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Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window

Author

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  • Ziqi Wang

    (College of Mechanical Engineering, Chongqing University, Chongqing 400044, China)

  • Peihan Wen

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

Abstract

Due to the rise of social and environmental concerns on global climate change, developing the low-carbon economy is a necessary strategic step to respond to greenhouse effect and incorporate sustainability. As such, there is a new trend for the cold chain industry to establish the low-carbon vehicle routing optimization model which takes costs and carbon emissions as the measurements of performance. This paper studies a low-carbon vehicle routing problem (LC-VRP) derived from a real cold chain logistics network with several practical constraints, which also takes customer satisfaction into account. A low-carbon two-echelon heterogeneous-fleet vehicle routing problem (LC-2EHVRP) model for cold chain third-party logistics servers (3PL) with mixed time window under a carbon trading policy is constructed in this paper and aims at minimizing costs, carbon emissions and maximizing total customer satisfaction simultaneously. To find the optimal solution of such a nondeterministic polynomial (NP) hard problem, we proposed an adaptive genetic algorithm (AGA) approach validated by a numerical benchmark test. Furthermore, a real cold chain case study is presented to demonstrate the influence of the mixed time window’s changing which affect customers’ final satisfaction and the carbon trading settings on LC-2EHVRP model. Experiment of LC-2EHVRP model without customer satisfaction consideration is also designed as a control group. Results show that customer satisfaction is a critical influencer for companies to plan multi-echelon vehicle routing strategy, and current modest carbon price and trading quota settings in China have only a minimal effect on emissions’ control. Several managerial suggestions are given to cold chain logistics enterprises, governments, and even consumers to help improve the development of cold chain logistics.

Suggested Citation

  • Ziqi Wang & Peihan Wen, 2020. "Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window," Sustainability, MDPI, vol. 12(5), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1967-:d:328460
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    1. Lipowski, Adam & Lipowska, Dorota, 2012. "Roulette-wheel selection via stochastic acceptance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(6), pages 2193-2196.
    2. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    3. Du, Timon & Wang, F.K. & Lu, Pu-Yun, 2007. "A real-time vehicle-dispatching system for consolidating milk runs," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(5), pages 565-577, September.
    4. G. Dantzig & R. Fulkerson & S. Johnson, 1954. "Solution of a Large-Scale Traveling-Salesman Problem," Operations Research, INFORMS, vol. 2(4), pages 393-410, November.
    5. Wan-Yu Liu & Chun-Cheng Lin & Ching-Ren Chiu & You-Song Tsao & Qunwei Wang, 2014. "Minimizing the Carbon Footprint for the Time-Dependent Heterogeneous-Fleet Vehicle Routing Problem with Alternative Paths," Sustainability, MDPI, vol. 6(7), pages 1-27, July.
    6. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    7. Diansheng Lin & Zhiyong Zhang & Jiaxin Wang & Liu Yang & Yongqiang Shi & Jeffrey Soar, 2019. "Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction," Sustainability, MDPI, vol. 11(16), pages 1-22, August.
    8. Guido Perboli & Roberto Tadei & Daniele Vigo, 2011. "The Two-Echelon Capacitated Vehicle Routing Problem: Models and Math-Based Heuristics," Transportation Science, INFORMS, vol. 45(3), pages 364-380, August.
    9. Soysal, Mehmet & Bloemhof-Ruwaard, Jacqueline M. & Bektaş, Tolga, 2015. "The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations," International Journal of Production Economics, Elsevier, vol. 164(C), pages 366-378.
    10. Zakeri, Atefe & Dehghanian, Farzad & Fahimnia, Behnam & Sarkis, Joseph, 2015. "Carbon pricing versus emissions trading: A supply chain planning perspective," International Journal of Production Economics, Elsevier, vol. 164(C), pages 197-205.
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    2. Demir, Sercan & Aktas, Ersin & Paksoy, Turan, 2021. "Cold chain logistics: The case of Turkish Airlines vaccine distribution," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 771-798, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Feiyue Qiu & Guodao Zhang & Ping-Kuo Chen & Cheng Wang & Yi Pan & Xin Sheng & Dewei Kong, 2020. "A Novel Multi-Objective Model for the Cold Chain Logistics Considering Multiple Effects," Sustainability, MDPI, vol. 12(19), pages 1-28, September.
    4. Abdul Salam Khan & Bashir Salah & Dominik Zimon & Muhammad Ikram & Razaullah Khan & Catalin I. Pruncu, 2020. "A Sustainable Distribution Design for Multi-Quality Multiple-Cold-Chain Products: An Integrated Inspection Strategies Approach," Energies, MDPI, vol. 13(24), pages 1-25, December.
    5. Qing Yuan, 2023. "Research on Improvement of Hotel Supply Chain Resource Management Decision in the Era of Big Data," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 16(1), pages 1-16, January.
    6. Yuntao Bai & Yuan Gao & Delong Li & Dehai Liu, 2022. "Coordinated Distribution or Client Introduce? Analysis of Energy Conservation and Emission Reduction in Canadian Logistics Enterprises," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    7. Rafidah Md Noor & Nadia Bella Gustiani Rasyidi & Tarak Nandy & Raenu Kolandaisamy, 2020. "Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    8. Sluijk, Natasja & Florio, Alexandre M. & Kinable, Joris & Dellaert, Nico & Van Woensel, Tom, 2023. "Two-echelon vehicle routing problems: A literature review," European Journal of Operational Research, Elsevier, vol. 304(3), pages 865-886.
    9. Lihua Liu & Aneng He & Tian Tian & Lai Soon Lee & Hsin-Vonn Seow, 2024. "Bi-Objective Mixed Integer Nonlinear Programming Model for Low Carbon Location-Inventory-Routing Problem with Time Windows and Customer Satisfaction," Mathematics, MDPI, vol. 12(15), pages 1-35, July.

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