sum() / sum(oneDemandScenario)
trans_cost = (trans * tranposrtation_cost).sum()
penal_cost = sum(
(oneDemandScenario - trans.sum(axis=0)) * penalty_cost)
results.append(
[type1ServiceLevel, type2ServiceLevel, trans_cost, penal_cost])
type1ServiceLevel_mean, type2ServiceLevel_mean, trans_cost_mean, penal_cost_mean = np.mean(
np.asarray(results), axis=0)
type1ServiceLevel_worst, type2ServiceLevel_worst, trans_cost_best, penal_cost_best = np.amin(
np.asarray(results), axis=0)
return type1ServiceLevel_mean, type2ServiceLevel_mean, type1ServiceLevel_worst, type2ServiceLevel_worst, trans_cost_mean, penal_cost_mean
num_demand = []
# length that every buyer need
L = []
for amount, l in zip(demand['Amount'].values, demand['Length'].values):
num_demand.append(int(amount))
L.append(l)
#
buyer = range(len(num_demand))
max_material = range(sum(num_demand))
#General model
#estabilish model
m = grb.Model('model1')
m.modelSense = grb.GRB.MINIMIZE
m.setParam('OutputFlag', 0)
# addvars
X = m.addVars(max_material, buyer, vtype=grb.GRB.INTEGER, ub=1, lb=0, obj=0)
Y = m.addVars(max_material, vtype=grb.GRB.INTEGER, ub=1, lb=0, obj=1)
#add constraints
#1rt : aggregated cutted length should less than total length
for i in max_material:
expr = 0
for j in buyer:
expr = X[i, j] * L[j] + expr
m.addConstr(expr <= Y[i] * length)
def hoeffding_design(cost,
demand,
a,
num_plants,
num_demand,
service_level=0.1):
fixed_cost = cost[0]
holding_cost = cost[1]
lower_demand = demand[0]
mean_demand = demand[1]
upper_demand = demand[2]
plants = range(num_plants)
demand = range(num_demand)
inventory = range(num_plants)
#记录每次加入的约束是Case1 or Case2 or Others0
constraintType = []
#记录每次迭代时候的objvalue
iterValue = []
SNum = []
#------------申明主问题-----------#
#申明主模型
master_problem = grb.Model('master_problem')
master_problem.setParam('OutputFlag', 0)
master_problem.modelSense = grb.GRB.MINIMIZE
#申明决策变量 obj=***就表明了该组变量在目标函数中的系数
####这一块参考gurobi给的例子：facility.py
I = master_problem.addVars(inventory,
vtype=grb.GRB.INTEGER,
obj=holding_cost,
name='I')
Z = master_problem.addVars(plants,
vtype=grb.GRB.BINARY,
obj=fixed_cost,
name='Z')
#----------载入各种约束-----------#
#利用hoeffding inequality计算总库存水平
temp_sum = sum((upper_demand - lower_demand)**2)
TI = math.ceil(mean_demand.sum() +
math.sqrt(-math.log(service_level) * temp_sum / 2))
del temp_sum
#载入chance constraints
#至少有 demand个约束（至少每个需求点的需求，都能被和它相连的supplier满足吧）
for j in demand:
expr = sum(a[:, j] * I.values())
master_problem.addQConstr(
expr >= min(upper_demand[j], TI -
(lower_demand.sum() - lower_demand[j])))
del expr
#记录加入的约束的类型
SNum.append(1)
if upper_demand[j] < TI - (lower_demand.sum() - lower_demand[j]):
constraintType.append(1)
if upper_demand[j] > TI - (lower_demand.sum() - lower_demand[j]):
constraintType.append(2)
if upper_demand[j] == TI - (lower_demand.sum() - lower_demand[j]):
constraintType.append(3)
#载入约束3-----Ii<=M*Zi
M = upper_demand.sum()
master_problem.addConstrs(I[i] <= M * Z[i] for i in plants)
for i in plants: #记录加入的约束的类型
constraintType.append(0)
SNum.append(0)
#total inventory的约束
master_problem.addConstr(sum(I.values()) == TI)
constraintType.append(0) #记录加入的约束的类型
SNum.append(0)
#------------------开始迭代主问题------------------------------------#
#-------------------------------------------------------------------#
add_constraints_time = 0
time_start = time.clock()
while True:
#解主问题
master_problem.optimize()
#确定主问题解的状态
if master_problem.status == 3:
break
elif master_problem.status == 2:
print('Master_Val:', master_problem.ObjVal)
iterValue.append(master_problem.ObjVal)
#-------------开始子问题---------------#
#申明子问题
sub_problem = grb.Model('sub_problem')
sub_problem.setParam('OutputFlag', 0)
#申明子问题的变量
x = sub_problem.addVars(demand, vtype=grb.GRB.BINARY, name='x')
