This paper introduces a new bi-objective minimum latency problem with profit collection, where routes must be constructed in order to maximize the collected profit and to minimize the total latency. These objectives are usually conflicting. Thus, considering some important features, as the segmentation of the customers into two classes, mandatory and optional, and the presence of uncertain travel times, we follow a bi-objective approach, aiming to compute a set of Pareto-optimal alternatives with different trade-offs for a decision-maker to choose from. In order to address this computationally challenging problem, we propose a Multi-Objective Iterated Local Search. Computational results confirm the practicality of the algorithm, in terms of the quality of the solutions, and its computational efficiency in terms of time spent. We conclude that the algorithm finds good-quality solutions for small and medium-size instances.

The Bi-objective Minimum Latency Problem with Profit Collection and Uncertain Travel Times / Bruni, M. E.; Khodaparasti, S.; Nucamendi, S. M.. - 1:(2020), pp. 109-118. (Intervento presentato al convegno 9th International Conference on Operations Research and Enterprise Systems ICORES).

The Bi-objective Minimum Latency Problem with Profit Collection and Uncertain Travel Times

Bruni M. E.;Khodaparasti S.;
2020

Abstract

This paper introduces a new bi-objective minimum latency problem with profit collection, where routes must be constructed in order to maximize the collected profit and to minimize the total latency. These objectives are usually conflicting. Thus, considering some important features, as the segmentation of the customers into two classes, mandatory and optional, and the presence of uncertain travel times, we follow a bi-objective approach, aiming to compute a set of Pareto-optimal alternatives with different trade-offs for a decision-maker to choose from. In order to address this computationally challenging problem, we propose a Multi-Objective Iterated Local Search. Computational results confirm the practicality of the algorithm, in terms of the quality of the solutions, and its computational efficiency in terms of time spent. We conclude that the algorithm finds good-quality solutions for small and medium-size instances.
2020
978-989-758-396-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2980526