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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Optimizing COVID-19 medical waste management using goal and robust possibilistic programming

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 131, С. 107838 - 107838

Опубликована: Янв. 6, 2024

Язык: Английский

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The Future of Last-Mile Delivery: Lifecycle Environmental and Economic Impacts of Drone-Truck Parallel Systems

Drones, Год журнала: 2025, Номер 9(1), С. 54 - 54

Опубликована: Янв. 14, 2025

With rapid advancements in unmanned aerial vehicle (UAV) technology, its integration into logistics operations has emerged as a promising solution for improving efficiency and sustainability. Among the emerging solutions, collaborative delivery model involving drones trucks addresses last-mile challenges by leveraging complementary strengths of both modes transport. However, evaluating environmental economic impacts this transportation mode requires systematic framework to capture unique characteristics minimize costs. This paper investigates Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP) evaluate sustainability drone-truck system. Specifically, mathematical system is developed optimize joint operations. Environmental are assessed using comprehensive Life Cycle Assessment (LCA), including emissions operational noise, while Cost Analysis (LCCA) quantifies performance across five cost dimensions. Sensitivity analysis explores factors such density, traffic congestion, wind conditions. Results show that, compared electric fleet, proposed achieves an approximate 20% reduction carbon emissions, delivering 20–30% relative fuel truck fleet. Drones’ short-distance deliveries alleviates trucks’ load, cutting study offers practical insights recommendations implementing parallel systems, particularly high-demand density areas.

Язык: Английский

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The two-echelon truck-unmanned ground vehicle routing problem with time-dependent travel times

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2025, Номер 194, С. 103954 - 103954

Опубликована: Янв. 8, 2025

Язык: Английский

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Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones

Applied Sciences, Год журнала: 2025, Номер 15(4), С. 2207 - 2207

Опубликована: Фев. 19, 2025

This paper addresses the time-dependent vehicle routing problem with drones in vehicle-restricted zones and no-fly (TDVRPD-VRZ-NFZ). The optimization model considers impacts of zones, road networks on delivery paths. objective is to minimize total cost, including dispatch costs, energy consumption costs for vehicles drones, time-window penalty costs. verified correctness using Gurobi. In response problem’s characteristics, a hybrid genetic algorithm variable neighborhood search learning mechanism (HGAVNS-LM) proposed solve problem. starts by generating initial population combination logistic mapping reverse learning. It then improves operators optimize population. To improve algorithm’s performance, an individual elite archive used knowledge learning, self-learning established dynamically adjust key parameters. solution obtained HGAVNS-LM shows deviation −0.2% −0.3% compared Gurobi, but it saves 99.68% solving time. Compared algorithm, improvement rates are 5.1% 13.0%, respectively. Through analysis multiple sets test cases, concluded that time-varying networks, different detour rules all affect plans. research results provide more scientific theoretical basis logistics companies customize solutions.

Язык: Английский

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Sustainable decisions in a ridesharing system with a tri-objective optimization approach

Transportation Research Part D Transport and Environment, Год журнала: 2023, Номер 125, С. 103958 - 103958

Опубликована: Ноя. 17, 2023

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 fuzzy programming model for decentralization and drone utilization in urban humanitarian relief chains

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2025, Номер 195, С. 103949 - 103949

Опубликована: Янв. 20, 2025

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A Survey on Variable Neighborhood Search for Sustainable Logistics

Algorithms, Год журнала: 2025, Номер 18(1), С. 38 - 38

Опубликована: Янв. 10, 2025

Sustainable logistics aims to balance economic efficiency, environmental responsibility, and social well-being in supply chain operations. This study explores the use of Variable Neighborhood Search (VNS), a metaheuristic optimization method, addressing sustainable challenges provides insights into potential it has support them by delivering efficient solutions that align with global sustainability goals. The review identifies key trends, including significant increase research since 2019, strong focus on routing, scheduling, location problems. Hybrid approaches, combining VNS other methods, multiobjective address trade-offs between goals are prominent. most frequently applied versions closely those commonly used broader literature, reflecting similar adoption proportions. In recent years, noticeable studies incorporating adaptation mechanisms frameworks emerged. trend is largely driven growing influence Artificial Intelligence approaches across numerous fields science engineering, highlighting need for more dynamic intelligent techniques. However, important gaps remain. These include limited consideration uncertainty systems, underrepresentation sustainability, lack standardized benchmarks comparing results. Future work should these explore emerging applications.

Язык: Английский

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