poisson_instance.py

import random
import numpy as np

# Total_Machine=[10,20,30,40,50]  
# Initial_Job_num=20              
# Job_insert=[50,100,200]         #
# DDT=[0.5,1.0,1.5]               
# E_ave=[50,100,200]              

def Instance_Generator(M_num,E_ave,New_insert,DDT):
    '''
    :param M_num: Machine Number
    :param E_ave: exponetional distribution
    :param New_insert: New Job insert
    :param DDT:DDT
    :return: Processing time,A:New Job arrive time,
                                D:Deliver time,
                                M_num: Machine Number,
                                Op_num: Operation Number,
                                J_num:Job NUMBER
    '''
    Initial_Job_num=5
    Op_num=[random.randint(1,20) for i in range(New_insert+Initial_Job_num)]
    Processing_time=[]
    for i in range(Initial_Job_num+New_insert):
        Job_i=[]
        for j in range(Op_num[i]):
            k=random.randint(1,M_num-2)
            T=list(range(M_num))
            random.shuffle(T)
            T=T[0:k+1]
            O_i = list(np.ones(M_num) * (-1))
            for M_i in range(len(O_i)):
                if M_i in T:
                    O_i[M_i]=random.randint(1,50)
            Job_i.append(O_i)
        Processing_time.append(Job_i)
    A1=[0 for i in range(Initial_Job_num)]
    A=np.random.exponential(E_ave, size=New_insert)
    A=[int(A[i]) for i in range(len(A))]#New Insert Job arrive time
    A1.extend(A)
    T_ijave = []
    for i in range(Initial_Job_num+New_insert):
        Tad = []
        for j in range(Op_num[i]):
            T_ijk = [k for k in Processing_time[i][j] if k != -1]
            Tad.append(sum(T_ijk) / len(T_ijk))
        T_ijave.append(sum(Tad))
    D1=[int(T_ijave[i]*DDT) for i in range(Initial_Job_num)]
    D=[int(A1[i]+T_ijave[i]*DDT) for i in range(Initial_Job_num,Initial_Job_num+New_insert)]
    D1.extend(D)
    O_num=sum(Op_num)
    J=dict(enumerate(Op_num))
    J_num=Initial_Job_num+New_insert

    return Processing_time,A1,D1,M_num,Op_num,J,O_num,J_num

# random.seed(30)
# np.random.seed(30)
# machine=8
# e_ave=50
# New_insert=20
# DDT=1.5
# Processing_time, A, D, M_num, Op_num, J, O_num, J_num = Instance_Generator(machine, e_ave, New_insert, DDT)
# print('Processing_time:',Processing_time)
# print('A:',A)
# print('D:',D)
# print('M_num:',M_num)
# print('Op_num:',Op_num)
# print('J:',J)
# print('O_num:',O_num)
# print('J_num:',J_num)