Optimized Design of Multi-Level Low-Carbon Logistics Distribution Scheme Based on Two Stages

Authors

DOI:

https://doi.org/10.61089/aot2024.2mdq5h75

Keywords:

low-carbon logistics network, carbon tax, distribution scheme, NSGA-II algorithm

Abstract

The logistics network, as a key component of commodity distribution, has a direct impact on carbon emissions and resource utilization. Its main objective is to optimize the distribution process of commodities in order to improve efficiency, reduce costs, ensure timely delivery of commodities, and simultaneously satisfy customers' needs. The problem of multiple factors in the optimal allocation of logistics network objectives leading to decision-making difficulties is addressed. The complex multilevel logistics network optimization problem is decomposed into two stages. The first stage determines the selection of cargo transit points and the distribution of cargo flow between nodes, starting with the establishment of a Comprehensive Modal Emission Model (CMEM) taking into account the speed of the vehicle, the amount of cargo loaded, the road surface conditions and the characteristics of the vehicle itself. Secondly, the carbon emission cost generated from the flow of goods, together with the transportation cost, distribution cost and fixed cost at the transit point, constitute the comprehensive cost, and establish a multi-objective optimization model of low-carbon logistics network with the goal of minimizing the comprehensive cost and transportation time. The Non-dominated Sorted Genetic Algorithm with Elite Strategies (NSGA-II) is used for the solution. Finally, MATLAB software was used to numerically analyze the two schemes of "Considering Carbon Tax Levy" and "Not Considering Carbon Tax Levy". The results show that the government's imposition of an environmental tax on companies will change the distribution of transit points and flows within the logistics network, reducing CO2 emissions by 226.5 kg and saving 257.65 CNY in comprehensive costs. The second stage determines the order and path of distribution from each transit point to its own customers, establishes a low-carbon logistics network distribution path optimization model with the goal of minimizing the cost of carbon emissions, and solves the problem using Genetic Algorithm (GA). Through the coordinated use of the two-stage optimization model, it provides enterprises with a network distribution solution that takes into account the low-carbon goal and logistics efficiency, and provides the government with a basis for carbon tax levy and a reference for the tax rate.

References

Bao, C., Zhang, S.,(2018). Route Optimization of cold chain logistics in joint distribution with consideration of carbon emission. Industrial Engineering and Management, 23(05), 95-100+107. https://doi.org/10.19495/j.cnki.1007-5429.2018.05.013.

Bao, X., Song, A.,(2023). Improved NSGA-II algorithm for solving multi-objective logistics vehicle routing optimization problem. Computer Applications and Software, 40(02), 274-280. https://doi.org/10.3969/j.issn.1000-386x.2023.02.043.

Büşra, O., Çağrı, K., Fulya, A.,(2021) A Hyper Heuristic for the Green vehicle routing problem with simultaneous pickup and Delivery. Computers & Industrial Engineering, 153. https://doi.org/10.1016/j.cie.2020.107010.

Elhedhli, S., Merrick, R.,(2012). Green supply chain network design to reduce carbon emissions. Transportation Research Part D, 17(5), 370-379. https://doi.org/10.1016/j.trd.2012.02.002.

Gao, Y.,(2006). Non-dominated sorting genetic algorithm and its applications. Zhejiang University.

Guo, X., Zhang, W., Liu, B.,(2022). Low-carbon routing for cold-chain logistics considering the time-dependent effects of traffic congestion. Transportation Research Part D ,113. https://doi.org/10.1016/j.trd.2022.103502.

He, D., Li, Y.,(2018). Optimization model of green multi-type vehicles routing problem. Journal of Computer Applications, 38(12), 3618-3624+3637. https://doi.org/1001-9081(2018)12-3618-3624-07.

Ji, Y., Du, J., Wu, X.,(2021). Robust optimization approach to two-echelon agricultural cold chain logistics considering carbon emission and stochastic demand. Environment, Development and Sustainability, 23, 13731-13754. https://doi.org/10.1007/s10668-021-01236-z.

Leng, L.,(2020). Research on low-carbon logistics location-routing problem and hyper-heuristic algorithm. Zhejiang University of Technology. https://doi.org/10.27463/d.cnki.gzgyu.2020.000009.

Li, B., Zhao, G.,(2017). Model of low-carbon remanufacturing logistics network based on robust optimization. Journal of Shandong University (Science Edition), 52(01), 43-55. https://doi.org/10.6040/j.issn.1671-9352.0.2016.174.

Li, C., Li, S., Liu, S.,(2020). Research on urban logistics distribution network from the perspective of low carbon. Ecological Economics, 36(01), 106-110.

Li, J.,(2015). Credibility-based Multi-objective fuzzy programming problem for low-carbon logistics network design. Systems Engineering - Theory&Practice, 35(06), 1482-1492. https://doi.org/10.12011/1000-6788(2015)6-1482

Li, Q.,(2022). Research on multi-objective agricultural cold chain logistics vehicle path planning . Tianjin Normal University. https://doi.org/10.27363/d.cnki.gtsfu.2022.000365.

