Cold Chain Low-carbon Logistics Path Optimisation Method Based on Improved Hummingbird Optimisation Algorithm

Authors

  • Xuan Long School of international trade Hainan College of Economics and Business, Hai'kou 571127, Hainan , China

DOI:

https://doi.org/10.4108/ew.4991

Keywords:

cold chain low-carbon logistics path optimisation, Hummingbird optimisation algorithm, Jiaden set improvement strategy, two-layer coding approach

Abstract

INTRODUCTION:The research of scientific and reasonable logistics and distribution programme time the pursuit of each logistics enterprise, not only can improve customer satisfaction and corporate image, but also help to reduce distribution costs.

OBJECTIVES: For the current cold chain low-carbon logistics distribution path optimisation methods there are problems such as easy to fall into the local optimum, optimisation time-consuming.

METHODS: This paper proposes a cold chain low-carbon logistics distribution path optimisation method based on the improved Hummingbird optimisation algorithm. Firstly, by analyzing the characteristics of the cold chain low-carbon logistics distribution path optimization problem, designing the cold chain low-carbon logistics path optimization objective function and constraints, and constructing a cold chain low-carbon logistics distribution path optimization model based on a soft time window; then, the hummingbird optimization algorithm is improved by using the initialization strategy of the set of good points and the cardinality leap strategy, to overcome the defects of the hummingbird optimization algorithm; secondly, a method based on intelligent optimization algorithm is proposed by designing the double-layer array coding and the adaptive function, combined with the improved hummingbird optimization algorithm. A cold chain low-carbon logistics path optimization method based on intelligent optimization algorithm is proposed; finally, the superiority and robustness of the proposed method are verified by simulation experimental analysis.

RESULTS: The results show that the proposed method not only improves the optimisation time, but also increases the optimisation fitness value.

CONCLUSION: This paper solves the problem that the optimisation of the green low-carbon logistics path optimisation problem is time-consuming and prone to falling into local optimum.

Downloads

Download data is not yet available.

References

Liu S , Zhang C .Multiobjective optimization of railway cold-chain transportation route based on dynamic train information[J]. transport planning & management, 2023. DOI: https://doi.org/10.2139/ssrn.4194312

Bai Q , Yin X , Lim M K ,Dong C.Low-carbon VRP for cold chain logistics considering real-time traffic conditions in the road network[J].Industrial management & data systems, 2022. DOI: https://doi.org/10.1108/IMDS-06-2020-0345

Jin J , Wen Q , Cheng S , Qiu Y, Zhang X, Guo X. Optimization of carbon emission reduction paths in the low-carbon power dispatching process[J].Renewable Energy, 2022, 188. DOI: https://doi.org/10.1016/j.renene.2022.02.054

Andoh E A , Yu H .A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines[J]. .Annals of Operations Research, 2022:1-31. DOI: https://doi.org/10.1007/s10479-022-04906-x

Yang Y , Ma C , Zhou J , Dong J, Ling G, Li J. A multi-dimensional robust optimization approach for cold-chain emergency medical materials dispatch under COVID-19: A case study of Hubei Province[J]. Journal of Transport Engineering: English Edition, 2022, 9(1):1-20.

Wangsa I D , Vanany I , Siswanto N .An optimisation model for fresh-food electronic commerce supply chain with carbon emissions and food waste[J]. Journal of Industrial and Production Engineering, 2023. DOI: https://doi.org/10.1080/21681015.2022.2099473

Lim M K , Li Y , Wang C , Tseng M L. Prediction of cold chain logistics temperature using a novel hybrid model based on the mayfly algorithm and extreme learning machine[J].Industrial management & data systems, 2022(3):122. DOI: https://doi.org/10.1108/IMDS-10-2021-0607

Shi Y , Lin Y , Lim M K , Tseng M L, Tan C, Li Y. An intelligent green scheduling system for sustainable cold chain logistics[J].Expert Systems with Application, 2022. DOI: https://doi.org/10.1016/j.eswa.2022.118378

Wu D , Zhu Z , Hu D , Mansour R F. Optimizing Fresh Logistics Distribution Route Based on Improved Ant Colony Algorithm[J]. Computers, Materials and Continuum (English), 2022(10):17. DOI: https://doi.org/10.32604/cmc.2022.027794

