Research on Vehicle Routing Problem with Time Windows Based on Improved Genetic Algorithm and Ant Colony Algorithm

The Vehicle Routing Problem with Time Window (VRPTW) is of crucial importance in modern societies, where the aim is to optimize resource utilization, reduce costs and address constraints such as time and vehicle capacity. Traditional genetic algorithms often face premature convergence and slow speed...

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Vydané v:Electronics (Basel) Ročník 14; číslo 4; s. 647
Hlavní autori: Chen, Guangqiao, Gao, Jun, Chen, Daozheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.02.2025
Predmet:
Ant colony optimization
Chromosomes
Constraints
Customer services
Deviation
Distribution centers
Efficiency
Fines & penalties
Genetic algorithms
Heuristic
Mathematical models
Optimization
R&D
Research & development
Resource utilization
Searching
Travel
Traveling salesman problem
Vehicle routing
Vehicles
Windows (intervals)
ISSN:2079-9292, 2079-9292
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Shrnutí:The Vehicle Routing Problem with Time Window (VRPTW) is of crucial importance in modern societies, where the aim is to optimize resource utilization, reduce costs and address constraints such as time and vehicle capacity. Traditional genetic algorithms often face premature convergence and slow speed in solving such problems. This paper proposes an Improved Genetic Ant Colony Optimization (IGA-ACO) algorithm to efficiently solve VRPTW. The algorithm combines the strengths of a genetic algorithm with the Generalized Variable Neighborhood Search (GVNS) and Ant Colony Optimization (ACO), aiming to minimize the total cost and optimize balance. The Solomon insertion heuristic is employed to initialize the population and enhance local search capabilities, while the two-population structure improves global search performance by exchanging the optimal solutions between the two populations, helping to avoid local optima. Experiments on the Solomon benchmark dataset show that the IGA-ACO algorithm matches the Best Known Solution (BKS) in Class C instances, reduces vehicle usage by 24.45% in Class R, with a travel distance deviation of 9.19%, and slightly reduces vehicle usage by 0.19% in Class RC, with a travel distance deviation of 7.05%. These results demonstrate the algorithm’s effectiveness in optimizing vehicle routing, particularly under complex constraints, and its competitive advantage over other methods.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14040647