A multi-objective dynamic vehicle routing optimization for fresh product distribution: A case study of Shenzhen

Wenjie Wang; Suzhen Wen; Shen Gao; Pengyi Lin

doi:10.3934/era.2024132

journal article Jan 01, 2024

A multi-objective dynamic vehicle routing optimization for fresh product distribution: A case study of Shenzhen

Wenjie Wang

Suzhen Wen Shen Gao Pengyi Lin

Electronic Research Archive Vol. 32 No. 4 pp. 2897-2920 · American Institute of Mathematical Sciences (AIMS)

View at Publisher Save 10.3934/era.2024132

Abstract

<abstract>

To improve the fast and efficient distribution of fresh products with dynamic customer orders, we constructed a multi-objective vehicle routing optimization model with the objectives of minimizing the distribution costs including freshness-loss cost, cold-chain-refrigeration cost, and delay-penalty cost, and maximizing customer time satisfaction. An improved multi-objective genetic algorithm (GA)-based particle swarm optimization (MOGAPSO) algorithm was designed to quickly solve the optimal solution for the distribution routes for fresh-product orders from regular customers. Furthermore, online real-time orders of fresh products were periodically inserted into the distribution routes with local optimization solutions by applying a dynamic inserting algorithm. Finally, a case study of a fresh-product distribution company in Shenzhen, China was conducted to validate the practicality of the proposed model and algorithms. A comparison with the NSGA-Ⅱ and MOPSO algorithms showed the superiority of the proposed MOGAPSO algorithm on distribution-cost reduction and customer time-satisfaction improvement. Moreover, the dynamic inserting algorithm demonstrated a better performance on distribution-cost reduction.

</abstract>

Topics

No keywords indexed for this article. Browse by subject →

References

21

[1]

IiMedia Research, China Fresh E-commerce Operation Big Data and Development Prospect Research Report from 2023 to 2024, 2023. Available from: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.iimedia.cn/c400/92930.html">https://www.iimedia.cn/c400/92930.html</ext-link>.

[2]

X. Cai, J. Chen, Y. Xiao, X. Xu, Optimization and coordination of fresh product supply chains with freshness‐keeping effort, Prod. Oper. Manage., 19 (2010), 261–278. http://doi.org/10.1111/j.1937-5956.2009.01096.x 10.1111/j.1937-5956.2009.01096.x

[3]

M. Yu, A. Nagurney, Competitive food supply chain networks with application to fresh produce, Eur. J. Oper. Res., 224 (2013), 273–282. https://doi.org/10.1016/j.ejor.2012.07.033 10.1016/j.ejor.2012.07.033

[4]

G. B. Dantzig, J. H. Ramser, The truck dispatching problem, Manage. Sci., 6 (1959), 80–91. https://doi.org/10.1287/mnsc.6.1.80 10.1287/mnsc.6.1.80

[5]

L. Li, S. Li, W. Li, F. Zhou, Freshness-driven vehicle routing problem: Modeling and application to the fresh agricultural product pick-storage-transportation, J. Ind. Manage. Optim., 19 (2023), 6218–6243. https://doi.org/10.3934/jimo.2022213 10.3934/jimo.2022213

[6]

W. Liao, L. Zhang, Z. Wei, Multi-objective green meal delivery routing problem based on a two-stage solution strategy, J. Clean. Prod., 258 (2020), 120627. https://doi.org/10.1016/j.jclepro.2020.120627 10.1016/j.jclepro.2020.120627

[7]

L. Leng, J. Zhang, C. Zhang, Y. Zhao, W. Wang, G. Li, Decomposition-based hyper heuristic approaches for the bi-objective cold chain considering environmental effects, Comput. Oper. Res., 123 (2020), 105043. https://doi.org/10.1016/j.cor.2020.105043 10.1016/j.cor.2020.105043

[8]

H. T. Hu, Y. Zhang, L. Zhen, A two-stage decomposition method on fresh product distribution problem, Int. J. Prod. Res., 55 (2017), 4729–4752. https://doi.org/10.1080/00207543.2017.1292062 10.1080/00207543.2017.1292062

[9]

Y. F. Cui, Z. Q. Geng, Q. X. Zhu, Y. Han, Review: Multi-objective optimization methods and application in energy saving, Energy, 125 (2017), 681–704. https://doi.org/10.1016/j.energy.2017.02.174 10.1016/j.energy.2017.02.174

[10]

