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

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 219, P. 119654 - 119654

Published: Feb. 4, 2023

Language: Английский

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

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(21), P. 10295 - 10295

Published: Nov. 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.

Language: Английский

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A modelling framework to design an evacuation support system for healthcare infrastructures in response to major flood events

Progress in Disaster Science, Journal Year: 2022, Volume and Issue: 13, P. 100218 - 100218

Published: Jan. 1, 2022

Extreme weather events such as floods are predicted to become increasingly common and severe the climate changes. Effectively functioning hospitals critical a community's resilience adverse health impacts of events. Yet many have not been designed with extreme risks in mind built flood-prone areas, raising concerns about their ability support community healthcare needs when they eventuate. While considerable research has conducted on developing disaster responses maintain services face floods, there is paucity hospital evacuating planning. Addressing this gap research, paper explores current state-of-the-art evacuation transportation models identify key factors for more effective planning future flooding risks. A new modelling framework proposed, which addresses limitations existing proposes that should be incorporated into enhance growing flood due change.

Language: Английский

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A city logistics system for freight transportation: integrating information technology and operational research

Operational Research, Journal Year: 2022, Volume and Issue: 22(5), P. 5953 - 5982

Published: March 1, 2022

Language: Английский

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A systematic literature review of the vehicle routing problem in reverse logistics operations

Computers & Industrial Engineering, Journal Year: 2023, Volume and Issue: 177, P. 109011 - 109011

Published: Jan. 18, 2023

The increasing rate at which goods ranging from food products to electronic are consumed has led rapidly waste worldwide. This huge amount of poses an environmental threat but can have economic value. Thus, governments argue that this should be collected and included in recycling processes. However, collecting these wastes pose some logistical problems, such as the large amounts carbon emissions created need for transportation plans collect costly wastes. Many researchers industrial practitioners developed vehicle routing systems reverse logistics operations address model challenges. To date, few efforts been made systematically review models process. paper provides a literature 109 published articles international scientific journals related problem domain between 2000 2022. Six carefully designed questions proposed answered article regarding modelling approaches, solution techniques, variants, sustainability aspects, types, future research opportunities. According results review, important points were highlighted discussed.

Language: Английский

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An adaptive large neighborhood search for the multi-depot dynamic vehicle routing problem with time windows

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 191, P. 110122 - 110122

Published: April 8, 2024

Language: Английский

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Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

Applied Artificial Intelligence, Journal Year: 2024, Volume and Issue: 38(1)

Published: March 27, 2024

This paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows offer flexibility help route planners find more fuel-efficient routes ("green delivery"). Customers can rank their preferred as first, second, third. The optimization aims reduce tour costs, promote electromobility over fossil fuels, such diesel, meet customer preferences when possible affordable. study incorporates multi-objective three objectives, overall cost, use fuel, satisfaction. For problem, set realistic benchmark problems created four mainstream solvers applied Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, SMS-EMOA. These compared terms effectiveness achieving objectives minimizing travel promoting electromobility, meeting preferences. uses five different single-vehicle planning. Two major findings that selection metaheuristic make big difference algorithm performance. resulting 3-D fronts reveal nature this class problems: Interestingly, flexible windows, most users still be delivered only small concessions other objectives. However, using one window per user lead an increasingly drastic cost fuel consumption.

Language: Английский

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Connecting Cities: A Case Study on the Application of Morphological Shortest Paths

Symmetry, Journal Year: 2025, Volume and Issue: 17(1), P. 114 - 114

Published: Jan. 13, 2025

Navigatingdensely connected networks can be complex due to the different connection structures present within a network. No explicit algorithms are designed specifically for this navigation, so heuristic approaches and existing network systems often employed. However, task become computationally asymmetrical, as complexity of creating representation city is lower than involved in identifying set feasible paths combinatorial order. This paper extends applicability morphological compute shortest path smart cities, driven by size vital communication infrastructure. As well known, infrastructure changes dynamically, particularly with evolving continuous population growth. Consequently, efficient trajectories quickly obsolete. The challenge computing best respond more growing comes high computational complexity. presents an application that uses discrete algorithm through approach. Specifically, it seeks identify trajectory densely populated based on density graph. By incorporating into path-search algorithms, we define new family methods operate spaces representation, resulting have requirements. Other well-known applications context include delivery resources, such managing electrical power consumption or minimizing time delays resource delivery. essential but classified NP problem, making appropriate scenario applying proposed navigate dense highlights problem finding one potential introduced algorithm. aims optimal graph representing city’s real scenario. discussion compares contrasts proposal other established approaches, highlighting advantages characteristics method.

Language: Английский

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

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 911 - 911

Published: Feb. 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.

Language: Английский

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Designing efficient algorithms for logistics management: Optimizing time-constrained vehicle routing

Strategic Management, Journal Year: 2025, Volume and Issue: 00, P. 83 - 83

Published: Jan. 1, 2025

Background: City logistics is a critical component of urban economic development, as it optimizes supply chains, enhances customer satisfaction through reliable deliveries, and minimizes environmental impacts in densely populated areas. This field addresses various challenges, including traffic congestion, concerns, noise pollution, the crucial need for timely deliveries. Routing scheduling are central to operations, with modern software integrating time windows meet precise demands driven by detailed requirements operational efficiencies. Furthermore, advanced vehicle routing models now effectively simulate real-world factors such stochastic travel times, dynamic product demands. Purpose: paper aims develop an algorithm that decisions. Our approach extends dimension, considering times service within predefined windows. Study design/methodology/approach: The proposed structured execute iterative phases, aiming optimize key logistical objectives. In order generate competitive solutions, we seek minimize number vehicles utilized overall costs. evaluation solution space was conducted via Simulated Annealing. Findings/conclusions: performance algorithm, evaluated using Gehring Homberger benchmark instances 200 customers, demonstrates its effectiveness. successfully meets target required, associated costs on average 1% best solutions reported relevant literature. Limitations/future research: Given ongoing from decision-makers, future research endeavors will focus enhancing computational efficiency algorithm. Additionally, incorporating more time-related features, could further improve algorithm's real-time applicability.

Language: Английский

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