我目前正在研究一个演示问题，该问题的网络是：工厂-仓库-客户。我需要找出最小化成本所需的最佳仓库数量。我有工厂和仓库的运输费用和搬运费用。我也有客户的需求。 我已经使用混合整数编程解决了问题，但想减少运行时间。对于这个问题，有人可以用模拟退火或遗传算法的方法来帮助我。请提前感谢。

关于， Shourya

PLANTS=['P1','P2']
FACILITIES = ['A','B','C','D']
CUSTOMERS  = range(1,10)

# Capacity of a facility at each site
CAPACITY = dict(A=0,B=0,C=0,D=0)
CAPACITY_P=dict(P1=100,P2=200)

# Demand from each customer
DEMAND = {1:10,2:14,3:17,4:8,5:9,6:12,7:11,8:15,9:16}
DEMAND_W = {'A': 0,'B':0,'C':0,'D':0}

# Transportation cost from each facility to each customer
C = dict(A = {1:1000000,2: 4,4:33,5:47,6:98,7:19,8:10,9: 6},B = {1:2,2:12,3: 4,4:23,5:16,6:78,7:47,8: 9,9:82},C = {1:17,2:34,3:65,4:25,5: 7,6:67,7:45,8:13,9:54},D = {1:60,2: 8,3:79,4:24,5:28,6:19,7:62,8:18,9:45}
)

# Transportation cost from each plant to each facility 
C_P = dict(
        P1= {'A':50,'B':1,'C':5,'D': 30},P2 = {'A':35,'B':70,'C':45,'D':100})
#Handling cost 
H_COST = dict(A=500,B=600,C=700,D=800,P1=1000,P2= 4000)