Innovative Hybrid Algorithm for Solving Vehicle Routing Problem with Time Window

Document Type : Research Note

Authors

1 Associate Professor, Civil Engineering Department, Faculty of Engineering, University of Zanjan, Zanjan, Iran

2 MSc. Graduated of Transportation Planning, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran.

3 Associate Professor, Department of Railway Engineering and Transportation Planning, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran.

10.24200/j30.2024.63335.3268

Abstract

Efficient transportation of goods is crucial for cost reduction, improved delivery time, and enhanced service quality. Advanced logistics systems analyze data to find the most efficient routes. This minimizes fuel consumption and decreases transportation costs. The Vehicle Routing Problem with Time Window Constraints (VRPTW) is a classic optimization problem in the field of operations research and logistics. It is a challenging optimization problem in logistics, classified as NP-hard. Hybrid approaches combine multiple optimization techniques to improve the quality and efficiency of solutions. This paper presents a hybrid cat-swarming algorithm that utilizes genetic operators to effectively address the VRPTW problem. The goal is to determine the optimal routes for the vehicles, considering both the vehicle capacity constraints and the time window constraints at each customer location. In this paper the objective function of the algorithm aims to minimize both the total distance traveled and the number of vehicles utilized, ensuring efficient and cost-effective routing. The hybrid cat swarming algorithm proposed in this study offers a novel approach to tackle the challenges posed by the VRPTW problem. By integrating genetic operators such as crossover and mutation, the algorithm enhances performance and improves the quality of solutions. Its primary objective of minimizing total distance and vehicle usage guarantees efficient and economically viable routing strategies. To evaluate the effectiveness of the algorithm, it was tested using a simulated dataset of salmon samples as a benchmark. For samples comprising 50 customers, an improvement of up to 48 to 59 percent in previous response rates has been achieved. For samples comprising 100 customers, optimal global responses, as obtained from previous articles, have been observed in several instances. The proposed algorithm is suitable for transportation and logistics systems with limited customers and leads to cost reduction, improved delivery times, and increased service quality.

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References

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