Vehicle Routing Optimization for Underground Mines Considering Fuzzy Demand and Time Tolerance

doi:10.16182/j.issn1004731x.joss.24-1256

Abstract

Abstract:

To solve the problem of material demand fluctuation and different time windows in auxiliary transportation of underground mines, a routing optimization method for material distribution considering fuzzy demand and time tolerance was proposed.Based on the fuzzy credibility theory, uncertain demand was constrained by fuzzy chance constraints, and a multi-objective routing optimization model for underground mine auxiliary transportation was constructed with the objectives of minimizing the total operating cost of vehicles and maximizing time tolerance. A multi-objective genetic algorithm with mixed dominance strength was designed to solve the model. By calculating the deviation difference of Pareto frontier solutions, a vehicle transportation scheme that simultaneously satisfies the demands of mine workers and enterprises was obtained. Simulation results show that, under different problem scales, the non-dominated solutions obtained by the proposed algorithm have higher quality and better satisfy the actual transportation requirements of material distribution. The research results provide a theoretical basis for optimization and decision-making of material distribution schemes in underground mine auxiliary transportation.

Key words: underground mine, auxiliary transportation, routing optimization, fuzzy demand, time tolerance

CLC Number:

TP391.9

Wang Guorong, Deng Haishun, Kou Ziming, Yan Xuanxuan, Huang Zhixiang. Vehicle Routing Optimization for Underground Mines Considering Fuzzy Demand and Time Tolerance[J]. Journal of System Simulation, 2026, 38(4): 903-915.

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编号	坐标	模糊需求/t	可接受时间窗	期望时间窗	服务时间/min	容忍系数
1	(5 501.856，1 797.591)	[4.5；5.2；5.5]	08:02—08:40	08:16—08:25	5	0.7
2	(5 501.981，1 797.618)	[2.4；3.3；3.6]	08:14—09:38	08:25—09:03	3	0.4
3	(5 502.058，1 797.672)	[1.8；2.4；2.9]	08:13—08:54	08:33—08:44	5	1
4	(5 502.066，1 797.595)	[6.5；6.9；7.4]	08:09—09:09	08:20—08:47	5	1
5	(5 502.100，1 797.500)	[4.7；5.6；6.1]	08:08—08:37	08:22—08:32	4	1
6	(5 502.137，1 797.365)	[1.9；2.4；3.1]	08:03—09:17	08:23—08:46	6	0.8
7	(5 502.197，1 797.228)	[0.9；1.4；1.6]	08:11—08:35	08:15—08:18	6	0.4
8	(5 502.145，1 797.190)	[3.3；3.5；4.2]	08:09—08:41	08:18—08:29	2	0.6
9	(5 502.135，1 797.054)	[7.2；7.6；7.8]	08:23—09:37	08:36—09:18	4	0.9
10	(5 502.164，1 796.887)	[3.9；4.4；4.8]	08:26—09:32	08:55—09:18	5	0.5
11	(5 502.284，1 797.638)	[2.6；3.1；3.5]	08:07—09:03	08:20—08:26	6	0.3
12	(5 502.628，1 797.559)	[4.6；4.9；5.3]	08:11—08:55	08:24—08:38	3	0.7
13	(5 502.794，1 797.523)	[5.1；5.5；5.7]	08:19—08:45	08:32—08:39	4	1
14	(5 502.739，1 797.530)	[1.7；2.1；2.6]	08:02—08:51	08:19—08:28	6	1
15	(5 502.758，1 797.385)	[8.8；9.2；9.5]	08:12—09:34	08:32—09:07	4	0.7
16	(5 502.667，1 797.329)	[4.9；5.6；6.2]	08:05—09:20	08:15—08:45	5	0.5
17	(5 502.733，1 797.175)	[10.5；11；11.6]	08:03—08:56	08:20—08:35	3	0.8
18	(5 502.756，1 797.079)	[5.6；6.1；6.7]	08:17—10:12	08:47—10:02	3	0.7
19	(5 502.668，1 797.021)	[2.8；3.3；3.6]	08:03—08:55	08:20—08:55	6	0.3
20	(5 502.691，1 796.986)	[4.6；5.5；5.9]	08:13—09:11	08:31—09:01	5	0.6

算例规模	车辆数	MOPSO				NSGA-Ⅱ				SDS-NSGA-Ⅱ
算例规模	车辆数	非劣解数	运行里程/km	容忍度	求解时间/s	非劣解数	运行里程/km	容忍度	求解时间/s	非劣解数	运行里程/km	容忍度	求解时间/s
30	10	13	422.88	0.745	43.77	12	440.67	0.759	43.39	7	415.19	0.787	41.77
50	16	14	791.11	0.684	69.34	18	770.76	0.734	62.99	26	750.72	0.750	62.78
100	32	28	1 463.42	0.743	121.34	23	1 449.73	0.773	112.37	21	1 407.60	0.781	113.14