y = sub_problem.addVars(plants, vtype=grb.GRB.BINARY, name='y')
z = sub_problem.addVar(vtype=grb.GRB.CONTINUOUS, name='z')
#设置目标函数
expr1 = grb.quicksum([y[i] * I[i].x for i in plants])
expr2 = grb.quicksum([upper_demand[j] * x[j] for j in demand])
sub_problem.setObjective(expr1 - expr2 + z, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#z>0约束
sub_problem.addConstr(z >= 0)
#子问题约束1
expr1 = sum(upper_demand * x.values())
expr2 = grb.quicksum(
[lower_demand[j] * (1 - x[j]) for j in demand])
sub_problem.addConstr(z >= expr1 - TI + expr2)
del expr1
del expr2
#子问题约束2
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem.addConstr(y[i] >= x[j])
#解子问题
sub_problem.optimize()
#判断子问题的目标函数值是否为非负数，若是，则主问题得到了最优解,break；否则加入新的约束到主问题中
if sub_problem.ObjVal >= -0.001:
break
else:
print(sub_problem.ObjVal)
expr1 = grb.quicksum([I[i] * y[i].x for i in plants])
expr2 = sum([upper_demand[j] * x[j].x for j in demand])
expr3 = sum([lower_demand[j] * (1 - x[j].x) for j in demand])
temp_expr = (expr1 >= min(expr2, TI - expr3))
master_problem.addConstr(temp_expr) #加新的约束到主问题中
#记录加入的约束的类型
if expr2 < TI - expr3:
constraintType.append(1)
if expr2 > TI - expr3:
constraintType.append(2)
if expr2 == TI - expr3:
constraintType.append(3)
SNum.append(sum([x[j].x for j in demand]))
del expr1
del expr2
del expr3
del temp_expr
add_constraints_time += 1
time_end = time.clock()
################################到这里图就设计好了###################################
################################到这里图就设计好了###################################
#输出一些图的参数(仓库数，道路数，总库存=TI)
if master_problem.status == 2:
I_return = []
Z_return = []
open_DC = 0
for i in plants:
I_return.append(I[i].x)
Z_return.append(Z[i].x)
open_DC = Z[i].x + open_DC
p = TI
return master_problem, constraintType, iterValue, SNum, [
'hoeffding', open_DC, I_return, Z_return, p, p / TI,
master_problem.ObjVal, time_end - time_start, add_constraints_time,
'optimal'
]
elif master_problem.status == 3:
return master_problem, constraintType, iterValue, SNum, [
'-', '-', '-', '-', '-', '-', '-', '-', '-', 'infeasible'
]
def small_big_design_multiitem(cost,
demand,
a,
num_plants,
num_demand,
s,
S,
weight,
service_level=0.1,
mode='set'):
add_constraints_time = 0
fixed_cost = cost[0]
holding_cost = cost[1]
lower_demand = demand[0]
mean_demand = demand[1]
upper_demand = demand[2]
items = range(lower_demand.shape[0])
plants = range(num_plants)
demand = range(num_demand)
constraintType = []
iterValue = []
iterValue_small = []
iterValue_big = []
# ---- 申明主问题
#申明主模型
master_problem = grb.Model('master_problem')
master_problem.setParam('OutputFlag', 0)
master_problem.modelSense = grb.GRB.MINIMIZE
#申明决策变量 obj=***就表明了该组变量在目标函数中的系数
####这一块参考gurobi给的例子：facility.py
I = master_problem.addVars(items,
plants,
vtype=grb.GRB.CONTINUOUS,
obj=holding_cost,
name='I')
Z = master_problem.addVars(plants,
vtype=grb.GRB.BINARY,
obj=fixed_cost,
name='Z')
# ---- 载入各种约束
#额外算的值
TI = np.zeros(shape=lower_demand.shape[0])
for k in items:
temp_sum = sum((upper_demand[k] - lower_demand[k])**2)
TI[k] = math.ceil(mean_demand[k].sum() +
math.sqrt(-math.log(service_level) * temp_sum / 2))
del temp_sum
#载入chance constraints
#至少有 demand个约束（至少每个需求点的需求，都能被和它相连的supplier满足吧）
#这里是small set的约束
for k in items:
for j in demand:
expr = sum(a[:, j] * I.values()[13 * k:13 * (k + 1)])
master_problem.addConstr(expr >= upper_demand[k, j])
del expr
constraintType.append('small')
#这里是big set的约束
#要求|S|>25 （某数），最直观的的是取30时候，此时不属于这个集合的点是空集
#所以没有约束。
#第二个的约束去子问题中迭代加入
expr = 0
del expr
#载入约束3-----Ii<=M*Zi
M = upper_demand.sum()
master_problem.addConstrs(I[k, i] <= M * Z[i] for k in items
for i in plants)
for i in plants:
constraintType.append('others')
# ---- 开始迭代主问题
time_start = time.clock()
while True:
#解主问题