X. Xu, Z. Lin, X. Li, C. Shang, Q. Shen, Multi-objective robust optimization model for MDVRPLS in refined oil distribution, Int. J. Prod. Res., 60 (2021), 6772–6792. https://doi.org/10.1080/00207543.2021.1887534 10.1080/00207543.2021.1887534

[11]

R. J. Kuo, M. F. Luthfiansyah, N. A. Masruroh, F. E. Zulvia, Application of improved multi-objective particle swarm optimization algorithm to solve disruption for the two-stage vehicle routing problem with time windows, Expert Syst. Appl., 125 (2023), 120009. https://doi.org/10.1016/j.eswa.2023.120009 10.1016/j.eswa.2023.120009

[12]

Y. Wang, K. Assogba, J. Fan, M. Xu, Y. Liu, H. Wang, Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost, J. Clean. Prod., 232 (2019), 12–29. https://doi.org/10.1016/j.jclepro.2019.05.344 10.1016/j.jclepro.2019.05.344

[13]

D. Huang, Y. Gu, S. Wang, Z. Liu, W. Zhang, A two-phase optimization model for the demand-responsive customized bus network design, Transp. Res. Part C Emerg. Technol., 111 (2020), 1–21. https://doi.org/10.1016/j.trc.2019.12.004 10.1016/j.trc.2019.12.004

[14]

F. E. Achamrah, F. Riane, S. Limbourg, Solving inventory routing with transshipment and substitution under dynamic and stochastic demands using genetic algorithm and deep reinforcement learning, Int. J. Prod. Res., 60 (2022), 6187–6204. https://doi.org/10.1080/00207543.2021.1987549 10.1080/00207543.2021.1987549

[15]

T. H. Nguyen, J. J. Jung, Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles, Neural. Comput. Appl., 33 (2021), 6405–6414. https://doi.org/10.1007/s00521-020-05402-8 10.1007/s00521-020-05402-8

[16]

Y. N. Guo, J. Cheng, S. Luo, D. Gong, Y. Xue, Robust dynamic multi-objective vehicle routing optimization method, IEEE/ACM Trans. Comput. Biol. Bioinf., 15 (2018), 1891–1903. https://doi.org/10.1109/TCBB.2017.2685320 10.1109/tcbb.2017.2685320

[17]

Ant Colony System for a Dynamic Vehicle Routing Problem

R. Montemanni, L. M. Gambardella, A. E. Rizzoli et al.

Journal of Combinatorial Optimization 10.1007/s10878-005-4922-6

[18]

Z. Wang, J. Liu, J. Zhang, Hyper-heuristic algorithm for traffic flow-based vehicle routing problem with simultaneous delivery and pickup, J. Comput. Des. Eng., 10 (2023), 2271–2287. https://doi.org/10.1093/jcde/qwad097 10.1093/jcde/qwad097

[19]

S. Prakash, I. Mukherjee, A multi-objective solution framework for the assembly inventory routing problem considering supply risk and carbon offset policies, J. Clean. Prod., 418 (2023), 138212. https://doi.org/10.1016/j.jclepro.2023.138212 10.1016/j.jclepro.2023.138212

[20]

P. Chen, H. Li, Optimal model and algorithm based on time satisfaction for O2O food delivery, Chin. J. Manage. Sci., 24 (2016), 170–176.

[21]

J. Gao, L. Zhen, S. Wang, Multi-trucks-and-drones cooperative pickup and delivery problem, Transp. Res. Part C Emerging Technol., 157 (2023), 104407. https://doi.org/10.1016/j.trc.2023.104407 10.1016/j.trc.2023.104407

Cited By

7

Dynamic request vehicle routing problem with workload balancing under time-dependent travel times

Wenxue Zhang, Boquan Gao · 2026

Transportation Research Part E: Log...

Metrics

7

Citations

21

References

Details

Published: Jan 01, 2024
Vol/Issue: 32(4)
Pages: 2897-2920

Authors

W

Wenjie Wang

Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China

S

Suzhen Wen

Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China

S

Shen Gao

Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China

P

Pengyi Lin

Meizhou Power Supply Bureau, Guangdong Power Grid Corporation, Meizhou 514021, China

Cite This Article

Wenjie Wang, Suzhen Wen, Shen Gao, et al. (2024). A multi-objective dynamic vehicle routing optimization for fresh product distribution: A case study of Shenzhen. Electronic Research Archive, 32(4), 2897-2920. https://doi.org/10.3934/era.2024132

A multi-objective dynamic vehicle routing optimization for fresh product distribution: A case study of Shenzhen

You May Also Like