基于改进蚁群算法的低碳冷链配送路径优化

doi:10.16182/j.issn1004731x.joss.22-0963

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (1): 183-194.doi: 10.16182/j.issn1004731x.joss.22-0963

• 论文 • 上一篇

基于改进蚁群算法的低碳冷链配送路径优化

鲍惠芳(), 方杰(), 张进思, 王传胜

皖西学院电气与光电工程学院，安徽六安 237012

收稿日期:2022-08-15 修回日期:2022-11-29 出版日期:2024-01-20 发布日期:2024-01-19
通讯作者: 方杰 E-mail:1069484572@qq.com;63640193@qq.com
第一作者简介:鲍惠芳(1992-)，女，讲师，硕士，研究方向为自动控制与路径规划。E-mail：1069484572@qq.com
基金资助:
安徽省教育厅重点项目(2022AH051675);皖西学院自然重点项目(WXZR202015);皖西学院青年项目(WXZR202120)

Optimization on Cold Chain Distribution Routes Considering Carbon Emissions Based on Improved Ant Colony Algorithm

Bao Huifang(), Fang Jie(), Zhang Jinsi, Wang Chuansheng

School of Electrical and Photoelectric Engineering, West Anhui University, Luan 237012, China

Received:2022-08-15 Revised:2022-11-29 Online:2024-01-20 Published:2024-01-19
Contact: Fang Jie E-mail:1069484572@qq.com;63640193@qq.com

摘要/Abstract

摘要：

针对目前冷链配送路径优化中存在的综合配送成本考虑不全面的问题，根据生鲜配送特点，综合考虑基本运输成本、碳排放、制冷、货损以及时间窗约束，建立以综合配送成本最小化为目标的路径优化模型。提出改进蚁群算法对该优化模型进行求解，在初始阶段使用遗传算法生成初期信息素分布，而后使用蚁群算法进行后续寻优搜索，再引入模拟退火算法的Metropolis准则筛选优质解。通过仿真实验验证了优化模型和改进算法的有效性，对低碳可持续发展理念下生鲜品冷链配送路径优化问题研究具有一定意义，助力冷链运输行业向低碳经济转型。

关键词: 冷链配送, 优化模型, 改进蚁群算法, 低碳

Abstract:

As the comprehensive distribution cost is not considered comprehensively in the current cold chain distribution route optimization, this paper builds a path optimization model to minimize the comprehensive distribution cost. The model combines with the characteristics of fresh distribution, and comprehensively considers the transportation cost, carbon emission, refrigeration, cargo damage and time window constraints during cold chain transportation. Then, an improved ant colony algorithm is designed to solve this model. At the initial stage, the genetic algorithm is adopted to generate the initial pheromone, and then the ant colony algorithm is applied to conduct the subsequent optimization search. The Metropolis criterion of the simulated annealing algorithm is introduced to screen the high-quality solution. Finally, the effectiveness of the proposed optimization model and improved algorithm is verified by several experiments. The proposed model and improved algorithm have a certain significance for the research on the optimization of the cold chain distribution route of fresh food under the concept of low-carbon sustainable development. They helps the cold chain transportation industry to transition to low-carbon economy.

Key words: cold chain distribution, optimization model, improved ant colony algorithm, low-carbon

中图分类号:

TP301

鲍惠芳,方杰,张进思等 . 基于改进蚁群算法的低碳冷链配送路径优化[J]. 系统仿真学报, 2024, 36(1): 183-194.

Bao Huifang,Fang Jie,Zhang Jinsi,et al . Optimization on Cold Chain Distribution Routes Considering Carbon Emissions Based on Improved Ant Colony Algorithm[J]. Journal of System Simulation, 2024, 36(1): 183-194.