master_problem.optimize()
#确定主问题解的状态
if master_problem.status == 3:
iterValue.append(master_problem.ObjVal)
break
elif master_problem.status == 2:
print('master_Value:', master_problem.ObjVal)
iterValue.append(master_problem.ObjVal)
# ---- 开始子问题
IterSubObjVal = np.zeros(shape=(lower_demand.shape[0], 2))
IterX = np.zeros(shape=(lower_demand.shape[0],
lower_demand.shape[1], 2))
IterY = np.zeros(shape=(lower_demand.shape[0],
lower_demand.shape[1], 2))
# K-th item:
for k in items:
#申明small set的子问题----sub_problem1
sub_problem1 = grb.Model('sub_problem1')
sub_problem1.setParam('OutputFlag', 0)
#申明子问题的变量
x1 = sub_problem1.addVars(demand,
vtype=grb.GRB.BINARY,
name='x1')
y1 = sub_problem1.addVars(plants,
vtype=grb.GRB.BINARY,
name='y1')
#设置目标函数
expr1 = grb.quicksum([y1[i] * I[k, i].x for i in plants])
expr2 = sum(upper_demand[k] * x1.values())
#for j in demand:
# expr2 = expr2 + upper_demand[j] * x1[j]
sub_problem1.setObjective(expr1 - expr2, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
expr = sum(x1.values())
sub_problem1.addConstr(expr <= s)
del expr
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem1.addConstr(y1[i] >= x1[j])
#解子问题
sub_problem1.optimize()
#申明big set的子问题----sub_problem2 -----------
#big set
#big set
#big set
sub_problem2 = grb.Model('sub_problem1')
sub_problem2.setParam('OutputFlag', 0)
#申明子问题的变量
x2 = sub_problem2.addVars(demand,
vtype=grb.GRB.BINARY,
name='x2')
y2 = sub_problem2.addVars(plants,
vtype=grb.GRB.BINARY,
name='y2')
#设置目标函数
expr1 = grb.quicksum([(1 - y2[i]) * I[k, i].x for i in plants])
expr2 = grb.quicksum(
[lower_demand[k, j] * (1 - x2[j]) for j in demand])
sub_problem2.setObjective(expr2 - expr1, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
expr = grb.quicksum(x2.values())
sub_problem2.addConstr(expr >= S)
del expr
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem2.addConstr(y2[i] >= x2[j])
#解子问题
sub_problem2.optimize()
IterSubObjVal[k, 0] = sub_problem1.ObjVal
IterSubObjVal[k, 1] = sub_problem2.ObjVal
for j in demand:
IterX[k, j, 0] = x1[j].x
IterX[k, j, 1] = x2[j].x
for i in plants:
IterY[k, i, 0] = y1[i].x
IterY[k, i, 1] = y2[i].x
#判断子问题的目标函数值是否为非负数，若是，则主问题得到了最优解,break；否则加入新的约束到主问题中
if all(IterSubObjVal.reshape(6) >= -0.01):
iterValue_small.append(IterSubObjVal[k, 0] for k in items)
iterValue_big.append(IterSubObjVal[k, 1] for k in items)
break
else:
for k in items:
if IterSubObjVal[k, 0] < -0.001:
print('small_set:', IterSubObjVal[k, 0])
iterValue_small.append(IterSubObjVal[k, 0])
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = IterX[k, j, 0] * a[:, j] + a_temp
expr2 = upper_demand[k, j] * IterX[k, j, 0] + expr2
for i in plants:
if round(a_temp[i]) != 0:
expr1 = expr1 + round(a_temp[i]) / round(
a_temp[i]) * I[k, i]
temp_expr = expr1 >= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
constraintType.append('small')
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
else:
iterValue_small.append(np.nan)
if IterSubObjVal[k, 1] < -0.001:
print('big_set:', IterSubObjVal[k, 1])
iterValue_big.append(IterSubObjVal[k, 1])
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = IterX[k, j, 1] * a[:, j] + a_temp
expr2 = lower_demand[k, j] * (
int(not IterX[k, j, 1])) + expr2
for i in plants:
expr1 = expr1 + int(not a_temp[i]) * I[k, i]
temp_expr = expr1 <= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
constraintType.append('big')
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
else:
iterValue_big.append(np.nan)
time_end = time.clock()
if master_problem.status == 2:
open_DC = sum([Z[i].x for i in plants])
I_return = [[I[k, i].x for i in plants] for k in items]
Z_return = [Z[i].x for i in plants]
p = [sum(I[k, i].x for i in plants) for k in items]
return master_problem, constraintType, iterValue, iterValue_small, iterValue_big, [
