IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i5p1967-d328460.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/5/1967/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/5/1967/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bowen Hou & Yang Cao & Dongye Lv & Shuzhi Zhao, 2020. "Transit-Based Evacuation for Urban Rail Transit Line Emergency," Sustainability, MDPI, vol. 12(9), pages 1-18, May.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Stuart X. & Ursavas, Evrim, 2018. "Design and analysis of a satellite network with direct delivery in the pharmaceutical industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 190-207.
    2. Liu, Dan & Yan, Pengyu & Pu, Ziyuan & Wang, Yinhai & Kaisar, Evangelos I., 2021. "Hybrid artificial immune algorithm for optimizing a Van-Robot E-grocery delivery system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    3. Zhou, Lin & Baldacci, Roberto & Vigo, Daniele & Wang, Xu, 2018. "A Multi-Depot Two-Echelon Vehicle Routing Problem with Delivery Options Arising in the Last Mile Distribution," European Journal of Operational Research, Elsevier, vol. 265(2), pages 765-778.
    4. Li, Hongqi & Zhang, Lu & Lv, Tan & Chang, Xinyu, 2016. "The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 169-188.
    5. Yue Lu & Maoxiang Lang & Xueqiao Yu & Shiqi Li, 2019. "A Sustainable Multimodal Transport System: The Two-Echelon Location-Routing Problem with Consolidation in the Euro–China Expressway," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    6. Drexl, Michael & Schneider, Michael, 2015. "A survey of variants and extensions of the location-routing problem," European Journal of Operational Research, Elsevier, vol. 241(2), pages 283-308.
    7. Nan Ding & Manman Li & Jianming Hao, 2023. "A Two-Phase Approach to Routing a Mixed Fleet with Intermediate Depots," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
    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. Longlong Leng & Yanwei Zhao & Zheng Wang & Jingling Zhang & Wanliang Wang & Chunmiao Zhang, 2019. "A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints," Sustainability, MDPI, vol. 11(6), pages 1-31, March.
    10. Xujing Zhang & Lichuan Wang & Yan Chen, 2019. "Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    11. Waltho, Cynthia & Elhedhli, Samir & Gzara, Fatma, 2019. "Green supply chain network design: A review focused on policy adoption and emission quantification," International Journal of Production Economics, Elsevier, vol. 208(C), pages 305-318.
    12. Huang, Yixiao & Savelsbergh, Martin & Zhao, Lei, 2018. "Designing logistics systems for home delivery in densely populated urban areas," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 95-125.
    13. Ozgur Kabadurmus & Mehmet S. Erdogan, 2023. "A green vehicle routing problem with multi-depot, multi-tour, heterogeneous fleet and split deliveries: a mathematical model and heuristic approach," Journal of Combinatorial Optimization, Springer, vol. 45(3), pages 1-29, April.
    14. Yu, Vincent F. & Jodiawan, Panca & Hou, Ming-Lu & Gunawan, Aldy, 2021. "Design of a two-echelon freight distribution system in last-mile logistics considering covering locations and occasional drivers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    15. Ravi Shankar Kumar & Alok Choudhary & Soudagar A. K. Irfan Babu & Sri Krishna Kumar & A. Goswami & M. K. Tiwari, 2017. "Designing multi-period supply chain network considering risk and emission: a multi-objective approach," Annals of Operations Research, Springer, vol. 250(2), pages 427-461, March.
    16. Janjevic, Milena & Winkenbach, Matthias & Merchán, Daniel, 2019. "Integrating collection-and-delivery points in the strategic design of urban last-mile e-commerce distribution networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 37-67.
    17. Hanpeng Zhang & Yuxin Wu & Yi Liao & Yuvraj Gajpal, 2020. "Cooperative Strategies in Two-Echelon Rescue Delivery Environment with Accessibility Uncertainty," Sustainability, MDPI, vol. 12(13), pages 1-18, July.
    18. Surendra Reddy Kancharla & Gitakrishnan Ramadurai, 2019. "Multi-depot Two-Echelon Fuel Minimizing Routing Problem with Heterogeneous Fleets: Model and Heuristic," Networks and Spatial Economics, Springer, vol. 19(3), pages 969-1005, September.
    19. Gaoyuan Qin & Fengming Tao & Lixia Li, 2019. "A Vehicle Routing Optimization Problem for Cold Chain Logistics Considering Customer Satisfaction and Carbon Emissions," IJERPH, MDPI, vol. 16(4), pages 1-17, February.
    20. Limin Su & Yongchao Cao & Wenjuan Zhang, 2023. "Low-Carbon Supply Chain Operation Decisions and Coordination Strategies Considering the Consumers’ Preferences," Sustainability, MDPI, vol. 15(14), pages 1-20, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1967-:d:328460. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.