Picking Path Planning of Container Robots Based on Improved Genetic Algorithm

doi:10.16182/j.issn1004731x.joss.22-0084

Abstract

Abstract:

Under the new "container-to-person" picking mode in intelligent warehouses, a new optimization model and its improved genetic algorithm are proposed to solve the picking path planning problem of multiple container robots. According to the picking mode and characteristics of container robots, the picking path planning problem is transformed into an asymmetric vehicle routing problem, and a mixed integer programming model is established with bi-objectives of the shortest total picking path and the least completion time. A hybrid genetic algorithm is designed to solve this model, and the effectiveness and stability of the algorithm are verified through large-scale examples. The computational results demonstrate that the picking efficiency of container robots is improved by the proposed model and its algorithm, and their total picking cost is reduced.

Key words: intelligent warehouse, container robots for picking, bi-objective path planning, asymmetric vehicle routing problem, hybrid genetic algorithm

CLC Number:

TP242

Yuwen Wu, Zhiyue Niu, Zhenping Li. Picking Path Planning of Container Robots Based on Improved Genetic Algorithm[J]. Journal of System Simulation, 2023, 35(5): 1086-1097.

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机器人编号	路径	路径长度/m	完成时间/s
目标值		591	125
1	0-13-6-18-12-1-0	116	116
2	0-17-21-4-3-24-0	116	116
3	0-28-25-8-26-23-0	90	90
4	0-9-10-29-20-16-0	70	70
5	0-11-27-22-15-19-0	74	74
6	0-30-2-14-5-7-0	125	125

算法	机器人编号	路径	路径长度/m	完成时间/s
SA- GA	1	0-6-18-12-8-1-0	140	140
	2	0-27-16-15-20-9-0	94	94
	3	0-10-29-30-2-14-0	101	101
	4	0-28-26-25-24-19-0	108	108
	5	0-11-23-17-21-7-0	88	88
	6	0-22-13-5-4-3-0	140	140
	目标值		671	140
SA	1	0-9-2-25-16-20-0	108	108
	2	0-11-21-14-5-4-0	124	124
	3	0-30-17-6-22-8-0	124	124
	4	0-23-12-28-10-18-0	98	98
	5	0-7-19-13-27-24-0	116	116
	6	0-29-26-31-3-15	123	123
	目标值		693	124
GA	1	0-14-7-19-13-2-0	116	116
	2	0-10-30-21-17-3-0	118	118
	3	0-6-5-15-31-4-0	160	160
	4	0-20-12-28-16-24-0	74	74
	5	0-18-22-8-11-23-0	94	94
	6	0-29-26-9-27-25-0	108	108
	目标值		670	160
CW	1	0-27-9-13-19-2-0	162	162
	2	0-5-3-31-15-7-0	159	159
	3	0-14-25-4-6-17-0	178	178
	4	0-30-21-16-26-29-0	122	122
	5	0-18-22-8-11-10-0	72	72
	6	0-12-28-23-24-20-0	86	86
	目标值		779	178

货箱数量	最大货架命中货箱数量	最大承载货箱数量	目标函数1		目标函数2		Gap1/%	Gap2/%
货箱数量	最大货架命中货箱数量	最大承载货箱数量	算法1(obj1)	算法2 (obj2)	算法1 (obj3)	算法2 (obj4)	Gap1/%	Gap2/%
avg							4.85	1.82
100	1	5	1 599	1 750	122	127	9.44	4.10
110	1	5	1 808	1 894	116	120	4.76	3.45
120	1	5	1 955	1 986	123	124	1.59	0.81
130	1	5	2 118	2 150	124	124	1.51	0
140	1	5	2 257	2 362	119	118	4.65	-0.84
150	1	5	2 418	2 591	118	122	7.15	3.39

货箱数量	最大货架命中货箱数量	最大承载货箱数量	目标函数1		目标函数2		Gap1/%	Gap2/%
货箱数量	最大货架命中货箱数量	最大承载货箱数量	算法1 (obj1)	算法2 (obj2)	算法1 (obj3)	算法2 (obj4)	Gap1/%	Gap2/%
avg							2.12	2.16
100	2	5	1 616	1 637	117	127	1.30	8.55
100	3	5	1 685	1 741	123	153	3.32	24.39
100	4	5	1 762	1 706	124	124	-3.18	0
120	2	5	1 872	1 951	125	120	4.22	-4.00
120	3	5	1 853	1 938	126	114	4.59	-9.52
120	4	5	2 024	2 082	119	119	2.87	0
150	2	5	2 371	2 403	116	116	1.35	0
150	3	5	2 275	2 358	119	119	3.65	0
150	4	5	2 345	2 368	119	119	0.98	0

货箱数量	最大货架命中货箱数量	最大承载货箱数量	目标函数1		目标函数2		Gap1/%	Gap2/%
货箱数量	最大货架命中货箱数量	最大承载货箱数量	算法1 (obj1)	算法2 (obj2)	算法1 (obj3)	算法2 (obj4)	Gap1/%	Gap2/%
avg							4.78	6.37
100	1	6	1 447	1 584	122	136	9.47	11.48
100	1	7	1 363	1 497	121	148	9.83	22.31
100	1	8	1 268	1 364	132	138	7.57	4.55
100	2	6	1 424	1 392	134	134	-2.25	0
100	2	7	1 330	1 417	130	136	6.54	4.62
100	2	8	1 187	1 302	123	134	9.69	8.94
100	3	6	1 440	1 423	117	123	-1.18	5.13
100	3	7	1 354	1 368	122	128	1.03	4.92
100	3	8	1 177	1 204	130	124	2.29	-4.62