s, open_DC, I_return, Z_return, p, p / TI, master_problem.ObjVal,
time_end - time_start, add_constraints_time, 'optimal'
]
elif master_problem.status == 3:
return master_problem, constraintType, iterValue, iterValue_small, iterValue_big, [
'-', '-', '-', '-', '-', '-', '-', '-', '-', 'infeasible'
]
def small_big_fulldesign(cost,
demand,
a,
num_plants,
num_demand,
s,
S,
weight,
service_level=0.1):
'''
把exponential多的约束全部加入到模型中，解一个大模型
'''
fixed_cost = np.asarray(cost[0])
holding_cost = np.asarray(cost[1])
lower_demand = demand[0]
mean_demand = demand[1]
upper_demand = demand[2]
inventory = range(num_plants)
plants = range(num_plants)
demand = range(num_demand)
#------------申明主问题-----------#
#申明主模型
master_problem = grb.Model('master_problem')
master_problem.setParam('OutputFlag', 0)
master_problem.modelSense = grb.GRB.MINIMIZE
#申明决策变量 obj=***就表明了该组变量在目标函数中的系数
####这一块参考gurobi给的例子：facility.py
I = master_problem.addVars(inventory,
vtype=grb.GRB.CONTINUOUS,
obj=holding_cost,
name='I')
Z = master_problem.addVars(plants,
vtype=grb.GRB.BINARY,
obj=fixed_cost,
name='Z')
#----------载入各种约束-----------#
#额外算的值
temp_sum = sum((upper_demand - lower_demand)**2)
TI = math.ceil(mean_demand.sum() +
math.sqrt(-math.log(service_level) * temp_sum / 2))
del temp_sum
#载入chance constraints
#至少有 demand个约束（至少每个需求点的需求，都能被和它相连的supplier满足吧）
#这里是small set的约束
for k in range(1, s + 1):
for nodeSet in itertools.combinations(range(num_demand), k):
expr = sum((a[:, nodeSet].sum(axis=1) >= 1) * I.values())
master_problem.addConstr(expr >= sum(upper_demand[j]
for j in nodeSet))
del expr
print('Small=={},约束添加完成'.format(k))
#这里是big set的约束
for k in range(S + 1, num_demand + 1):
for nodeSet in itertools.combinations(range(num_demand), k):
nonNodeSet = [j for j in range(num_demand) if j not in nodeSet]
nonAdjacentNodeSet = np.where(a[:, nodeSet].sum(axis=1) == 0)[0]
expr_I = sum(I[i] for i in nonAdjacentNodeSet)
expr_DL = sum(lower_demand[j] for j in nonNodeSet)
if nonAdjacentNodeSet.size != 0: #if nonAdjacentNodeSet.size==0, expr_i==0,下面的等式始终成立
master_problem.addConstr(expr_I <= expr_DL)
del expr_I, expr_DL
print('Big=={},约束添加完成'.format(k))
#载入约束3-----Ii<=M*Zi
M = upper_demand.sum()
master_problem.addConstrs(I[i] <= M * Z[i] for i in plants)
#求解
master_problem.optimize()
return master_problem, {
'# of Open DC': sum(Z.values()),
'FSC': sum(Z.values() * fixed_cost) + sum(I.values() * holding_cost),
'Inventory': I.values(),
'Total Inventory': sum(I.values())
}
def small_big_design(cost,
demand,
a,
num_plants,
num_demand,
s,
S,
weight,
service_level=0.1,
mode='set'):
if mode == 'set':
add_constraints_time = 0
fixed_cost = cost[0]
holding_cost = cost[1]
lower_demand = demand[0]
mean_demand = demand[1]
upper_demand = demand[2]
inventory = range(num_plants)
plants = range(num_plants)
demand = range(num_demand)
constraintType = []
iterValue = []
iterValue_small = []
iterValue_big = []
#------------申明主问题-----------#
#申明主模型
master_problem = grb.Model('master_problem')
master_problem.setParam('OutputFlag', 0)
master_problem.modelSense = grb.GRB.MINIMIZE
#申明决策变量 obj=***就表明了该组变量在目标函数中的系数
####这一块参考gurobi给的例子：facility.py
I = master_problem.addVars(inventory,
vtype=grb.GRB.CONTINUOUS,
obj=holding_cost,
name='I')
Z = master_problem.addVars(plants,
vtype=grb.GRB.BINARY,
obj=fixed_cost,
name='Z')
#----------载入各种约束-----------#
#额外算的值
temp_sum = sum((upper_demand - lower_demand)**2)
TI = math.ceil(mean_demand.sum() +
math.sqrt(-math.log(service_level) * temp_sum / 2))
del temp_sum
#载入chance constraints
#至少有 demand个约束（至少每个需求点的需求，都能被和它相连的supplier满足吧）
#这里是small set的约束
for j in demand:
expr = sum(a[:, j] * I.values())
master_problem.addConstr(expr >= upper_demand[j])
del expr
