用python实现基于遗传算法求解带AGV的作业车间调度问题

Posted 2022-12-02 码丽莲梦露

1 项目描述

针对带AGV的作业车间调度问题，以最小化完工时间为目标，考虑AGV在装载站、机器、卸载站之间的有效负载时间和空载时间，采用遗传算法进行求解，设计了面向工件运输次数的一维编码，和面向工件运输的驱动解码，以此来联动工件排序和AGV指派两个调度子问题，达到接近最优解的效果。算法采用Bilge和Ulusoy等人设计的40个算例进行验证。

算例下载可前往个人CSDN：机器人作业车间的算例JSP_Transbot.zip-制造文档类资源-CSDN文库进行下载。

 2 问题描述

带AGV的作业车间调度问题可描述为：n个工件在m台机器上加工，w台AGV负责工件在机器之间的搬运；每个工件至少有一道工序，每道工序在加工完成后由AGV搬运至下一台机器上进行加工。

对此类问题做如下假设：

（1）机器旁出入缓冲区容量无限大；

（2）所有工件和AGV起始位置和加工完成回到的位置都在L/U站；

（3）不考虑AGV运输过程中的碰撞和故障；

3 算法简述

3.1 编码

编码形式如下，染色体长度为各工序搬运次数的总和，其中VT11表示工件1由L/U站搬运至第一道工序加工处，VT1E表示工序加工完最后一道工序返回L/U站。

3.2 解码

解码采用驱动的方式+间隙解码方法，间隙解码可参考北京科技大学 吴秀丽的某些论文中有提到，贪婪解码的另一种表述。

怎么一个驱动方式呢？

那就是如果这个工件分配了运输任务，那么在运输至下一工序加工机器前，当前工序必须加工完成，以此思想，便可在搬运前驱动该工序加工完成。

3.3 关于AGV的分配

此处由于偷了个小懒，AGV的分配采用随机分配的方式，或者采用最早可用的AGV作为当前搬运工序使用的AGV，这也是算法在某些算例中会陷入局部最优的情形，感兴趣的可加入一维关于AGV的编码。

4 部分算例展示

EX53的算例甘特图如下：

5 部分代码展示

5.1 GA.py

import random
import numpy as np
import os
from utils import RJSP_Env
from Gantt_graph import Gantt
import time
import pickle

class GA:
    def __init__(self, args):
        # self.Shop_Floor,self.args=RFSP_Env(args)
        self.Shop_Floor, self.args = RJSP_Env(args)
        self.Pm = 0.2
        self.Pc = 0.9
        self.Pop_size =200

    # 随机产生染色体
    def RCH(self):
        Chromo1=[]
        for i in range(len(self.args.MT)):
            Chromo1.extend([i for _ in range(len(self.args.MT[i])-1)])
        random.shuffle(Chromo1)
        self.args.Chromo_len=len(Chromo1)
        Chromo2=[]
        # print(Chromo1)

        return Chromo1

    # 生成初始种群
    def CHS(self):
        CHS = []
        for i in range(self.Pop_size):
            CHS.append(self.RCH())
        return CHS

    # 选择
    def Select(self, Fit_value):
        Fit = []
        for i in range(len(Fit_value)):
            fit = 1 / Fit_value[i]
            Fit.append(fit)
        Fit = np.array(Fit)
        idx = np.random.choice(np.arange(len(Fit_value)), size=len(Fit_value), replace=True,
                               p=(Fit) / (Fit.sum()))
        return idx

    # 交叉
    def Crossover(self, CHS1, CHS2):
        Job_list = [i for i in range(self.args.n)]
        random.shuffle(Job_list)
        r = random.randint(1, self.args.n - 1)
        Set1 = Job_list[0:r]
        new_CHS1 = list(np.zeros(self.args.Chromo_len, dtype=int))
        new_CHS2 = list(np.zeros(self.args.Chromo_len, dtype=int))
        for k, v in enumerate(CHS1):
            if v in Set1:
                new_CHS1[k] = v+1
        for i in CHS2:
            if i not in Set1:
                Site = new_CHS1.index(0)
                new_CHS1[Site] = i + 1

        for k, v in enumerate(CHS2):
            if v not in Set1:
                new_CHS2[k] = v + 1
        for i in CHS2:
            if i  in Set1:
                Site = new_CHS2.index(0)
                new_CHS2[Site] = i + 1

        new_CHS1= np.array([j - 1 for j in new_CHS1])
        new_CHS2 = np.array([j - 1 for j in new_CHS2])
        return  new_CHS1,new_CHS2

    # 变异
    def Mutation(self, CHS):
        Tr = [i_num for i_num in range(self.args.Chromo_len)]
        # 机器选择部分
        r = random.randint(1, self.args.Chromo_len)  # 在变异染色体中选择r个位置
        random.shuffle(Tr)
        T_r = Tr[0:r]
        K = []
        for i in T_r:
            K.append(CHS[i])
        random.shuffle(K)
        k = 0
        for i in T_r:
            CHS[i] = K[k]
            k += 1
        return CHS

