[人工智能]车间调度问题简单，Python实现

车间调度问题，Python实现

学习https://www.bilibili.com/video/BV1Wy4y1H74p?spm_id_from=333.999.0.0，使用Python实现标准算例车间作业的编码、作业的简单分配以及简单的甘特图的绘制功能。
中间由于自己太菜遇到很多问题，需要学习的东西还有很多很多。

源文件:https://download.csdn.net/download/fwx1111/23404436

//2021.09.17
import numpy as np
import random
import pandas as pd
import matplotlib.pyplot as plt


#num_job = 3 # 工件数目
#num_machine = 4  #机器数目
pop = 6

data1 = [
    [2,1,0,3,1,6,3,7,5,3,4,6],
    [1,8,2,5,4,10,5,10,0,10,3,4],
    [2,5,3,4,5,8,0,9,1,1,4,7],
    [1,5,0,5,2,5,3,3,4,8,5,9],
    [2,9,1,3,4,5,5,4,0,3,3,1],
    [1,3,3,3,5,9,0,10,4,4,2,1],
]
data = np.array(data1)

#class Encode():

    # 生成编码，传入的参数为num_job, num_machine
def createChrome(num_job, num_machine):
    a = []
    for i in range(1,num_job+1):
        a.append(i)

    chrome = []
    for j in range(num_machine):
        chrome += a                   # 生成初始的编码
    random.shuffle(chrome)            # 将编码乱序排列，生成作业码的乱序
    return chrome

    # 根据种群数量和作业数量和设备数量生成初始种群
def createChromes(num_job, num_machine, pop):
    chromes = np.zeros((pop, num_job * num_machine),dtype=int)
    for i in range(pop):
        chromes[i,:] = createChrome(num_job,num_machine)
    return chromes

# 初始化工时设备数据
def initData(data):
    for i in range(0,11,2):
        data[:,i] = data[:,i]+1
    return data


def createSchedule(data,chrome):
    num_job = np.size(data,0)
    num_machine = np.size(data,1) // 2
    schedule = np.zeros((num_job * num_machine,5))

    # 定义中间数组
    jobCanStartTime = np.zeros((1, num_job),dtype=int)
    jobProcessId = np.ones((1, num_job),dtype=int)

    
    # 获取编码中当前的作业号
    chrome = createChrome(num_job,num_machine)
    data = initData(data)
    for i in range(num_job * num_machine):
        nowJobId = chrome[i]-1
        nowProcessId = jobProcessId[0,nowJobId]
        nowMachId = data[nowJobId, 2*nowProcessId-2]
        nowProcTime = data[nowJobId, 2*nowProcessId -1]
        
        machSch = schedule[schedule[:,1]==nowMachId,:]
        jobCanST = jobCanStartTime[0,nowJobId]
        if np.size(machSch,0) == 0:    # 该工件还未安排作业
            startTime = jobCanStartTime[0,nowJobId]
            endTime = startTime + nowProcTime
        else:   # 设备已安排了工作
            machSch = machSch[np.argsort(machSch[:,3]),:]
            rows = np.size(machSch,0)
            # 处理第一行已排作业，检查是否能将当前作业排到之前
            done = 0
            if jobCanST < machSch[0,3]:
                if machSch[0,3] - jobCanST > nowProcTime:
                    startTime = jobCanST
                    endTime = startTime + nowProcTime
                    done = 1
            if done == 0 :

                for j in range(rows):
                    if jobCanStartTime[0,nowJobId] < machSch[j,3]:
                        if machSch[j,3] - max(jobCanST,machSch[j-1,4]) > nowProcTime:
                            startTime = max(jobCanST,machSch[j-1,4])
                            endTime = startTime + nowProcTime
                            done = 1 
                            break
            if done ==0:  # 表示该作业不能排到该设备已有作业之前
                startTime = max(jobCanST,machSch[rows-1,4])
                endTime = startTime + nowProcTime
                





        schedule[i,0] = nowJobId + 1 
        schedule[i,1] = nowMachId
        schedule[i,2] = nowProcessId
        schedule[i,3] = startTime
        schedule[i,4] = endTime
        jobCanStartTime[0,nowJobId] = endTime
        jobProcessId[0,nowJobId] += 1 


    return schedule


# 根据调度方案绘制甘特图
def drawGant(schedule):
    rows = np.size(schedule,0)
    num_job = int(max(schedule[:,1]))
    mycolor = np.random.random((num_job,3))
    mycolor = list(mycolor)

    for i in range(rows):
        x = [schedule[i, 3],schedule[i,4]]
        y = [schedule[i,1],schedule[i,1]]
        n = int(schedule[i,0])
        plt.plot(x,y,linewidth=8.0,color=mycolor[n-1] )
    plt.show()




e=createChromes(6,6,6)


s = createSchedule(data,e)
print(s)

d = drawGant(s)