The combinatorial optimization, as a scientific paradigm, has a significant influence on the increasement of the effectiveness of any logistic decision, with particular emphasis on vehicle routing decisions. Although these decisions have been widely studied, research on vehicle routing optimization mostly focused on the empirical application of the solution methods (exact or approximate), already established in the scientific literature, or on providing new methods for such purposes. However, in both cases, the greatest contributions are addressed to the design, improvement and application of optimization methods, a priori unknowing, how effective these methods can be, considering the complexity feature of the problem in a multivariate context. Therefore, the general methodologies to carry out the optimization process for such decisions lack of an integrative approach, which allows to check the relevancy degree of the proposed methods. To avoid the mentioned inadequacies, a conceptual model and procedure have been proposed in this thesis. Both proposals involved the assistance of decision-making in vehicle routing optimization. In this sense, the major research contributions are summarized in the conception of the optimization process into three stages, the design and modification of meta-heuristic algorithms based on Ant Colony Optimization (ACO) and the application of some robust statistic techniques in decision-making. Finally, the proposed algorithms were successfully applied to a realistic case study: route planning for the repair of electrical breakdowns in Cuban territory, set up in the city of Santa Clara.
Algorithmic assistance to the optimization process in vehicle routing problems
2013
162 Seiten, Bilder, Tabellen, 265 Quellen
Theses
English
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