    #解码
    def decode(self,chs,):
        self.Shop_Floor.reset()
        k = 0
        for i in chs:
            agv=random.choice([0,1])        #这里目前写的随机，所以得到的结果不是很好，可改成两层编码
            K = self.Shop_Floor.scheduling(i,agv)
            # Gantt(self.Shop_Floor.Machines, self.Shop_Floor.AGVs)
            k += 1
            # if done:
            #     break
        return self.Shop_Floor.C_max()

    def main(self,target_C=None,file=None):
        start=time.time()
        BF = []
        C = self.CHS()
        Fit = []
        for C_i in C:
            Fit.append(self.decode(C_i))
        best_C = None
        best_fit = min(Fit)
        BF.append(best_fit)
        for i in range(100):
            C_id = self.Select(Fit)
            C = [C[_] for _ in C_id]
            for Ci in range(len(C)):
                if random.random() < self.Pc:
                    _C = [C[Ci]]
                    CHS1, CHS2 = self.Crossover(C[Ci], random.choice(C))
                    _C.extend([CHS1, CHS2])
                    Fi = []
                    for ic in _C:
                        Fi.append(self.decode(ic))
                    C[Ci] = _C[Fi.index(min(Fi))]
                    Fit.append(min(Fi))
                elif random.random() < self.Pm:
                    CHS1 = self.Mutation(C[Ci])
                    C[Ci] = CHS1
            Fit = []
            for C_i in C:
                fi=self.decode(C_i)
                Fit.append(fi)
                # Gantt(self.Shop_Floor.Machines, self.Shop_Floor.AGVs)
                if fi<=97:
                    target_C=fi
                    Gantt(self.Shop_Floor.Machines, self.Shop_Floor.AGVs)
            if min(Fit) < best_fit:
                best_fit = min(Fit)
                best_C = C[Fit.index(min(Fit))]
                # print(best_C)
                # Gantt(self.Shop_Floor.Machines, self.Shop_Floor.AGVs)
            BF.append(best_fit)
            # print('迭代次数：',i+1,'完工时间：',best_fit)
        x = [_ for _ in range(len(BF))]
        # plt.plot(x, BF)
        # plt.show()
        # best_C.Gantt()
        end=time.time()
        T=end-start
        print('运行时间--->>>>',T)
        return  best_C,best_fit

if __name__ == "__main__":
    import argparse
    from Instance.Text_extract import data, data_for_storer
    from Instance.data_extract import change
    import numpy as np
    K=["11",'21','31','41','51','61','71','81','91','101',"12",'22','32','42','52','62','72','82','92','102',
       "13",'23','33','43','53','63','73','83','93','103',"14",'24','34','44','54','64','74','84','94','104']
    for fi in K:
        f='.\\Instance\\Bilge_Ulusoy\\C1\\E'+str(53)+'.pkl'
        print(f)
        n, m, PT, agv_trans, MT, agv_num = data(f)
        # n, m, PT, MT=change('la',1)
        # agv_trans=[]
        # print(agv_trans)
        agv_num = 2


        parser = argparse.ArgumentParser("Reinforcement Learning experiments for multiagent environments")
        # TWO_AGVs_FS_ENV
        parser.add_argument("--n", type=int, default=n, help="Job number")
        parser.add_argument("--m", type=int, default=m, help="Machine number")
        parser.add_argument("--PT", type=list, default=PT, help="PROCESSING TIME OF JOB")
        parser.add_argument("--MT", type=list, default=MT, help="PROCESSING TIME OF JOB")
        parser.add_argument("--AGV_num", type=int, default=2, help="transportation time between machines")
        parser.add_argument("--agv_trans", type=list, default=agv_trans, help="transportation time between machines")
        args = parser.parse_args()
        print('测试算例:EX',fi)
        for i in range(10):
            # print( PT,  MT)
            g = GA(args)
            best_C,best_fit=g.main(96)
            print(best_fit)
            # print(g.RCH())
            # g.decode([4, 4 ,0 ,3, 4, 0, 0, 1, 1, 1, 1, 2, 2, 0, 3, 3, 2, 2])
            # Gantt(g.Shop_Floor.Machines,g.Shop_Floor.AGVs)

 5.2 JS_ENV.py

from Flow_Shop_Environment.FS_Env import RFS_Env
from Job_Shop_Environment.JS_Job import Job
import copy


class JS_Env(RFS_Env):
    def __init__(self,args):
        super().__init__(args)
        self.MT=args.MT

    def Create_Job(self):
        self.Jobs = []
        for k in range(self.J_num):
            J = Job(k, self.PT[k],self.MT[k])
            self.Jobs.append(J)

    def reset(self):
        self.Create_Item()
        for Ji in self.Jobs:
            self.Machines[Ji.handling_machine[0]].put_into_job(Ji,0)
            Ji.Cur_site=Ji.handling_machine[0]

    def scheduling(self,action,agv_num):
        done = False
        # 空载
        agv = self.AGVs[agv_num]
        t = agv.end
        selected_job = self.Jobs[action]
        O_num=copy.copy(selected_job.O_num)
        Cur_Machine = self.Machines[selected_job.Cur_site]
        target_Machine = self.Machines[selected_job.next_machine()]
        trans_t1 = self.agv_trans[agv.Current_Site][Cur_Machine.index]
        trans_t2 = self.agv_trans[Cur_Machine.index][target_Machine.index]

