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A probability guided evolutionary algorithm for multi-objective green express cabinet assignment in urban last-mile logistics

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  • Shou-feng Ji
  • Rong-juan Luo
  • Xiao-shuai Peng

Abstract

In the past decade, urban last-mile logistics (ULML) has attracted increasing attention with the growth of e-commerce. Under this background, express cabinet has been gradually advocated to improve the efficiency of ULML. This paper focuses on the multi-objective green express cabinet assignment problem (MGECAP) in ULML, where the objectives to be minimised are the total cost and the energy consumption. MGECAP is concerned with optimising the purchase and assignment decision of express cabinets, which is different from conventional assignment problems. To solve MGECAP, firstly, the integer programming model and the corresponding surrogate model are established. Secondly, problem-dependent heuristics, including the solution representation, genetic operators, and repair strategy of infeasible solutions, are proposed. Thirdly, a probability guided multi-objective evolutionary algorithm based on decomposition (PG-MOEA/D) is proposed, which can balance the limited computation resource among sub-problems during the iterative process. Meanwhile, a feedback strategy is put forward to alternatively generate new solutions when the probability condition is not satisfied. Finally, numerical results and a real-life case study demonstrate the effectiveness and the practical values of the PG-MOEA/D.

Suggested Citation

  • Shou-feng Ji & Rong-juan Luo & Xiao-shuai Peng, 2019. "A probability guided evolutionary algorithm for multi-objective green express cabinet assignment in urban last-mile logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(11), pages 3382-3404, June.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:11:p:3382-3404
    DOI: 10.1080/00207543.2018.1533653
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    Cited by:

    1. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.
    2. Kexin Bi & Mengke Yang & Latif Zahid & Xiaoguang Zhou, 2020. "A New Solution for City Distribution to Achieve Environmental Benefits within the Trend of Green Logistics: A Case Study in China," Sustainability, MDPI, vol. 12(20), pages 1-25, October.
    3. Magdalena Mucowska, 2021. "Trends of Environmentally Sustainable Solutions of Urban Last-Mile Deliveries on the E-Commerce Market—A Literature Review," Sustainability, MDPI, vol. 13(11), pages 1-26, May.

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