Optimization refers to finding the optimal value or best possible option. With optimization, the resource utilization can be planned to be the most effective and cost-efficient, especially in the vehicles sector, where cost and quality are both important factors. However, when dealing with complex systems, findings the best solution is considered almost impossible due to the time and the resources consumed. Therefore, optimization algorithms are used to find an optimum solution as much as possible within a relatively short time. The optimization algorithms evolved from conventional mathematical approaches to modern developed methods that use heuristic and metaheuristic approaches. Within the frame of this paper, the authors present a study that describes the effectiveness of three metaheuristic algorithms and show a municipal waste collection case study in Miskolc. After an introduction and theoretical background about the optimization algorithms development, the authors describe three metaheuristic algorithms: genetic, particle swarm, and simulated annealing. Five benchmarks are used to compare the results and consumed time for the mentioned algorithms. A Traveling Salesman Problem case study is solved to find the shortest real route of twenty locations for a municipal waste collection system in Miskolc city center by using the analyzed three algorithms. After that. The results are compared with a random solution. Particle swarm showed the best results, while simulated annealing was the fastest algorithm in the average execution time.


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    Title :

    Vehicle Routing for Municipal Waste Collection Systems: Analysis, Comparison and Application of Heuristic Methods


    Additional title:

    Lect.Notes Mechanical Engineering


    Contributors:

    Conference:

    Vehicle and Automotive Engineering ; 2022 ; MISKOLC, Hungary September 08, 2022 - September 09, 2022



    Publication date :

    2022-09-10


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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