Teymourian E , Kayvanfar V , Komaki G M , Zandieh M. Enhanced intelligent water drops and cuckoo search algorithms for solving the capacitated vehicle routing problem[J].Information Sciences, 2016, 334:354-378. DOI: https://doi.org/10.1016/j.ins.2015.11.036

Jabir E , Panicker V V , Sridharan R .Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem[J].Transportation Research Part D: Transport and Environment, 2017, 57(12):422-457. DOI: https://doi.org/10.1016/j.trd.2017.09.003

Okulewicz M ,Mańdziuk, Jacek.The impact of particular components of the PSO-based algorithm solving the Dynamic Vehicle Routing Problem[J]. Applied Soft Computing, 2017, 58: 586-604. DOI: https://doi.org/10.1016/j.asoc.2017.04.070

Lagos C, Guerrero G, Cabrera E. An improved particle swarm optimisation algorithm for the VRP with simultaneous pickup and delivery and time windows [J]. IEEE Latin America Transactions, 2018, 16(6): 1732-1740. DOI: https://doi.org/10.1109/TLA.2018.8444393

Zhang Xiaonan,Fan Houming. Fuzzy demand vehicle path optimisation and real-time adjustment[J]. Journal of Shanghai Jiao Tong University, 2016, 50(1):9.

FAN Houming, LIU Pengcheng, LIU Hao, HOU Dengkai. VRPSDP problem with stochastic collection demand under multi-centre joint distribution model[J]. Journal of Automation, 2021, 47(7):15.

Ke-Wei J, San-Yang L, Xiao-Jun S. A hybrid algorithm for time-dependent vehicle routing problem with soft time windows and stochastic factors[J]. Engineering Applications of Artificial Intelligence, 2022, 109: 104606. DOI: https://doi.org/10.1016/j.engappai.2021.104606

Zhang J , Ding B .Multi-Objective Cold Chain Path Optimization Based on Customer Satisfaction[J]. Applied Mathematics and Applied Physics(English), 2023, 11(6):1806-1815. DOI: https://doi.org/10.4236/jamp.2023.116116

Zheng C , Sun K , Gu Y , Shen J, Du M, Guo Y. Multimodal Transport Path Selection of Cold Chain Logistics Based on Improved Particle Swarm Optimization Algorithm[J].Journal of advanced transportation, 2022. DOI: https://doi.org/10.1155/2022/5458760

Abdallah A , Dauwed M , Aly A A , Felemban B F, Khan I, Choi B J. An Optimal Method for Supply Chain Logistics Management Based on Neural Network[J]. Computers, Materials and Continuum (English), 2022. DOI: https://doi.org/10.32604/cmc.2022.031514

Kate T .ASHP's second cold chain forum tackles excursion management challenges and optimisation[J]. 2022(2):2.

Yang Y , Ma C , Zhou J , Dong S, Ling G, Li J. A multi-dimensional robust optimization approach for cold-chain emergency medical materials dispatch under COVID-19:A case study of Hubei Province[J].Journal of Traffic and Transportation Engineering (English Edition), 2022, 9(1):1-20. DOI: https://doi.org/10.1016/j.jtte.2022.01.001

Liu S .Multimodal Transportation Route Optimization of Cold Chain Container in Time-Varying Network Considering Carbon Emissions[J]. Sustainability, 2023. DOI: https://doi.org/10.3390/su15054435

Zhang Z , Huang C , Huang H ,et al. An optimisation method: hummingbirds optimization algorithm[J]. 2018, 29(2):386-404. DOI: https://doi.org/10.21629/JSEE.2018.02.19

J. Chen,Q. He. Sparrow optimisation algorithm with mixed-strategy improvement[J]. Small Microcomputer Systems, 2023, 44(7):1470-1478.

Xiaoxin Du, Zhenfei Wang, Bo Wang, Hao Wang, Tianru Hao, Lianhe Cui. Black spider optimisation algorithm based on cardinality leaping strategy with applications[J]. Journal of Shaanxi University of Science and Technology, 2023, 41(6):162-175.

Downloads

Published

31-01-2024

How to Cite

1.
Long X. Cold Chain Low-carbon Logistics Path Optimisation Method Based on Improved Hummingbird Optimisation Algorithm. EAI Endorsed Trans Energy Web [Internet]. 2024 Jan. 31 [cited 2024 Feb. 22];11. Available from: https://publications.eai.eu/index.php/ew/article/view/4991