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

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

Abstract

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

CLC Number:

TP301

Bao Huifang, Fang Jie, Zhang Jinsi, Wang Chuansheng. 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.

Figures/Tables 13

Fig. 1

Fig. 2

Table 1

Settings of model parameter values

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

Table 1

Table 2

Table 3

Fig. 3

Table 4

Comparative analysis of pheromone updating strategies

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

Table 4

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Table 5

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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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