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Metaheuristic and hybrid algorithms for multi-objective vehicle routing problems

Gkouveris Konstantinos

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URI: http://purl.tuc.gr/dl/dias/04A84D47-8619-4650-9594-1BA739F327C1
Year 2024
Type of Item Diploma Work
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Bibliographic Citation Konstantinos Gkouveris, "Metaheuristic and hybrid algorithms for multi-objective vehicle routing problems", Diploma Work, School of Production Engineering and Management, Technical University of Crete, Chania, Greece, 2024 https://doi.org/10.26233/heallink.tuc.101407
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Summary

This thesis attempts to contribute to the understanding and practical application of optimization algorithms in the field of operational research and logistics. By applying, evaluating and comparing the results of a variety of heuristic and meta-heuristic algorithms, as well as their hybridized versions, useful information will emerge on their effectiveness in dealing with complex routing problems. Firstly, a number of metaheuristic algorithms (tabu search, simulated annealing, iterated local search, ant colony optimization, etc.) that are usually applied for solving single-objective optimization problems is presented. Then, some multi-objective optimization algorithms (MOTS, NSGA-2, SPEA-2) are presented and subsequently, customized hybrid approaches are proposed that combine at least two of the above techniques in order to achieve better solutions and/or faster convergence to the optimal Pareto frontier. These hybrid algorithms are applied to solve two different multi-objective vehicle routing problems to serve 200 customers, under constraints. Finally, the results are compared to the results that are produced by the application of a single metaheuristic algorithm.

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