Question

我是混合整数优化问题的新手。目前，我正在使用带有默认CBC求解器的纸浆python接口来解决该问题。问题是要提高癌症诊所模型中的资源利用率，以下是具有目标功能和约束的代码。当我使用prob.solve（）时，我有3个不同的问题： 1.我为BeginTreatment变量获得了值1.0，但是没有为ContinueTreatment变量获得了值1.0？ 2.根据主席的连续性约束，不应将编号大于29的插槽分配给pat_type 8，因为最多只能有40个插槽。但是我仍然看到吗？（不仅对于pat_type为8，其他类型也是如此） 3.是否应该尝试使用其他求解器而不是默认的纸浆CBC求解器？如果是，该怎么办？

import pulp
# Indices and Parameters
chair = list(range(1,24))
pat_type = list(range(1,9))
slot = list(range(1,41))
type_and_demand = {1:24,2:10,3:13,4:9,5:7,6:6,7:2,8:1}
type_and_slots = {1:1,2:4,3:8,4:12,5:16,6:20,7:24,8:28}
# Decision Variables
Y = pulp.LpVariable.dicts("BeginTreatment", 
    (chair,pat_type,slot),0,1,pulp.LpBinary)
X = pulp.LpVariable.dicts("ContinueTreatment", 
    (chair,pat_type,slot),0,1,pulp.LpBinary)
# Objective Function
prob = pulp.LpProblem("ChairUtilization", pulp.LpMaximize)
prob += pulp.lpSum([Y[i][j][t] for i in chair for j in pat_type for t in 
        slot]) 
# Constraints
# Patient Type 1 Continuity constraint
for i in chair:
 for t in slot:
   for j in range(1,2):
        prob += X[i][j][t] == 0
# Chair Continuity Constraint
for i in chair:
 for j in range(2,9):
   for t in range(1,(len(slot)-type_and_slots[j]+1)+1):
    prob += pulp.lpSum([X[i][j][u] for u in range(t+1,t+type_and_slots[j])]) 
            == (type_and_slots[j] - 1)*Y[i][j][t]
# No more than one patient per chair
for t in slot:
 for i in chair:
    prob += pulp.lpSum([X[i][j][t] for j in pat_type]) + pulp.lpSum([Y[i][j] 
            [t] for j in pat_type]) <= 1
 # No new arrivals during lunch time period
 prob += pulp.lpSum([Y[i][j][t] for i in chair for j in pat_type for t in 
         range(19,23)]) == 0
# Patient Mix
for j in pat_type:
 prob += pulp.lpSum([Y[i][j][t] for i in chair for t in slot]) == 
         type_and_demand[j]
prob.solve()

Python混合整数优化

0 个答案: