I am currently working on a demo problem where the network is : Plant -- Warehouse -- Customer . I need to find out the optimal number of warehouses which are required for cost minimization . I have transportation cost and handling cost of Plants and warehouses . I also have demand of customers . I have already solved using Mixed Integer Programming ,but want to reduce the time of run .Can anyone please help me out with the approach with Simulated Annealing or Genetic Algorithm for this problem .Thanking in advance .
With regards, 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, 3:17, 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)