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

被引:22
|
作者
Leng, Longlong [1 ]
Zhao, Yanwei [1 ]
Wang, Zheng [2 ]
Zhang, Jingling [1 ]
Wang, Wanliang [2 ]
Zhang, Chunmiao [1 ]
机构
[1] Zhejiang Univ Technol, Minist Educ, Key Lab Special Equipment Mfg & Adv Proc Technol, Hangzhou 310023, Zhejiang, Peoples R China
[2] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
regional location-routing problem; low-carbon; hyper-heuristic; quantum-based selection; environmental selection; SUPPLY CHAIN; EVOLUTIONARY ALGORITHM; DISTRIBUTION-SYSTEM; GENETIC ALGORITHM; FUEL CONSUMPTION; OPTIMIZATION; SELECTION; EMISSIONS; DEMAND; MODELS;
D O I
10.3390/su11061596
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:31
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  • [1] Shared mechanism based self-adaptive hyper-heuristic for regional low-carbon location-routing problem
    Leng, Longlong
    Zhao, Yanwei
    Zhang, Chunmiao
    [J]. Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2020, 26 (05): : 1407 - 1424
  • [2] Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches
    Leng, Longlong
    Zhang, Chunmiao
    Zhao, Yanwei
    Wang, Wanliang
    Zhang, Jingling
    Li, Gongfa
    [J]. JOURNAL OF CLEANER PRODUCTION, 2020, 273
  • [3] Low carbon location-routing problem based on evolutionary hyper-heuristic algorithm of ant colony selection mechanism
    基于蚁群选择超启发算法的低碳选址-路径问题
    [J]. Zhao, Yanwei (ywz@zjut.edu.cn), 1702, CIMS (26): : 1702 - 1716
  • [4] A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem
    Zhang, Chunmiao
    Zhao, Yanwei
    Leng, Longlong
    [J]. ALGORITHMS, 2019, 12 (07)
  • [5] An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem
    Leng, Longlong
    Zhao, Yanwei
    Zhang, Jingling
    Zhang, Chunmiao
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2019, 16 (11)
  • [6] A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery
    Yanwei Zhao
    Longlong Leng
    Chunmiao Zhang
    [J]. Operational Research, 2021, 21 : 1299 - 1332
  • [7] A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery
    Zhao, Yanwei
    Leng, Longlong
    Zhang, Chunmiao
    [J]. OPERATIONAL RESEARCH, 2021, 21 (02) : 1299 - 1332
  • [8] Evolutionary hyper-heuristics for low-carbon location-routing problem with heterogeneous fleet
    进化式超启发算法求解多车型低碳选址-路径问题
    [J]. Zhao, Yan-Wei (ywz@zjut.edu.cn), 1600, Northeast University (35): : 257 - 271
  • [9] A simple hyper-heuristic approach for a variant of many-to-many hub location-routing problem
    Venkatesh Pandiri
    Alok Singh
    [J]. Journal of Heuristics, 2021, 27 : 791 - 868
  • [10] A simple hyper-heuristic approach for a variant of many-to-many hub location-routing problem
    Pandiri, Venkatesh
    Singh, Alok
    [J]. JOURNAL OF HEURISTICS, 2021, 27 (05) : 791 - 868