constraintType.append('small')
#这里是big set的约束
#要求|S|>25 （某数），最直观的的是取30时候，此时不属于这个集合的点是空集
#所以没有约束。
#第二个的约束去子问题中迭代加入
expr = 0
del expr
#载入约束3-----Ii<=M*Zi
M = upper_demand.sum()
master_problem.addConstrs(I[i] <= M * Z[i] for i in plants)
for i in plants:
constraintType.append('others')
#------------------开始迭代主问题------------------------------------#
#-------------------------------------------------------------------#
time_start = time.clock()
while True:
#解主问题
master_problem.optimize()
#确定主问题解的状态
if master_problem.status == 3:
iterValue.append(master_problem.ObjVal)
break
elif master_problem.status == 2:
print('master_Value:', master_problem.ObjVal)
iterValue.append(master_problem.ObjVal)
#-------------开始子问题---------------#
#申明small set的子问题----sub_problem1
sub_problem1 = grb.Model('sub_problem1')
sub_problem1.setParam('OutputFlag', 0)
#申明子问题的变量
x1 = sub_problem1.addVars(demand,
vtype=grb.GRB.BINARY,
name='x1')
y1 = sub_problem1.addVars(plants,
vtype=grb.GRB.BINARY,
name='y1')
#设置目标函数
expr1 = grb.quicksum([y1[i] * I[i].x for i in plants])
expr2 = sum(upper_demand * x1.values())
#for j in demand:
# expr2 = expr2 + upper_demand[j] * x1[j]
sub_problem1.setObjective(expr1 - expr2, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
expr = sum(x1.values())
sub_problem1.addConstr(expr <= s)
del expr
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem1.addConstr(y1[i] >= x1[j])
#解子问题
sub_problem1.optimize()
#申明big set的子问题----sub_problem2 -----------
#big set
#big set
#big set
sub_problem2 = grb.Model('sub_problem1')
sub_problem2.setParam('OutputFlag', 0)
#申明子问题的变量
x2 = sub_problem2.addVars(demand,
vtype=grb.GRB.BINARY,
name='x2')
y2 = sub_problem2.addVars(plants,
vtype=grb.GRB.BINARY,
name='y2')
#设置目标函数
expr1 = grb.quicksum([(1 - y2[i]) * I[i].x for i in plants])
expr2 = grb.quicksum(
[lower_demand[j] * (1 - x2[j]) for j in demand])
sub_problem2.setObjective(expr2 - expr1, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
expr = grb.quicksum(x2.values())
sub_problem2.addConstr(expr >= S)
del expr
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem2.addConstr(y2[i] >= x2[j])
#解子问题
sub_problem2.optimize()
#判断子问题的目标函数值是否为非负数，若是，则主问题得到了最优解,break；否则加入新的约束到主问题中
if sub_problem1.ObjVal >= -0.01 and sub_problem2.ObjVal >= -0.01:
iterValue_small.append(sub_problem1.ObjVal)
iterValue_big.append(sub_problem2.ObjVal)
break
else:
if sub_problem1.ObjVal < -0.001:
print('small_set:', sub_problem1.ObjVal)
iterValue_small.append(sub_problem1.ObjVal)
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = x1[j].x * a[:, j] + a_temp
expr2 = upper_demand[j] * x1[j].x + expr2
for i in plants:
if round(a_temp[i]) != 0:
expr1 = expr1 + round(a_temp[i]) / round(
a_temp[i]) * I[i]
temp_expr = expr1 >= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
constraintType.append('small')
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
else:
iterValue_small.append(np.nan)
if sub_problem2.ObjVal < -0.001:
print('big_set:', sub_problem2.ObjVal)
iterValue_big.append(sub_problem2.ObjVal)
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = x2[j].x * a[:, j] + a_temp
expr2 = lower_demand[j] * (
int(not x2[j].x)) + expr2
for i in plants:
expr1 = expr1 + int(not a_temp[i]) * I[i]
temp_expr = expr1 <= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
constraintType.append('big')
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
else:
iterValue_big.append(np.nan)
time_end = time.clock()
if master_problem.status == 2:
I_return = []
Z_return = []
p = 0
open_DC = 0
for i in plants:
open_DC = open_DC + Z[i].x
p = p + I[i].x
I_return.append(I[i].x)
Z_return.append(Z[i].x)
return master_problem, constraintType, iterValue, iterValue_small, iterValue_big, [
s, open_DC, I_return, Z_return, p, p / TI,
