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A bi-objective study of the minimum latency problem
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 o...
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Published in: | Journal of heuristics 2019-06, Vol.25 (3), p.431-454 |
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container_title | Journal of heuristics |
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creator | Arellano-Arriaga, N. A. Molina, J. Schaeffer, S. E. Álvarez-Socarrás, A. M. Martínez-Salazar, I. A. |
description | 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. |
doi_str_mv | 10.1007/s10732-019-09405-0 |
format | article |
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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.</description><identifier>ISSN: 1381-1231</identifier><identifier>EISSN: 1572-9397</identifier><identifier>DOI: 10.1007/s10732-019-09405-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Approximation ; Artificial Intelligence ; Calculus of Variations and Optimal Control; Optimization ; Evolutionary algorithms ; Heuristic methods ; Management Science ; Mathematics ; Mathematics and Statistics ; Multiple objective analysis ; Operations Research ; Operations Research/Decision Theory ; Travel time</subject><ispartof>Journal of heuristics, 2019-06, Vol.25 (3), p.431-454</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Journal of Heuristics is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-40302930ef56a7f03338afc41b37c811591d6c70522630251e71248d0e92eb523</citedby><cites>FETCH-LOGICAL-c319t-40302930ef56a7f03338afc41b37c811591d6c70522630251e71248d0e92eb523</cites><orcidid>0000-0002-7002-2753</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2172482434/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2172482434?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11687,27923,27924,36059,44362,74666</link.rule.ids></links><search><creatorcontrib>Arellano-Arriaga, N. A.</creatorcontrib><creatorcontrib>Molina, J.</creatorcontrib><creatorcontrib>Schaeffer, S. E.</creatorcontrib><creatorcontrib>Álvarez-Socarrás, A. M.</creatorcontrib><creatorcontrib>Martínez-Salazar, I. A.</creatorcontrib><title>A bi-objective study of the minimum latency problem</title><title>Journal of heuristics</title><addtitle>J Heuristics</addtitle><description>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.</description><subject>Approximation</subject><subject>Artificial Intelligence</subject><subject>Calculus of Variations and Optimal Control; Optimization</subject><subject>Evolutionary algorithms</subject><subject>Heuristic methods</subject><subject>Management Science</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Multiple objective analysis</subject><subject>Operations Research</subject><subject>Operations Research/Decision Theory</subject><subject>Travel time</subject><issn>1381-1231</issn><issn>1572-9397</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp9kEFLxDAQhYMouK7-AU8Bz9GZTNM0x2VRV1jwoufQZlNt2bZr0gr7741W8OZp5vC99-Bj7BrhFgH0XUTQJAWgEWAyUAJO2AKVlsKQ0afppwIFSsJzdhFjCwCmULRgtOJVI4aq9W5sPj2P47Q78qHm47vnXdM33dTxfTn63h35IQzV3neX7Kwu99Ff_d4le324f1lvxPb58Wm92gpHaEaRAYE0BL5WealrIKKirF2GFWlXICqDu9xpUFLmiVToNcqs2IE30ldK0pLdzL1p92PycbTtMIU-TVqJOqEyoyxRcqZcGGIMvraH0HRlOFoE-y3HznJskmN_5FhIIZpDMcH9mw9_1f-kvgDdsGQu</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Arellano-Arriaga, N. 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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.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10732-019-09405-0</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0002-7002-2753</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Approximation Artificial Intelligence Calculus of Variations and Optimal Control Optimization Evolutionary algorithms Heuristic methods Management Science Mathematics Mathematics and Statistics Multiple objective analysis Operations Research Operations Research/Decision Theory Travel time |
title | A bi-objective study of the minimum latency problem |
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