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A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

Author

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  • Longlong Leng

    (Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yanwei Zhao

    (Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China)

  • Zheng Wang

    (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jingling Zhang

    (Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China)

  • Wanliang Wang

    (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)

  • Chunmiao Zhang

    (Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1596-:d:214342
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    References listed on IDEAS

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    Cited by:

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    2. Hongli Zhu & Congcong Liu & Yongming Song, 2022. "A Bi-Level Programming Model for the Integrated Problem of Low Carbon Supplier Selection and Transportation," Sustainability, MDPI, vol. 14(16), pages 1-11, August.
    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. Yong Wang & Yingying Yuan & Xiangyang Guan & Haizhong Wang & Yong Liu & Maozeng Xu, 2019. "Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows," Sustainability, MDPI, vol. 11(12), pages 1-30, June.
    5. Longlong Leng & Jingling Zhang & Chunmiao Zhang & Yanwei Zhao & Wanliang Wang & Gongfa Li, 2020. "A novel bi-objective model of cold chain logistics considering location-routing decision and environmental effects," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-29, April.

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