Modelling and Optimization of Asymmetric Vehicle Routing Problem Using Particle Swarm Optimization Algorithm

Various problems related to vehicle routing problem attract interest of researchers and industry. Specific optimization model and algorithm were developed to solve the problem. This intensive effort aims to reduce logistics costs and number of vehicle usage. In this context, most articles focus on d...

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Bibliographic Details
Main Authors: Muhamad Rozikin, Kamaluddin, M. F. F., Ab Rashid
Format: Conference or Workshop Item
Language:English
Published: Springer 2021
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/30960/1/modelling.pdf
http://umpir.ump.edu.my/id/eprint/30960/
https://doi.org/10.1007/978-981-15-9505-9
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Institution: Universiti Malaysia Pahang
Language: English
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Summary:Various problems related to vehicle routing problem attract interest of researchers and industry. Specific optimization model and algorithm were developed to solve the problem. This intensive effort aims to reduce logistics costs and number of vehicle usage. In this context, most articles focus on different optimization method approaches. Asymmetric vehicle routing problem (AVRP) appeared when the const of delivering and returning route using the same path were difference. It was used in practical applications to solve AVRP problems identified for specific application. This paper used a well-known metaheuristic optimization method, Particle Swarm Optimization (PSO) for solving AVRP models. To optimize AVRP, three optimization objectives were recommended; the total travelling time, efficiency of the route and the number of vehicles. This is to optimize the number of targets visited. The performance of PSO is evaluated by comparing its results with other popular metaheuristics. The computational experiment was conducted using five test problems with different sizes. The optimization results indicated that this algorithm able to offer good solutions with the best answer for the practical problem. Finally, this study shows that the algorithm can significantly reduce travel costs via number of bus needed to serve all the stop points.