A genetic-based approach for vehicle routing problem with fuzzy alpha-cut constraints

In today’s business environment, logistics and supply chain management are especially important for the timely delivery of materials and goods. Delivery must not only be fast, it also needs to be performed within a specific time frame. Therefore, this study examines a vehicle routing problem (VRP) t...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 29; H. 2; S. 1169 - 1189
Hauptverfasser: Kang, He-Yau, Lee, Amy H. I.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
Springer Nature B.V
Schlagworte:
Application of Soft Computing
Artificial Intelligence
Computational Intelligence
Constraints
Control
Customer satisfaction
Customer services
Design of experiments
Distribution costs
Energy consumption
Engineering
Fuzzy sets
Genetic algorithms
Heuristic
Linear programming
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Multiple objective analysis
Mutation
Objectives
Optimization
Pareto optimum
Performance evaluation
Robotics
Sensitivity analysis
Supply chains
Taxonomy
Travel
Travel time
Vehicle routing
Vehicles
Windows (intervals)
Fuzzy alpha-cut constraints
Time window
Improved genetic algorithm
Customer satisfaction
Vehicle routing problem
ISSN:1432-7643, 1433-7479
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Zusammenfassung:In today’s business environment, logistics and supply chain management are especially important for the timely delivery of materials and goods. Delivery must not only be fast, it also needs to be performed within a specific time frame. Therefore, this study examines a vehicle routing problem (VRP) that considers multiple goals and allows vehicles to reach their destinations within a time window with a crashed traveling time. Two objectives are considered, the minimization of total cost and the maximization of customer satisfaction. Firstly, a fuzzy multi-objective linear programming (FMOLP) model with alpha-cut technique and epsilon-constraint method is proposed to transform the multi-objective problem into a single-objective mathematical model, and then an improved genetic algorithm (IGA) is constructed to solve large-scale problems. The performances of the proposed methods are evaluated through several case studies. Design of experiments and sensitivity analysis are applied to evaluate the performance and robustness of IGA. The results show that both the FMOLP and IGA are effective, and the IGA can efficiently obtain a near-optimal solution.
Bibliographie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-025-10465-7