        # 等待
        if selected_job in Cur_Machine.OB:
            # 获取AGV的移动开始时间
            JS,JE=selected_job.start,selected_job.end
            t,trans_t1 = agv.ST_move(selected_job.end, Cur_Machine.index,trans_t1,trans_t2)
        else:
            JS,JE=Cur_Machine.handling(selected_job)
            # 获取AGV的移动开始时间
            t,trans_t1 = agv.ST_move(selected_job.end, Cur_Machine.index,trans_t1,trans_t2)

        agv.send_to(selected_job.Cur_site, t, trans_t1, load=None)
        t1 = t + trans_t1
        Cur_Machine.pick_up_job(selected_job)

        # 负载转移
        agv.send_to(selected_job.next_machine(), t1, trans_t2, load=selected_job.index)
        selected_job.Cur_site = selected_job.next_machine()
        selected_job.finished_move()
        t3 = t1 + trans_t2
        selected_job.arrive = t3

        target_Machine.put_into_job(selected_job, t3)
        if selected_job.Cur_site == self.M_num:
            selected_job.J = True
        # self.update()
        if self.judge_state():
            done = True
            self.rest_work()
        return done,O_num,t,t3,JS,JE

    def scheduling1(self,action):
        wait_Job_IB, wait_Job_OB=0,0
        done = False
        # 空载
        T=[]
        selected_job = self.Jobs[action]
        Cur_Machine = self.Machines[selected_job.Cur_site]
        target_Machine = self.Machines[selected_job.next_machine()]
        Trans=[]

        if selected_job not in Cur_Machine.OB:
            # 获取AGV的移动开始时间
            Cur_Machine.handling(selected_job)
            # 获取AGV的移动开始时间
        for agv in self.AGVs:
            # t = agv.end
            trans_t1 = self.agv_trans[agv.Current_Site][Cur_Machine.index]
            trans_t2 = self.agv_trans[Cur_Machine.index][target_Machine.index]
            Trans.append([trans_t1,trans_t2])
            t,trans_t1 = agv.ST_move(selected_job.end, Cur_Machine.index,trans_t1,trans_t2)
            T.append(t)
        agv=self.AGVs[T.index(min(T))]
        t=min(T)
        trans_t1,trans_t2=Trans[T.index(min(T))][0],Trans[T.index(min(T))][1]
        agv.send_to(selected_job.Cur_site, t, trans_t1, load=None)
        t1 = t + trans_t1
        wait_Job_IB = selected_job.start - selected_job.arrive
        Cur_Machine.pick_up_job(selected_job)
        # 负载转移
        agv.send_to(selected_job.next_machine(), t1, trans_t2, load=selected_job.index)
        # print('这是agv',agv.idx,'动作为',agv._on)
        selected_job.Cur_site = selected_job.next_machine()
        selected_job.finished_move()
        t3 = t1 + trans_t2
        selected_job.arrive = t3

        target_Machine.put_into_job(selected_job, t3)
        if selected_job.Cur_site == self.M_num - 1:
            selected_job.J = True
        # self.update()
        if self.judge_state():
            done = True
            self.rest_work()
        return done, wait_Job_IB, wait_Job_OB


if __name__=="__main__":
    from Gantt_graph import Gantt
    # from simple_MADRL.Params import args
    import random
    import pickle
    import numpy as np
    import argparse
    from Env.Instance.Text_extract import data

    fee = r"..\\Instance\\Bilge_Ulusoy\\C1\\E71.PKl"

    n, m, PT, agv_trans, MT, agv_num = data(fee)
    # print(PT)
    parser = argparse.ArgumentParser("Reinforcement Learning experiments for multiagent environments")
    # TWO_AGVs_FS_ENV
    parser.add_argument("--n", type=int, default=n, help="Job number")
    parser.add_argument("--m", type=int, default=m, help="Machine number")
    parser.add_argument("--PT", type=list, default=PT, help="PROCESSING TIME OF JOB")
    parser.add_argument("--MT", type=list, default=MT, help="PROCESSING TIME OF JOB")
    parser.add_argument("--AGV_num", type=int, default=2, help="transportation time between machines")
    parser.add_argument("--agv_trans", type=list, default=agv_trans, help="transportation time between machines")
    args = parser.parse_args()


    fs=JS_Env(args)
    # print(args.PT,args.MT)
    fs.reset()
    L=[0, 7, 6, 0, 5, 7, 2, 6, 4, 5, 1, 7, 3, 5, 6, 5, 3, 4, 1, 7, 3, 2, 0, 1, 6, 2, 4]
    for action in L:
        print(action)
        K=fs.scheduling1(action)
        # Gantt(fs.Machines, fs.AGVs)
        # if done:
        #     break
    print(fs.C_max())
    Gantt(fs.Machines, fs.AGVs)