A recent review of solution approaches for green vehicle routing problem and its variants

IF 3.7 4区 管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Annisa Kesy Garside , Robiah Ahmad , Mohd Nabil Bin Muhtazaruddin
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引用次数: 0

Abstract

The green vehicle routing problem (GVRP) has been a prominent topic in the literature on logistics and transportation, leading to extensive research and previous review studies covering various aspects. Operations research has seen the development of various exact and approximation approaches for different extensions of the GVRP. This paper presents an up-to-date and thorough review of GVRP literature spanning from 2016 to 2023, encompassing 458 papers. significant contribution lies in the updated solution approaches and algorithms applied to both single-objective and multi-objective GVRP. Notably, 92.58 % of the papers introduced a mathematical model for GVRP, with many researchers adopting mixed integer linear programming as the preferred modeling approach. The findings indicate that both metaheuristics and hybrid are the most employed solution approaches for addressing single-objective GVRP. Among hybrid approaches, the combination of metaheuristics-metaheuristics is particularly favored by GVRP researchers. Furthermore, large neighborhood search (LNS) and its variants (especially adaptive large neighborhood search) emerges as the most widely adopted algorithm in single-objective GVRP. These algorithms are proposed within both metaheuristic and hybrid approaches, where A-/LNS is often combined with other algorithms. Conversely, metaheuristics are predominant in addressing multi-objective GVRP, with NSGA-II being the most frequently proposed algorithm. Researchers frequently utilize GAMS and CPLEX as optimization modeling software and solvers. Furthermore, MATLAB is a commonly employed programming language for implementing proposed algorithms.

绿色车辆路由问题及其变体解决方法的最新综述
绿色车辆路由问题(GVRP)一直是物流和运输文献中的一个突出主题,引发了广泛的研究和以往涉及各个方面的综述研究。在运筹学研究中,针对 GVRP 的不同扩展提出了各种精确和近似方法。本文对 2016 年至 2023 年期间的 GVRP 文献进行了最新、全面的综述,其中包括 458 篇论文。本文的重要贡献在于更新了适用于单目标和多目标 GVRP 的求解方法和算法。值得注意的是,92.58% 的论文介绍了 GVRP 的数学模型,许多研究人员采用混合整数线性规划作为首选建模方法。研究结果表明,元启发式和混合式是解决单目标 GVRP 最常用的方法。在混合方法中,元启发式与元启发式的结合尤其受到 GVRP 研究人员的青睐。此外,大型邻域搜索(LNS)及其变体(尤其是自适应大型邻域搜索)成为单目标 GVRP 中最广泛采用的算法。这些算法是在元启发式和混合方法中提出的,其中 A-/LNS 通常与其他算法相结合。相反,元启发式算法在处理多目标 GVRP 时占主导地位,其中 NSGA-II 是最常被提出的算法。研究人员经常使用 GAMS 和 CPLEX 作为优化建模软件和求解器。此外,MATLAB 也是常用的编程语言,用于实现所提出的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Operations Research Perspectives
Operations Research Perspectives Mathematics-Statistics and Probability
CiteScore
6.40
自引率
0.00%
发文量
36
审稿时长
27 days
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