这个项目纯属小个人娱乐小项目,由于最近突然发现了一个比较好玩的第三方库simpy,由此引发了这一个小实验的灵感,本项目涉及内容:
路径优化算法:A*算法(采用A*做一个无路径冲突的AGV调度),其中节点间距离采用曼哈顿距离。
界面开发工具:tkinter
仿真工具:simpy
JS-AGV算例:3机器、3工件、2个AGV,不考虑AGV充电、考虑装卸站和路径冲突。
实验的目的:做一个动态演示的排产过程以及AGV调度过程。
实验扩展:后续有时间将采用强化学习来指导调度过程。
Machine=[
[1,2,3],
[2,3,1],
[1,3,2],
]
#机器在地图中的坐标
Machine_site=[[4,1],[4,2],[4,3]]
Processing_time=[
[4,7,8],
[5,2,3],
[1,3,4]
]
AGV_num=2
首先,搭建演示框架:
import simpy
import tkinter as tk
class Center:
def __init__(self,Env,x=4,y=5):
self.x=x
self.y=y
self.unit=70
self.Origin = [1, 2]
self.split = 5
self.Hight = self.y - 1 # 4
self.Width = self.x - 1 # 3
self.build_JSP()
def build_JSP(self):
self.window = tk.Tk()
self.window.title("Job shop scheduling simulation")
self.window.geometry("{1}x{1}".format((self.Hight + 4) * self.unit
, (self.Width + 9) * self.unit))
self.canvas = tk.Canvas(bg="white", height=(self.Hight + 4) \
* self.unit, width=(self.Width + 9) * self.unit)
# Grid Layout
for c in range(0, (self.Width * self.unit + 1), self.unit):
x0, y0, x1, y1 = self.Origin[0] * self.unit + c, self.Origin[1] * self.unit \
, self.Origin[0] * self.unit + c, (self.Hight + self.Origin[1]) \
* self.unit
self.canvas.create_line(x0, y0, x1, y1)
for r in range(0, (self.Hight * self.unit + 1), self.unit):
x0, y0, x1, y1 = self \
.Origin[0] * self.unit, self.Origin[1] * self.unit + r \
, (self.Width + self.Origin[0]) * self.unit \
, self.Origin[1] * self.unit + r
self.canvas.create_line(x0, y0, x1, y1)
# Loading Point
L = [[0, 2]]
for i in range(len(L)):
p1 = [(self.Origin[0] + L[i][0]) * self.unit - 5, (self.Origin[1] + self.Hight - L[i][1]) \
* self.unit - 5]
p2 = [(self.Origin[0] + L[i][0]) * self.unit + 5, (self.Origin[1] + self.Hight - L[i][1]) \
* self.unit + 5]
self.canvas.create_oval(p1[0], p1[1], p2[0], p2[1], fill="blue")
# Unloading Point
U = [[0, 1]]
for i in range(len(U)):
p1 = [(self.Origin[0] + U[i][0]) * self.unit - 5, (self.Origin[1] + self.Hight - U[i][1]) \
* self.unit - 5]
p2 = [(self.Origin[0] + U[i][0]) * self.unit + 5, (self.Origin[1] + self.Hight - U[i][1]) \
* self.unit + 5]
self.canvas.create_oval(p1[0], p1[1], p2[0], p2[1], fill="green")
# P/D Point
PD = [[2,1],[2,2],[2,3]]
Machine_name=["M1","M2","M3"]
#scheduling的布局
self.canvas.create_rectangle( 4.1* self.unit, 2* self.unit, 11* self.unit, 6* self.unit \
, fill="white")
for i in range(len(PD)):
p1 = [(self.Origin[0] + PD[i][0]) * self.unit - 5, (self.Origin[1] \
+ self.Hight - PD[i][1]) * self.unit - 5]
p2 = [(self.Origin[0] + PD[i][0]) * self.unit + 5, (self.Origin[1] \
+ self.Hight - PD[i][1]) * self.unit + 5]
self.canvas.create_rectangle(p1[0]-20, p1[1]-20, p2[0]+20, p2[1]+20 \
, fill="orange")
self.canvas.create_text(p1[0]+5, p1[1]+5, text=Machine_name[i]
, font=("arial", 12), fill="black")
self.canvas.create_rectangle(p1[0] - 20+1.7*self.unit, p1[1] - 20, p2[0] + 20+1.7*self.unit, p2[1] + 20 \
, fill="red")
self.canvas.create_text(p1[0] + 5+1.7*self.unit, p1[1] + 5, text=Machine_name[i]
, font=("arial", 12), fill="black")
# AS/RS]
p = [0.5 * self.unit, 4.5 * self.unit]
p1 = [0.1* self.unit, 5.5* self.unit]
p2 = [1* self.unit,3.5* self.unit]
self.canvas.create_rectangle(p1[0], p1[1], p2[0], p2[1] \
, fill="yellow")
self.canvas.create_text(p[0], p[1], text="AS/RS"
, font=("arial", 12), fill="black")
self.canvas.create_rectangle(0* self.unit \
, 0* self.unit, 1.6* self.unit \
, 0.8* self.unit \
, fill="gray")
self.time = self.canvas.create_text(0.8 * self.unit \
, 0.4 * self.unit, text="00:00" \
, font=("arial", 20) \
, fill="Blue")
self.canvas.create_text(2.5*self.unit, 1.5*self.unit, text="SIMULATION SIDE"
, font=("arial", 12), fill="black")
self.canvas.create_text(7.5 * self.unit, 1.5 * self.unit, text="SCHEDULING SIDE"
, font=("arial", 12), fill="black")
self.canvas.pack()
self.window.mainloop()
Env=simpy.Environment()
c=Center(Env)
得到界面框架如下: