A Quick Pheromone Matrix Adaptation Ant Colony Optimization for Dynamic Customers in the Vehicle Routing Problem DOI Creative Commons

Yuxin Liu,

Zhitian Wang,

Jin Liu

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(7), P. 1167 - 1167

Published: July 11, 2024

The path planning problem is an important issue in maritime search and rescue. This paper models the as a dynamic vehicle routing problem. It first designs generator that transforms existing benchmark sets for static into scenarios. Subsequently, it proposes effective Dynamic Ant Colony Optimization (DACO) algorithm, whose novelty lies dynamically adjusts pheromone matrix to efficiently handle customers’ changes. Moreover, DACO incorporates simulated annealing increase population diversity employs local operator dedicated route modification continuous performance maximization of route. experimental results demonstrated proposed outperformed approaches generating better routes across various sets. Specifically, achieved significant improvements cost, serviced customer quantity, adherence time window requirements. These highlight superiority problem, providing solution similar problems.

Language: Английский

Multi-strategy ant colony optimization with k-means clustering algorithm for capacitated vehicle routing problem DOI
Zhaojun Zhang,

Simeng Tan,

Jia-Jia Qin

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

Citations

0
Collaboration and Resource Sharing for the Multi-Depot Electric Vehicle Routing Problem with Time Windows and Dynamic Customer Demands DOI Open Access
Yong Wang, Can Chen,

Yuanhan Wei

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2700 - 2700

Published: March 18, 2025

With increasingly diverse customer demands and the rapid growth of new energy logistics industry, establishing a sustainable responsive network is critical. In multi-depot network, adopting collaborative distribution resource sharing can significantly improve operational efficiency. This study proposes collaboration for electric vehicle (EV) routing problem with time windows dynamic demands. A bi-objective optimization model formulated to minimize total operating costs number EVs. To solve model, novel hybrid algorithm combining mini-batch k-means clustering an improved multi-objective differential evolutionary (IMODE) proposed. integrates genetic operations non-dominated sorting operation enhance solution quality. The strategies dynamically inserting charging stations are embedded within identify Pareto-optimal solutions effectively. algorithm’s efficacy applicability verified through comparisons algorithm, particle swarm ant colony optimization, tabu search. Additionally, case company in Chongqing City, China demonstrates that proposed method reduces improves Sensitivity analysis considering different demand response modes provides insights reducing enhancing findings offer essential promoting environmentally resource-efficient city.

Language: Английский

Citations

0
Flexible Capacitated Vehicle Routing Problem Solution Method Based on Memory Pointer Network DOI Creative Commons
Enliang Wang,

Yue Cai,

Zhixin Sun

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(7), P. 1061 - 1061

Published: March 25, 2025

In real-world logistics scenarios, the complexities often surpass what traditional Capacitated Vehicle Routing Problem (CVRP) models can effectively address. For instance, when there is an excess of goods and limited vehicles, CVRP frequently fail to yield feasible solutions. Additionally, time sensitivity large scale vehicles in practical scenarios present significant challenges for efficient problem-solving. This underscores urgent need develop a novel model that better suited enhances scalability CVRP. To address these limitations, we propose flexible model, referred as Flexible CVRP, which modifies optimization objectives constraints. allows provide sensible solution even no exists sense. tackle posed by large-scale problems, leverage Memory Pointer Network (MemPtrN). approach enables modeling strategies, offering strong generalization capabilities mitigating explosive growth complexity some extent. Compared commonly used heuristic algorithms, our method achieves superior quality problems. Specifically, addressing MemPtrN outperforms Google’s OR-Tools solver, enhanced evolutionary other reinforcement learning methods terms both speed quality.

