Abstract
We study a bi-objective problem called the Minimum Latency-Distance Problem (mldp) that aims to minimise travel time and latency of a single-vehicle tour designed to serve a set of client requests. This tour is a Hamiltonian cycle for which we aim to simultaneously minimise the total travel time of the vehicle and the total waiting time (i.e., latency) of the clients along the tour. This problem is relevant in contexts where both client satisfaction and company profit are prioritise. We propose two heuristic methods for approximating Pareto fronts for mldp: SMSA that is based on a classic multi-objective algorithm and EiLS that is based on a novel evolutionary algorithm with intelligent local search. We report computational experiments on a set of artificially generated problem instances using an exact method and the two proposed heuristics, comparing the obtained fronts in terms of various quality metrics.
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Acknowledgements
The first author thanks CONACyT (the Mexican National Council for Science and Technology) which supported her studies at UANL under the Scholarship Number 446316, as well as the AUIP (the Asociación Universitaria Iberoamericana de Postgrado) for the scholarship granted to conclude her studies in the University of Malaga. The second author thanks the research project CSO2016-75898-P from the Spanish of Ministry of Science and Innovation, which supports his research.
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Arellano-Arriaga, N.A., Molina, J., Schaeffer, S.E. et al. A bi-objective study of the minimum latency problem. J Heuristics 25, 431–454 (2019). https://doi.org/10.1007/s10732-019-09405-0
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DOI: https://doi.org/10.1007/s10732-019-09405-0