图/表 13

图1

图2

表1

模型参数值设置一览表

参数	数值	参数	数值
$A$ /(元/辆)	500	$l t_b$ /h	3
$B$ /(元/升)	7	$l 1$ /(L/h)	2
$v$ /(km/h)	40	$l 2$ /(L/h)	4
$Q$	100	$P$ /kg	30
$c *$ /(L/km)	0.20	$ε 1$	0.004
$c 0$ /(L/km)	0.15	$ε 2$	0.007
$M$ (元)	2 000	$λ$ /(元/kg)	0.1
$f 0$ (元)	100	$c 1$ /(kg/L)	2.63
$p t$ /(元/h)	20	$c 2$ /(kg/h)	3
$a_e t$ /h	3	$c 3$ /(kg/h)	6

表1

表2

表3

图3

表4

信息素更新策略对比分析

算法	平均配送成本(元)	平均收敛次数
IP_ACA( $p = 0.3$ )	3 298.8	12.4
IP_ACA( $p = 0.6$ )	3 287.1	15.6
IP_ACA( $p = 1.0$ )	3 285.3	19.2
IP_ACA(Metropolis)	3 283.9	14.1

表4

图4

图5

图6

图7

图8

表5

参考文献 17

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客户数量	VRPTW模型			本文优化模型
客户数量	配送路线	传统优化目标(元)	综合配送成本(元)	配送路线	传统优化目标(元)	综合配送成本(元)
20	0-6-13-2-15-14-16-0	1 836.6	2 005.7	0-13-2-15-14-16-17-0	1 837.5	1 981.5
	0-5-17-8-18-7-19-11-10-0			0-6-5-18-8-7-19-11-10-0
	0-1-20-9-3-12-4-0			0-1-20-9-3-12-4-0
30	0-1-30-20-10-11-19-0	3 018.9	3 335.1	0-13-18-1-30-10-0	3 027.1	3 291.8
	0-7-18-8-17-16-14-15-0			0-7-19-11-20-9-0
	0-2-13-6-5-0			0-27-28-26-12-3-29-24-25-0
	0-27-28-26-12-3-9-0			0-6-5-8-17-16-14-15-0
	0-29-24-25-4-21-22-23-0			0-4-21-2-22-23-0
50	0-22-41-15-43-42-37-2-21-4-0	4 876.4	5 316.6	0-11-19-47-36-49-46-0	4 856.7	5 287.1
	0-27-28-26-12-3-33-0			0-5-17-45-8-48-7-0
	0-40-13-6-5-17-45-8-46-0			0-18-37-16-44-14-38-0
	0-19-47-36-49-11-0			0-22-41-15-43-42-2-13-6-0
	0-50-1-31-7-48-0			0-27-28-12-26-40-21-0
	0-10-30-32-20-9-35-0			0-3-33-9-35-34-29-24-50-1-0
	0-34-29-24-25-39-23-0			0-31-10-30-20-32-0
	0-18-16-44-14-38-0			0-4-25-39-23-0

算法	配送路线	配送成本(元)
ACA	0-18-8-5-17-16-14-15-0	3 301.3
	0-1-30-10-11-19-7-0
	0-27-28-26-12-24-29-3-0
	0-2-21-22-23-4-25-0
	0-13-6-9-20-0
IP_ACA	0-5-17-8-18-7-19-11-10-0	3 256.5
	0-2-13-6-1614-15-0
	0->1->30->20->9->0
	0-27-28-26-12-3-29-24-25-0
	0-4-21-22-23-0
GA	0-9-20-30-10-11-19-7-0	3 286.9
	0-18-8-5-17-16-14-15-0
	0-27-28-26-12-3-29-24-0
	0-2-21-22-23-4-25-1-0
	0-13-6-0
Improved ACA	0-5-6-18-8-7-19-1110-0	3 211.6
	0-13-2-15-14-16-17-0
	0-21-22-23-4-25-0
	0-27-28-26-12-24-29-3-0
	0-9-20-30-1-0

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基于改进蚁群算法的低碳冷链配送路径优化

Optimization on Cold Chain Distribution Routes Considering Carbon Emissions Based on Improved Ant Colony Algorithm

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