Li, W., Zhang, C., Ma, C.,(2020). An optimization model and solution algorithm of Multi-objective vehicle path under low carbon conditions. Traffic Information and Safety, 38(01), 118-126+144. https://doi.org/10.3963/j.jssn.1674-4861.2020.01.015.

Liu, F.,(2022). Research on logistics distribution fleet configuration and route optimization under low carbon. Shandong University. https://doi.org/10.27272/d.cnki.gshdu.2022.005427.

Liu, S., Zhang, C.,(2023). Multiobjective optimization of railway cold-chain transportation route based on dynamic train information. Journal of Rail Transport Planning & Management, 26. https://doi.org/10.1016/j.jrtpm.2023.100381.

Lv, P.,(2013). Study of logistics network optimization model considering carbon emission. Application Research of Computer, 30(10), 2977-2980. https://doi.org/10.3969/j.issn.1001-3695.2013.10.024.

Martinez-puras, A., Pacheco, J.,(2016). Moamp-tabu search and NSGA-II for a real bi-objective scheduling-routing problem. Knowledge-Based Systems, (112), 92-104. https://doi.org/10.1016/j.knosys.2016.09.001.

Masoud, H., Mohammad Ali, F., Ali, H.,(2 023). A two-echelon location routing problem considering sustainability and hybrid open and closed routes under uncertainty. Heliyon,9(3). https://doi.org/10.1016/j.heliyon.2023.e14258.

Peng, Y., Mo, Z., Liu, S.,(2021). Passenger’s routes planning in stochastic common-lines’ multi-modal transportation network through integrating Genetic Algorithm and Monte Carlo simulation. Archives of Transport, 59(3), 73-92. https://doi.org/10.5604/01.3001.0015.0123.

Ren, T., Luo, T., Gu, Z.,(2022). Optimization of urban logistics co-distribution path considering simultaneous pick-up and delivery. Computer Integrated Manufacturing System, 28(11), 3523-3534. https://doi.org/10.13196/j.cims. 2022.11.016.

Song, M., Li, J., Han, Y.,(2020). Metaheuristics for solving the vehicle routing problem with the time windows and energy consumption in cold chain logistics. Applied Soft Computing Journal, 95, 106561. https://doi.org/10.1016/j.asoc.2020.106561.

Tong, H., (2022). Research on the site selection and path layout of the logistics distribution center of marine ships based on a mathematical model. Archives of Transport, 63(3), 23-34. https://doi.org/10.5604/01.3001.0015.9925.

Vincent F, Y., Pham , T., Roberto B.,(2023). A robust optimization approach for the vehicle routing problem with cross-docking under demand uncertainty. Transportation Research Part E, 173. https://doi.org/10.1016/j.tre.2023.103106.

Wang, Y., Shi, Q., Song, W.,(2020). Dynamic multi-objective optimization model and algorithm for logistics networks. Computer Integrated Manufacturing Systems, 26(04), 1142-1150. https://doi.org/10.13196/j.cims.2020.04.027.

Yang, J., Guo, J., Ma, S.,(2016). Low-carbon city logistics distribution network design with resource deployment. Journal of Cleaner Production, 2016, 119, 223-228. https://doi.org/10.1016/j.jclepro.2013.11.011.

Yuan, Y., Diego, C., Maxime, O., (2021). A column generation based heuristic for the generalized vehicle routing problem with time windows. Transportation Research Part E, 152. https://doi.org/10.1016/j.tre.2021.102391.

Zhang, D., Qiao, X., Xiao, B., (2021). Multi-objective vehicle routing optimization based on low carbon perspective and random demand. Journal of Railway Science and Engineering, 18(08), 2165-2174. https://doi.org/10.19713/j.cnki.43-1423/u.t20200883.

Zhang, L., Tseng, M., Wang, C., (2019) Low-carbon cold chain logistics using ribonucleic acid-ant colony optimization algorithm. Journal of Cleaner Production, 233, 169-180. https://doi.org/10.1016/j.jclepro.2019.05.306.

Zhang, S., Chen, N., Li, Y.,(2022). Research on optimization decision of urban cold chain logistics distribution system from perspective of low carbon. Industrial Engineering and Management, 27(01), 56-64. https://doi.org/10.19495/j.cnki.1007-5429.2022.01.007.

Zhu, A., Wen, Y.,(2021). Green logistics location-routing optimization solution based on improved GA Algorithm considering low-carbon and environmental protection. Journal of Mathematics, 2021, 6101194. https://doi.org/10.1155/2021/6101194.

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Published

2024-03-13

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Original articles

How to Cite

Fu, Z., Yue, J., & Yan, Y. (2024). Optimized Design of Multi-Level Low-Carbon Logistics Distribution Scheme Based on Two Stages. Archives of Transport, 69(1), 145-165. https://doi.org/10.61089/aot2024.2mdq5h75

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