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Time-constrained maximal covering routing problem

We introduce the time-constrained maximal covering routing problem (TCMCRP), as a generalization of the covering salesman problem. In this problem, we are given a central depot, a set of facilities and several customers which are located within a pre-determined coverage distance of available facilit...

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Detalles Bibliográficos
Autores principales: Amiri, Afsaneh, Salari, Majid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079968/
https://www.ncbi.nlm.nih.gov/pubmed/32214572
http://dx.doi.org/10.1007/s00291-018-0541-3
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author Amiri, Afsaneh
Salari, Majid
author_facet Amiri, Afsaneh
Salari, Majid
author_sort Amiri, Afsaneh
collection PubMed
description We introduce the time-constrained maximal covering routing problem (TCMCRP), as a generalization of the covering salesman problem. In this problem, we are given a central depot, a set of facilities and several customers which are located within a pre-determined coverage distance of available facilities. Each facility can supply the demand of some customers which are within its coverage radius. Starting from the depot, the goal is to maximize the total number of covered customers, by constructing a set of p length constraint Hamiltonian cycles. We have proposed a mixed integer linear programming model and three heuristic algorithms, namely iterated local search (ILS), tabu search (TS) and variable neighborhood search (VNS), to solve the problem. Extensive computational tests on this problem and some of its variants clearly indicate the effectiveness of the developed solution methods.
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spelling pubmed-70799682020-03-23 Time-constrained maximal covering routing problem Amiri, Afsaneh Salari, Majid OR Spectr Regular Article We introduce the time-constrained maximal covering routing problem (TCMCRP), as a generalization of the covering salesman problem. In this problem, we are given a central depot, a set of facilities and several customers which are located within a pre-determined coverage distance of available facilities. Each facility can supply the demand of some customers which are within its coverage radius. Starting from the depot, the goal is to maximize the total number of covered customers, by constructing a set of p length constraint Hamiltonian cycles. We have proposed a mixed integer linear programming model and three heuristic algorithms, namely iterated local search (ILS), tabu search (TS) and variable neighborhood search (VNS), to solve the problem. Extensive computational tests on this problem and some of its variants clearly indicate the effectiveness of the developed solution methods. Springer Berlin Heidelberg 2018-12-04 2019 /pmc/articles/PMC7079968/ /pubmed/32214572 http://dx.doi.org/10.1007/s00291-018-0541-3 Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2018 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Regular Article
Amiri, Afsaneh
Salari, Majid
Time-constrained maximal covering routing problem
title Time-constrained maximal covering routing problem
title_full Time-constrained maximal covering routing problem
title_fullStr Time-constrained maximal covering routing problem
title_full_unstemmed Time-constrained maximal covering routing problem
title_short Time-constrained maximal covering routing problem
title_sort time-constrained maximal covering routing problem
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079968/
https://www.ncbi.nlm.nih.gov/pubmed/32214572
http://dx.doi.org/10.1007/s00291-018-0541-3
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