master_problem.ObjVal, time_end - time_start,
add_constraints_time, 'optimal'
]
elif master_problem.status == 3:
return master_problem, constraintType, iterValue, iterValue_small, iterValue_big, [
'-', '-', '-', '-', '-', '-', '-', '-', '-', 'infeasible'
]
if mode == 'weight':
add_constraints_time = 0
fixed_cost = cost[0]
holding_cost = cost[1]
lower_demand = demand[0]
mean_demand = demand[1]
upper_demand = demand[2]
inventory = range(num_plants)
plants = range(num_plants)
demand = range(num_demand)
#------------申明主问题-----------#
#申明主模型
master_problem = grb.Model('master_problem')
master_problem.setParam('OutputFlag', 0)
master_problem.modelSense = grb.GRB.MINIMIZE
#申明决策变量 obj=***就表明了该组变量在目标函数中的系数
####这一块参考gurobi给的例子：facility.py
I = master_problem.addVars(inventory,
vtype=grb.GRB.INTEGER,
obj=holding_cost,
name='I')
Z = master_problem.addVars(plants,
vtype=grb.GRB.BINARY,
obj=fixed_cost,
name='Z')
#----------载入各种约束-----------#
#额外算的值
temp_sum = 0
for j in demand:
temp_sum += (upper_demand[j] -
lower_demand[j]) * (upper_demand[j] - lower_demand[j])
TI = math.ceil(mean_demand.sum() +
math.sqrt(-math.log(service_level) * temp_sum / 2))
del temp_sum
#载入chance constraints
#没有约束。
#第二个的约束去子问题中迭代加入
expr = 0
del expr
#载入约束3-----Ii<=M*Zi
M = upper_demand.sum()
master_problem.addConstrs(I[i] <= M * Z[i] for i in plants)
#------------------开始迭代主问题------------------------------------#
#-------------------------------------------------------------------#
time_start = time.clock()
while True:
#解主问题
master_problem.optimize()
#确定主问题解的状态
if master_problem.status == 3:
break
elif master_problem.status == 2:
#-------------开始子问题---------------#
#申明small set的子问题----sub_problem1
sub_problem1 = grb.Model('sub_problem1')
sub_problem1.setParam('OutputFlag', 0)
#申明子问题的变量
x1 = sub_problem1.addVars(demand,
vtype=grb.GRB.BINARY,
name='x1')
y1 = sub_problem1.addVars(plants,
vtype=grb.GRB.BINARY,
name='y1')
#设置目标函数
expr1 = 0
expr2 = 0
for i in plants:
expr1 = expr1 + y1[i] * I[i].x
for j in demand:
expr2 = expr2 + upper_demand[j] * x1[j]
sub_problem1.setObjective(expr1 - expr2, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
sub_problem1.addConstr(
grb.quicksum(x1[j] * upper_demand[j]
for j in demand) <= weight * M)
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem1.addConstr(y1[i] >= x1[j])
#解子问题
sub_problem1.optimize()
#申明big set的子问题----sub_problem2 -----------
#big set
#big set
#big set
sub_problem2 = grb.Model('sub_problem1')
sub_problem2.setParam('OutputFlag', 0)
#申明子问题的变量
x2 = sub_problem2.addVars(demand,
vtype=grb.GRB.BINARY,
name='x2')
y2 = sub_problem2.addVars(plants,
vtype=grb.GRB.BINARY,
name='y2')
#设置目标函数
expr1 = 0
expr2 = 0
for i in plants:
expr1 = expr1 + (1 - y2[i]) * I[i].x
for j in demand:
expr2 = expr2 + lower_demand[j] * (1 - x2[j])
sub_problem2.setObjective(expr2 - expr1, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
sub_problem2.addConstr(
grb.quicksum(x1[j] * lower_demand[j]
for j in demand) >= (1 - weight) * M)
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem2.addConstr(y2[i] >= x2[j])
#解子问题
sub_problem2.optimize()
#判断子问题的目标函数值是否为非负数，若是，则主问题得到了最优解,break；否则加入新的约束到主问题中
if sub_problem1.ObjVal >= 0 and sub_problem2.ObjVal >= 0:
break
else:
if sub_problem1.ObjVal < -0.0001:
print('small_set:', sub_problem1.ObjVal)
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = x1[j].x * a[:, j] + a_temp
expr2 = upper_demand[j] * x1[j].x + expr2
for i in plants:
if round(a_temp[i]) != 0:
expr1 = expr1 + round(a_temp[i]) / round(
a_temp[i]) * I[i]
temp_expr = expr1 >= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
if sub_problem2.ObjVal < -0.0001:
print('big_set:', sub_problem2.ObjVal)
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = x2[j].x * a[:, j] + a_temp
