A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas DOI Creative Commons
Reza Jazemi, Ensieh Alidadiani, Kwangseog Ahn

и другие.

Applied Sciences, Год журнала: 2023, Номер 13(24), С. 13015 - 13015

Опубликована: Дек. 6, 2023

Logistics has long been important in an industrial society. Compared with the traditional structure of distribution, which requires freight to be delivered mostly warehouses or retail stores, customers now often prefer packages their residences, especially after delivery challenges during COVID-19 pandemic. The parcels urban residential areas increases challenge due amount volume, tight schedules, and continuously changing conditions. Last-mile tries address challenges, taking advantage available automation, sensor communication technologies, people’s attitudes toward parcel for benefit all stakeholders. Various approaches last-mile have proposed analyzed literature. This paper reviews recent literature on vehicle routing delivery. review identified four major categories: crowdshipping, lockers, by sidekicks, optional points. nature problems is discussed five aspects: fleet capacity, time window, option, dynamism input, stochastic parameters. identifies achievements limitations research proposes a future agenda.

Язык: Английский

Multi-objective evolutionary approach based on K-means clustering for home health care routing and scheduling problem DOI Creative Commons
Mariem Belhor, Adnen El Amraoui, Abderrazak Jemai

и другие.

Expert Systems with Applications, Год журнала: 2022, Номер 213, С. 119035 - 119035

Опубликована: Окт. 17, 2022

Язык: Английский

Процитировано

43
A robust algorithm based on Differential Evolution with local search for the Capacitated Vehicle Routing Problem DOI
Israel Pereira de Souza, Maria Claudia Silva Bóeres, Renato Elias Nunes de Moraes

и другие.

Swarm and Evolutionary Computation, Год журнала: 2023, Номер 77, С. 101245 - 101245

Опубликована: Янв. 14, 2023

Язык: Английский

Процитировано

33
Enhancing Reliability Calculation for One-Output k-out-of-n Binary-state Networks Using a New BAT DOI
Wei‐Chang Yeh

Reliability Engineering & System Safety, Год журнала: 2025, Номер 257, С. 110835 - 110835

Опубликована: Янв. 15, 2025

Язык: Английский

Процитировано

1
FLDQN: Cooperative Multi-Agent Federated Reinforcement Learning for Solving Travel Time Minimization Problems in Dynamic Environments Using SUMO Simulation DOI Creative Commons

Abdul Wahab Mamond,

Majid Kundroo, Seong-eun Yoo

и другие.

Sensors, Год журнала: 2025, Номер 25(3), С. 911 - 911

Опубликована: Фев. 3, 2025

The increasing volume of traffic has led to severe challenges, including congestion, heightened energy consumption, increased air pollution, and prolonged travel times. Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable effectiveness in dynamic scenarios like management, their primary focus been on single-agent setups, limiting applicability real-world multi-agent systems. Managing agents fostering collaboration a reinforcement learning scenario remains challenging task. This paper introduces cooperative federated algorithm named FLDQN address the challenge agent cooperation by solving time minimization challenges (MARL) scenarios. leverages facilitate knowledge sharing among intelligent agents, vehicle routing reducing congestion environments. Using SUMO simulator, multiple equipped with deep Q-learning models interact local environments, share model updates via server, collectively enhance policies using unique observations while benefiting from collective experiences other agents. Experimental evaluations demonstrate that achieves significant average reduction over 34.6% compared non-cooperative simultaneously lowering computational overhead through distributed learning. underscores vital impact provides an solution enabling environment.

Язык: Английский

Процитировано

1
A mixed closed-open multi-depot routing and scheduling problem for homemade meal delivery incorporating drone and crowd-sourced fleet: A self-adaptive hyper-heuristic approach DOI
Mahdi Hamid, Mohammad Mahdi Nasiri, Masoud Rabbani

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 120, С. 105876 - 105876

Опубликована: Янв. 28, 2023

Язык: Английский

Процитировано

16
The vehicle routing problem in the last decade: variants, taxonomy and metaheuristics DOI Open Access

Said Elatar,

Karim Abouelmehdi, Mohammed Essaid Riffi

и другие.

