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Iterated local search algorithm for solving the orienteering problem with soft time windows

In this paper we study the orienteering problem with time windows (OPTW) and the impact of relaxing the time windows on the profit collected by the vehicle. The way of relaxing time windows adopted in the orienteering problem with soft time windows (OPSTW) that we study in this research is a late se...

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Detalles Bibliográficos
Autores principales: Aghezzaf, Brahim, Fahim, Hassan El
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5061708/
https://www.ncbi.nlm.nih.gov/pubmed/27795923
http://dx.doi.org/10.1186/s40064-016-3440-6
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author Aghezzaf, Brahim
Fahim, Hassan El
author_facet Aghezzaf, Brahim
Fahim, Hassan El
author_sort Aghezzaf, Brahim
collection PubMed
description In this paper we study the orienteering problem with time windows (OPTW) and the impact of relaxing the time windows on the profit collected by the vehicle. The way of relaxing time windows adopted in the orienteering problem with soft time windows (OPSTW) that we study in this research is a late service relaxation that allows linearly penalized late services to customers. We solve this problem heuristically by considering a hybrid iterated local search. The results of the computational study show that the proposed approach is able to achieve promising solutions on the OPTW test instances available in the literature, one new best solution is found. On the newly generated test instances of the OPSTW, the results show that the profit collected by the OPSTW is better than the profit collected by the OPTW.
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spelling pubmed-50617082016-10-28 Iterated local search algorithm for solving the orienteering problem with soft time windows Aghezzaf, Brahim Fahim, Hassan El Springerplus Research In this paper we study the orienteering problem with time windows (OPTW) and the impact of relaxing the time windows on the profit collected by the vehicle. The way of relaxing time windows adopted in the orienteering problem with soft time windows (OPSTW) that we study in this research is a late service relaxation that allows linearly penalized late services to customers. We solve this problem heuristically by considering a hybrid iterated local search. The results of the computational study show that the proposed approach is able to achieve promising solutions on the OPTW test instances available in the literature, one new best solution is found. On the newly generated test instances of the OPSTW, the results show that the profit collected by the OPSTW is better than the profit collected by the OPTW. Springer International Publishing 2016-10-12 /pmc/articles/PMC5061708/ /pubmed/27795923 http://dx.doi.org/10.1186/s40064-016-3440-6 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Aghezzaf, Brahim
Fahim, Hassan El
Iterated local search algorithm for solving the orienteering problem with soft time windows
title Iterated local search algorithm for solving the orienteering problem with soft time windows
title_full Iterated local search algorithm for solving the orienteering problem with soft time windows
title_fullStr Iterated local search algorithm for solving the orienteering problem with soft time windows
title_full_unstemmed Iterated local search algorithm for solving the orienteering problem with soft time windows
title_short Iterated local search algorithm for solving the orienteering problem with soft time windows
title_sort iterated local search algorithm for solving the orienteering problem with soft time windows
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5061708/
https://www.ncbi.nlm.nih.gov/pubmed/27795923
http://dx.doi.org/10.1186/s40064-016-3440-6
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