expr2 = lower_demand[j] * (
int(not x2[j].x)) + expr2
for i in plants:
expr1 = expr1 + int(not a_temp[i]) * I[i]
temp_expr = expr1 <= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
time_end = time.clock()
if master_problem.status == 2:
I_return = []
Z_return = []
p = 0
open_DC = 0
for i in plants:
open_DC = open_DC + Z[i].x
p = p + I[i].x
I_return.append(I[i].x)
Z_return.append(Z[i].x)
return s, open_DC, I_return, Z_return, p, p / TI, master_problem.ObjVal, time_end - time_start, add_constraints_time, 'optimal'
elif master_problem.status == 3:
return '-', '-', '-', '-', '-', '-', '-', '-', '-', 'infeasible'
if mode == 'both':
add_constraints_time = 0
fixed_cost = cost[0]
holding_cost = cost[1]
lower_demand = demand[0]
mean_demand = demand[1]
upper_demand = demand[2]
inventory = range(num_plants)
plants = range(num_plants)
demand = range(num_demand)
#------------申明主问题-----------#
#申明主模型
master_problem = grb.Model('master_problem')
master_problem.setParam('OutputFlag', 0)
master_problem.modelSense = grb.GRB.MINIMIZE
#申明决策变量 obj=***就表明了该组变量在目标函数中的系数
####这一块参考gurobi给的例子：facility.py
I = master_problem.addVars(inventory,
vtype=grb.GRB.INTEGER,
obj=holding_cost,
name='I')
Z = master_problem.addVars(plants,
vtype=grb.GRB.BINARY,
obj=fixed_cost,
name='Z')
#----------载入各种约束-----------#
#额外算的值
temp_sum = 0
for j in demand:
temp_sum += (upper_demand[j] -
lower_demand[j]) * (upper_demand[j] - lower_demand[j])
TI = math.ceil(mean_demand.sum() +
math.sqrt(-math.log(service_level) * temp_sum / 2))
del temp_sum
#载入chance constraints
#至少有 demand个约束（至少每个需求点的需求，都能被和它相连的supplier满足吧）
#这里是small set的约束
for j in demand:
expr = 0
for i in plants:
expr = a[i, j] * I[i] + expr
master_problem.addQConstr(expr >= upper_demand[j])
del expr
#这里是big set的约束
#要求|S|>25 （某数），最直观的的是取30时候，此时不属于这个集合的点是空集
#所以没有约束。
#第二个的约束去子问题中迭代加入
expr = 0
del expr
#载入约束3-----Ii<=M*Zi
M = upper_demand.sum()
master_problem.addConstrs(I[i] <= M * Z[i] for i in plants)
#------------------开始迭代主问题------------------------------------#
#-------------------------------------------------------------------#
time_start = time.clock()
while True:
#解主问题
master_problem.optimize()
#确定主问题解的状态
if master_problem.status == 3:
break
elif master_problem.status == 2:
#-------------开始子问题---------------#
'''
2个和set大小有关的子问题
'''
#申明small set的子问题----sub_problem1
sub_problem1 = grb.Model('sub_problem1')
sub_problem1.setParam('OutputFlag', 0)
#申明子问题的变量
x1 = sub_problem1.addVars(demand,
vtype=grb.GRB.BINARY,
name='x1')
y1 = sub_problem1.addVars(plants,
vtype=grb.GRB.BINARY,
name='y1')
#设置目标函数
expr1 = 0
expr2 = 0
for i in plants:
expr1 = expr1 + y1[i] * I[i].x
for j in demand:
expr2 = expr2 + upper_demand[j] * x1[j]
sub_problem1.setObjective(expr1 - expr2, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
expr = 0
for j in demand:
expr = x1[j] + expr
sub_problem1.addConstr(expr <= s)
del expr
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem1.addConstr(y1[i] >= x1[j])
#解子问题
sub_problem1.optimize()
#申明big set的子问题----sub_problem2 -----------
#big set
#big set
#big set
sub_problem2 = grb.Model('sub_problem2')
sub_problem2.setParam('OutputFlag', 0)
#申明子问题的变量
x2 = sub_problem2.addVars(demand,
vtype=grb.GRB.BINARY,
name='x2')
y2 = sub_problem2.addVars(plants,
vtype=grb.GRB.BINARY,
name='y2')
#设置目标函数
expr1 = 0
expr2 = 0
for i in plants:
expr1 = expr1 + (1 - y2[i]) * I[i].x
for j in demand:
expr2 = expr2 + lower_demand[j] * (1 - x2[j])
sub_problem2.setObjective(expr2 - expr1, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
expr = 0
for j in demand:
expr = x2[j] + expr
sub_problem2.addConstr(expr >= S)
del expr
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem2.addConstr(y2[i] >= x2[j])
#解子问题
sub_problem2.optimize()
