Collaborative multidepot electric vehicle routing problem with time windows and shared charging stations

Expert Systems with Applications, Год журнала: 2023, Номер 219, С. 119654 - 119654

Опубликована: Фев. 4, 2023

Язык: Английский

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The Vehicle Routing Problem: State-of-the-Art Classification and Review

Applied Sciences, Год журнала: 2021, Номер 11(21), С. 10295 - 10295

Опубликована: Ноя. 2, 2021

Transportation planning has been established as a key topic in the literature and social production practices. An increasing number of researchers are studying vehicle routing problems (VRPs) their variants considering real-life applications scenarios. Furthermore, with rapid growth processing speed memory capacity computers, various algorithms can be used to solve increasingly complex instances VRPs. In this study, we analyzed recent published between 2019 August 2021 using taxonomic framework. We reviewed research according models solutions, divided into three categories customer-related, vehicle-related, depot-related models. classified solution exact, heuristic, meta-heuristic algorithms. The main contribution our study is classification table that available online Appendix A. This should enable future find relevant easily provide readers trends methodologies field VRPs some well-known variants.

Язык: Английский

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Two-echelon multi-depot multi-period location-routing problem with pickup and delivery

Computers & Industrial Engineering, Год журнала: 2023, Номер 182, С. 109385 - 109385

Опубликована: Июнь 15, 2023

Язык: Английский

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Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2024, Номер 192, С. 103798 - 103798

Опубликована: Сен. 30, 2024

Язык: Английский

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A clustering-based extended genetic algorithm for the multidepot vehicle routing problem with time windows and three-dimensional loading constraints

Applied Soft Computing, Год журнала: 2022, Номер 133, С. 109922 - 109922

Опубликована: Дек. 13, 2022

Язык: Английский

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Green location routing problem with flexible multi-compartment for source-separated waste: A Q-learning and multi-strategy-based hyper-heuristic algorithm

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 121, С. 105954 - 105954

Опубликована: Фев. 9, 2023

Язык: Английский

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A multi-depot vehicle routing problem with time windows, split pickup and split delivery for surplus food recovery and redistribution

Expert Systems with Applications, Год журнала: 2023, Номер 232, С. 120807 - 120807

Опубликована: Июнь 16, 2023

Язык: Английский

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Split-demand multi-trip vehicle routing problem with simultaneous pickup and delivery in airport baggage transit

European Journal of Operational Research, Год журнала: 2023, Номер 312(3), С. 996 - 1010

Опубликована: Июль 22, 2023

Язык: Английский

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A multitasking ant system for multi-depot pick-up and delivery location routing problem with time window

Complex & Intelligent Systems, Год журнала: 2025, Номер 11(2)

Опубликована: Янв. 22, 2025

Язык: Английский

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An adaptive ant colony algorithm for crowdsourcing multi-depot vehicle routing problem with time windows

Sustainable Operations and Computers, Год журнала: 2023, Номер 4, С. 62 - 75

Опубликована: Янв. 1, 2023

Logistics distribution faces difficulties such as increasing delivery demands, demanding time and lower costs. However, the routing optimization for logistic is not a simple vehicle problem (VRP). This study focuses on crowdsourcing multi-depot with windows (MDVRPTW) problem, permitting one driver to deliver multiple customers, which has been covered yet. To quickly distribute many tasks within certain time, this paper proposes two-stage MDVRPTW framework. The first stage division in k-means algorithm based elbow method used transform into several single-depot problems. Then VRPTW model developed, mixed-integer linear programming model. second stage, an adaptive ant colony (ADACO) proposed determine most reasonable routes each center. Solomons classic VRP data then validate models’ effectiveness, results confirming that strategy could save 10.02% costs than traditional strategy. By comparison, ADACO significantly improve ACO’s weakness of falling local optimum, would be more suitable solving large-scale problems GUROBI solver.

Язык: Английский

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