Optimal pole location model in transportation networks using meta-heuristic algorithms

Articles in Press

Document Type : Original Article

Authors

1 Professor-School of Civil Engineering-Iran University of Science and Technology

2 Islamic Azad University, South Tehran Branch

3 School of Civil Engineering, Iran University of Science and Technology

10.22034/tri.2023.402570.3167

Abstract

In this study, a two-objective mathematical model including minimizing costs and minimizing the transport time between each pair of nodes was presented and evaluated using two multi-objective genetic algorithms and harmonic search. On the other hand, due to the fact that the purpose is to apply uncertain conditions, the demand parameters, operating costs and additional capacity building costs were also considered as uncertain, and the uncertain parameters were considered as trapezoidal fuzzy numbers. The results obtained from solving problems with different dimensions based on two criteria of cost and time of movement between nodes showed the high performance of the genetic algorithm compared to the search for harmony. In order to compare the numerical results, it was found that in the small-scale model, the multi-objective genetic algorithm improved by 8.7 percent of the total costs in the system and was able to respond 9% faster than the harmonic search algorithm. Also, due to the solution of the model in medium dimensions, the multi-objective genetic algorithm has improved by 6% compared to the harmonic search algorithm in order to determine the cost of the entire network. At the time of network service, the genetic algorithm was 1.5% faster than the harmonic search algorithm. Finally, by examining the large-scale model, it was found that the multi-objective genetic algorithm with 2 percent improvement over the harmonic search algorithm reduced the transportation network costs more and by 2 percent less time.

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Journal of Transportation Research

Articles in Press, Accepted Manuscript
Available Online from 06 November 2023

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