'''
2个和weight 有关的子问题
'''
#申明small set的子问题----sub_problem1
sub_problem3 = grb.Model('sub_problem3')
sub_problem3.setParam('OutputFlag', 0)
#申明子问题的变量
x3 = sub_problem3.addVars(demand,
vtype=grb.GRB.BINARY,
name='x3')
y3 = sub_problem3.addVars(plants,
vtype=grb.GRB.BINARY,
name='y3')
#设置目标函数
expr1 = 0
expr2 = 0
for i in plants:
expr1 = expr1 + y3[i] * I[i].x
for j in demand:
expr2 = expr2 + upper_demand[j] * x3[j]
sub_problem3.setObjective(expr1 - expr2, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
sub_problem3.addConstr(
grb.quicksum(x3[j] * upper_demand[j]
for j in demand) <= weight * M)
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem3.addConstr(y3[i] >= x3[j])
#解子问题
sub_problem3.optimize()
#申明big weight的子问题----sub_problem2 -----------
#big set
#big set
#big set
sub_problem4 = grb.Model('sub_problem4')
sub_problem4.setParam('OutputFlag', 0)
#申明子问题的变量
x4 = sub_problem4.addVars(demand,
vtype=grb.GRB.BINARY,
name='x4')
y4 = sub_problem4.addVars(plants,
vtype=grb.GRB.BINARY,
name='y4')
#设置目标函数
expr1 = 0
expr2 = 0
for i in plants:
expr1 = expr1 + (1 - y4[i]) * I[i].x
for j in demand:
expr2 = expr2 + lower_demand[j] * (1 - x4[j])
sub_problem4.setObjective(expr2 - expr1, grb.GRB.MINIMIZE)
del expr1
del expr2
#载入子问题的约束
#子问题约束1
sub_problem4.addConstr(
grb.quicksum(x4[j] * lower_demand[j]
for j in demand) >= (1 - weight) * M)
#子问题约束2，
for i in plants:
for j in demand:
if round(a[i, j]) == 1:
sub_problem4.addConstr(y4[i] >= x4[j])
#解子问题
sub_problem4.optimize()
#判断子问题的目标函数值是否为非负数，若是，则主问题得到了最优解,break；否则加入新的约束到主问题中
if sub_problem1.ObjVal >= 0 and sub_problem2.ObjVal >= 0 and sub_problem3.ObjVal >= 0 and sub_problem4.ObjVal >= 0:
break
else:
if sub_problem1.ObjVal < -0.0001:
print('small_set:', sub_problem1.ObjVal)
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = x1[j].x * a[:, j] + a_temp
expr2 = upper_demand[j] * x1[j].x + expr2
for i in plants:
if round(a_temp[i]) != 0:
expr1 = expr1 + round(a_temp[i]) / round(
a_temp[i]) * I[i]
temp_expr = expr1 >= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
if sub_problem2.ObjVal < -0.0001:
print('big_set:', sub_problem2.ObjVal)
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = x2[j].x * a[:, j] + a_temp
expr2 = lower_demand[j] * (
int(not x2[j].x)) + expr2
for i in plants:
expr1 = expr1 + int(not a_temp[i]) * I[i]
temp_expr = expr1 <= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
if sub_problem3.ObjVal < -0.0001:
print('small_set:', sub_problem3.ObjVal)
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = x3[j].x * a[:, j] + a_temp
expr2 = upper_demand[j] * x3[j].x + expr2
for i in plants:
if round(a_temp[i]) != 0:
expr1 = expr1 + round(a_temp[i]) / round(
a_temp[i]) * I[i]
temp_expr = expr1 >= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
if sub_problem4.ObjVal < -0.0001:
print('big_set:', sub_problem4.ObjVal)
expr1 = 0
expr2 = 0
a_temp = [0] * num_plants
for j in demand:
a_temp = x4[j].x * a[:, j] + a_temp
expr2 = lower_demand[j] * (
int(not x4[j].x)) + expr2
for i in plants:
expr1 = expr1 + int(not a_temp[i]) * I[i]
temp_expr = expr1 <= expr2
master_problem.addConstr(temp_expr) #加新的约束到主问题中
del expr1
del expr2
del temp_expr
del a_temp
add_constraints_time += 1
time_end = time.clock()
if master_problem.status == 2:
I_return = []
Z_return = []
p = 0
open_DC = 0
for i in plants:
open_DC = open_DC + Z[i].x
p = p + I[i].x
I_return.append(I[i].x)
Z_return.append(Z[i].x)
return master_problem, constraintType, iterValue, [
s, open_DC, I_return, Z_return, p, p / TI,
master_problem.ObjVal, time_end - time_start,
add_constraints_time, 'optimal'
]
elif master_problem.status == 3:
return master_problem, constraintType, iterValue, [
'-', '-', '-', '-', '-', '-', '-', '-', '-', 'infeasible'
]