Procedia Computer Science, Год журнала: 2023, Номер 220, С. 398 - 404

Опубликована: Янв. 1, 2023

Vehicle routing problem is a NP-hard and combinatorial optimization problem; it appeared first time in 1959 the paper of mathematician Dantzig. The goal VRP to locate optimal routes some vehicles that begin from depot serve each customer one then return (i.e., Starting point). From its beginning, research literature this area growing rapidly causing extension many variants for making real-world problem. For solving it, researchers have tried firstly exact methods heuristics lastly metaheuristics. This aims targets instance: (i) discovering evolution over last decade; (ii) knowing trends, challenges opportunities next years fields by discovering, comparing recent reviews papers related either or metaheuristics exploiting these results building on them other papers.

Язык: Английский

Процитировано

12
A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research DOI Open Access

Qiuping Ni,

Yuanxiang Tang

Sustainability, Год журнала: 2023, Номер 15(9), С. 7394 - 7394

Опубликована: Апрель 29, 2023

The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been hot research topic in operations research. In order to gain deeper understanding of the VRP this work uses knowledge graph comprehensively analyze and summarize literature related from 1959 2022 Web Science (WoS) database. Firstly, according basic statistical information literature, annual publications, authors, their institutions countries, keyword co-occurrence, co-citation network are analyzed generalize on predict its future development trend. results show that, past 60 years, there have abundant changes VRP. United States China made most important contributions field According WoS retrieval classification methods, models solutions classified, model solving algorithms divided into exact algorithms, heuristic metaheuristic hyper-heuristic machine learning, etc. that computing technology plays an role dynamic VRP, deep reinforcement directions optimization. can provide help guidance for beginners scholars outside industry understand hotspots

Язык: Английский

Процитировано

12
Integrating Machine Learning Into Vehicle Routing Problem: Methods and Applications DOI Creative Commons
Reza Shahbazian, Luigi Di Puglia Pugliese, Francesca Guerriero

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 93087 - 93115

Опубликована: Янв. 1, 2024

The vehicle routing problem (VRP) and its variants have been intensively studied by the operational research community. existing surveys majority of published articles tackle traditional solutions, including exact methods, heuristics, meta-heuristics. Recently, machine learning (ML)-based methods applied to a variety combinatorial optimization problems, specifically VRPs. strong trend using ML in VRPs gap literature motivated us review state-of-the-art. To provide clear understanding ML-VRP landscape, we categorize related studies based on their applications/constraints technical details. We mainly focus reinforcement (RL)-based approaches because importance literature, while also address non RL-based methods. cover both theoretical practical aspects clearly addressing trends, gap, limitations advantages ML-based discuss some potential future directions.

Язык: Английский

Процитировано

4
Multi-task differential evolution algorithm with dynamic resource allocation: A study on e-waste recycling vehicle routing problem DOI
Ying Hou, Yanjie Shen, Honggui Han

и другие.

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 92, С. 101806 - 101806

Опубликована: Янв. 2, 2025

Язык: Английский

Процитировано

0
Green Vehicle Routing Problem Optimization for LPG Distribution: Genetic Algorithms for Complex Constraints and Emission Reduction DOI Open Access
Nur Indrianti, Raden Achmad Chairdino Leuveano, Salwa Hanim Abdul‐Rashid

и другие.

Sustainability, Год журнала: 2025, Номер 17(3), С. 1144 - 1144

Опубликована: Янв. 30, 2025

This study develops a Green Vehicle Routing Problem (GVRP) model to address key logistics challenges, including time windows, simultaneous pickup and delivery, heterogeneous vehicle fleets, multiple trip allocations. The incorporates emissions-related costs, such as carbon taxes, encourage sustainable supply chain operations. Emissions are calculated based on the total shipment weight travel distance of each vehicle. objective is minimize operational costs while balancing economic efficiency environmental sustainability. A Genetic Algorithm (GA) applied optimize routing allocation, enhancing reducing costs. Liquid Petroleum Gas (LPG) distribution case in Yogyakarta, Indonesia, validates model’s effectiveness. results show significant cost savings compared current route planning methods, alongside slight increase carbon. sensitivity analysis was conducted by testing with varying numbers stations, revealing its robustness impact station density solution quality. By integrating taxes detailed emission calculations into function, GVRP offers practical for real-world challenges. provides valuable insights achieving cost-effective operations advancing green practices.

Язык: Английский

Процитировано

0