Options
The bi-objective minimum latency problem with profit collection and uncertain travel times
Journal
Proceedings of the 9th International Conference on Operations Research and Enterprise Systems (ICORES 2020)
Publisher
SCITEPRESS
Date Issued
2020
Author(s)
Type
Resource Types::text::journal::journal article
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.
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.