Language: Английский

Citations

0
Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA DOI Creative Commons
Feng Cheng,

Shuchun Jia,

Wei Gao

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(7), P. 3061 - 3061

Published: April 5, 2024

In order to tackle the issue of carbon emissions in logistics and distribution, a vehicle routing model was proposed with aim minimizing overall cost, which includes vehicle’s fixed transportation costs, emission costs. An enhanced genetic algorithm, based on modified Nearest Neighbor Construction (NNC) method, developed solve this model. A comparative analysis conducted using Solomon dataset study impact optimization, comparing scenarios without considering The research findings revealed that Improved NNC (INNC) method exhibited faster convergence compared random generation insertion methods. Despite slight increase 0.5127% cost when factoring there decrease 4.6914% costs 0.3578% total cost. These results offer theoretical insights empirical evidence inform development models for industry context low-carbon economy.

Language: Английский

Citations

3
Double-assistant evolutionary multitasking algorithm for enhanced electric vehicle routing with backup batteries and battery swapping stations DOI
Yanguang Cai, Yanlin Wu, Chuncheng Fang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121600 - 121600

Published: Sept. 17, 2023

Language: Английский

Citations

7
Standardized validation of vehicle routing algorithms DOI Creative Commons
Tomasz Jastrząb, Michał Myller, Łukasz Tulczyjew

et al.

Applied Intelligence, Journal Year: 2024, Volume and Issue: 54(2), P. 1335 - 1364

Published: Jan. 1, 2024

Abstract Designing routing schedules is a pivotal aspect of smart delivery systems. Therefore, the field has been blooming for decades, and numerous algorithms this task have proposed various formulations rich vehicle problems. There is, however, an important gap in state art that concerns lack established widely-adopted approach toward thorough verification validation such practical scenarios. We tackle issue propose comprehensive can shed more light on functional non-functional abilities solvers. Additionally, we novel similarity metrics to measure distance between be used verifying convergence randomized techniques. To reflect aspects intelligent transportation systems, introduce algorithm elaborating solvable benchmark instances any formulation, alongside set quality help quantify real-life characteristics as their profitability. The experiments prove flexibility our through utilizing it NP-hard pickup problem with time windows, present qualitative, quantitative, statistical analysis scenarios which understand capabilities investigated believe efforts will step critical consistent evaluation emerging (and other) solvers, allow community easier confront them, thus ultimately focus most promising research avenues are determined quantifiable traceable manner.

Language: Английский

Citations

2
Optimizing electric vehicle routing with nonlinear charging and time windows using improved differential evolution algorithm DOI
Jiawen Deng, Jihui Zhang, Shengxiang Yang

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(4), P. 5423 - 5458

Published: Jan. 28, 2024

Language: Английский

Citations

2
Genetic Algorithm Optimization with Selection Operator Decider DOI Creative Commons
Büşra Meni̇z,

Fatma Tiryaki

Arabian Journal for Science and Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: May 2, 2024

Language: Английский

Citations

2
Serial multilevel-learned differential evolution with adaptive guidance of exploration and exploitation DOI
Jiatianyi Yu, Kaiyu Wang, Zhenyu Lei

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124646 - 124646

Published: July 4, 2024

Language: Английский

Citations

2
An Adaptive Nutcracker Optimization Approach for Distribution of Fresh Agricultural Products with Dynamic Demands DOI Creative Commons
Daqing Wu, Rong Yan, Hongtao Jin

et al.

Agriculture, Journal Year: 2023, Volume and Issue: 13(7), P. 1430 - 1430

Published: July 19, 2023

In the operational, strategic and tactical decision-making problems of agri-food supply chain, perishable nature commodities can represent a particular complexity problem. It is, therefore, appropriate to consider decision support tools that take into account characteristics products, needs requirements producers, sellers consumers. This paper presents green vehicle routing model for fresh agricultural product distribution designs an adaptive hybrid nutcracker optimization algorithm (AH-NOA) based on k-means clustering solve process, AH-NOA uses CW increase population diversity adds genetic operators local search enhance global ability optimization. Finally, experimental data show proposed approaches effectively avoid optima, promote reduce total costs carbon emission costs.

Language: Английский

Citations

6