Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores
This paper introduces the Team Orienteering Problem with Time Windows and Partial Scores (TOPTW-PS),which is an extension of the Team Orienteering Problem with Time Windows (TOPTW). In the context of theTOPTW-PS, each node is associated with a set of scores with respect to a set of attributes. The o...
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sg-smu-ink.sis_research-54722019-11-28T07:45:12Z Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores YU, Vincent F. REDI, Perwira A. A. N. JEWPANYA, Parida GUNAWAN, Aldy This paper introduces the Team Orienteering Problem with Time Windows and Partial Scores (TOPTW-PS),which is an extension of the Team Orienteering Problem with Time Windows (TOPTW). In the context of theTOPTW-PS, each node is associated with a set of scores with respect to a set of attributes. The objective ofTOPTW-PS is to find a set of routes that maximizes the total score collected from a subset of attributes whenvisiting the nodes subject to the time budget and the time window at each visited node. We develop a mathematical model and propose a discrete version of the Particle Swarm Optimization (PSO), namely, the SelectiveDiscrete PSO (S-DPSO), to solve TOPTW-PS. The proposed S-DPSO uses four different movement schemes tomove a particle from its current position. The best movement scheme is selected to determine the next position ofthe particle. To evaluate the performance of the proposed S-DPSO algorithm, we first test S-DPSO on two variants of Orienteering Problem, namely, Team Orienteering Problem (TOP) and TOPTW. Experimental resultsshow that S-DPSO performs well in solving benchmark instances of TOP and TOPTW. In general, S-DPSO iscomparable to the state-of-the-art algorithms for these problems. We also apply the S-DPSO to solve 168 newlygenerated TOPTW-PS instances and conclude that the proposed S-DPSO can produce high-quality TOPTW-PSsolutions. 2019-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4469 info:doi/10.1016/j.cie.2019.106084 https://ink.library.smu.edu.sg/context/sis_research/article/5472/viewcontent/Selective_discrete_particle_swarm_optimization_for_the_team_orienteering_problem_with_time_windows_and_partial_scores.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Team orienteering problem Time window Partial score Discrete particle swarm optimization Theory and Algorithms |
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Team orienteering problem Time window Partial score Discrete particle swarm optimization Theory and Algorithms YU, Vincent F. REDI, Perwira A. A. N. JEWPANYA, Parida GUNAWAN, Aldy Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores |
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This paper introduces the Team Orienteering Problem with Time Windows and Partial Scores (TOPTW-PS),which is an extension of the Team Orienteering Problem with Time Windows (TOPTW). In the context of theTOPTW-PS, each node is associated with a set of scores with respect to a set of attributes. The objective ofTOPTW-PS is to find a set of routes that maximizes the total score collected from a subset of attributes whenvisiting the nodes subject to the time budget and the time window at each visited node. We develop a mathematical model and propose a discrete version of the Particle Swarm Optimization (PSO), namely, the SelectiveDiscrete PSO (S-DPSO), to solve TOPTW-PS. The proposed S-DPSO uses four different movement schemes tomove a particle from its current position. The best movement scheme is selected to determine the next position ofthe particle. To evaluate the performance of the proposed S-DPSO algorithm, we first test S-DPSO on two variants of Orienteering Problem, namely, Team Orienteering Problem (TOP) and TOPTW. Experimental resultsshow that S-DPSO performs well in solving benchmark instances of TOP and TOPTW. In general, S-DPSO iscomparable to the state-of-the-art algorithms for these problems. We also apply the S-DPSO to solve 168 newlygenerated TOPTW-PS instances and conclude that the proposed S-DPSO can produce high-quality TOPTW-PSsolutions. |
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text |
author |
YU, Vincent F. REDI, Perwira A. A. N. JEWPANYA, Parida GUNAWAN, Aldy |
author_facet |
YU, Vincent F. REDI, Perwira A. A. N. JEWPANYA, Parida GUNAWAN, Aldy |
author_sort |
YU, Vincent F. |
title |
Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores |
title_short |
Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores |
title_full |
Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores |
title_fullStr |
Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores |
title_full_unstemmed |
Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores |
title_sort |
selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores |
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Institutional Knowledge at Singapore Management University |
publishDate |
2019 |
url |
https://ink.library.smu.edu.sg/sis_research/4469 https://ink.library.smu.edu.sg/context/sis_research/article/5472/viewcontent/Selective_discrete_particle_swarm_optimization_for_the_team_orienteering_problem_with_time_windows_and_partial_scores.pdf |
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