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Some Metaheuristics for Tourist Trip Design Problem

Tourist Trip Design Problem (TTDP) is fundamental in improving tourists' travel experiences and urban development. This study introduces a recommender engine to create a tour trip plan. The output of the system is a detailed trip itinerary for the tourist. It allows the tourist to determine the...

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Main Authors: Son, N.T., Nguyet Ha, T.T., Jaafar, J.B., Anh, B.N., Giang, T.T.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37593/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170066052&doi=10.1109%2fISIEA58478.2023.10212154&partnerID=40&md5=e2d71ca2a334f862f23f95c1bfbb691b
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record_format eprints
spelling oai:scholars.utp.edu.my:375932023-10-13T13:00:54Z http://scholars.utp.edu.my/id/eprint/37593/ Some Metaheuristics for Tourist Trip Design Problem Son, N.T. Nguyet Ha, T.T. Jaafar, J.B. Anh, B.N. Giang, T.T. Tourist Trip Design Problem (TTDP) is fundamental in improving tourists' travel experiences and urban development. This study introduces a recommender engine to create a tour trip plan. The output of the system is a detailed trip itinerary for the tourist. It allows the tourist to determine the places to visit, the length of stay, and the entire route. The system's core is an optimizer for the combinatorial multi-objective optimization problem (MOP). There, users specify time and budget conditions as a query for the system. We have proposed a combination of Compromise Programming (CP) and Metaheuristics for this multi-objective optimization problem. Our method can handle situations where decision-makers cannot assign preferences to each goal and different decision-making scenarios. We have built two metaheuristic algorithms based on the proposed approach, which are Genetic Algorithm (GA) and another is Ant Colony Optimization (ACO). The objective was to examine how the influence of different search strategies affects the quality of the solution. The results show that ACO's swarm search strategy allows for finding slightly better-quality solutions than GA. However, it must trade-off with CPU time. We also compared the proposed method with the Posteriority approach to MOP. The results show that CP-based algorithms are superior to NSGA-II in finding a Pareto frontier. © 2023 IEEE. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item NonPeerReviewed Son, N.T. and Nguyet Ha, T.T. and Jaafar, J.B. and Anh, B.N. and Giang, T.T. (2023) Some Metaheuristics for Tourist Trip Design Problem. In: UNSPECIFIED. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170066052&doi=10.1109%2fISIEA58478.2023.10212154&partnerID=40&md5=e2d71ca2a334f862f23f95c1bfbb691b 10.1109/ISIEA58478.2023.10212154 10.1109/ISIEA58478.2023.10212154 10.1109/ISIEA58478.2023.10212154
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Tourist Trip Design Problem (TTDP) is fundamental in improving tourists' travel experiences and urban development. This study introduces a recommender engine to create a tour trip plan. The output of the system is a detailed trip itinerary for the tourist. It allows the tourist to determine the places to visit, the length of stay, and the entire route. The system's core is an optimizer for the combinatorial multi-objective optimization problem (MOP). There, users specify time and budget conditions as a query for the system. We have proposed a combination of Compromise Programming (CP) and Metaheuristics for this multi-objective optimization problem. Our method can handle situations where decision-makers cannot assign preferences to each goal and different decision-making scenarios. We have built two metaheuristic algorithms based on the proposed approach, which are Genetic Algorithm (GA) and another is Ant Colony Optimization (ACO). The objective was to examine how the influence of different search strategies affects the quality of the solution. The results show that ACO's swarm search strategy allows for finding slightly better-quality solutions than GA. However, it must trade-off with CPU time. We also compared the proposed method with the Posteriority approach to MOP. The results show that CP-based algorithms are superior to NSGA-II in finding a Pareto frontier. © 2023 IEEE.
format Conference or Workshop Item
author Son, N.T.
Nguyet Ha, T.T.
Jaafar, J.B.
Anh, B.N.
Giang, T.T.
spellingShingle Son, N.T.
Nguyet Ha, T.T.
Jaafar, J.B.
Anh, B.N.
Giang, T.T.
Some Metaheuristics for Tourist Trip Design Problem
author_facet Son, N.T.
Nguyet Ha, T.T.
Jaafar, J.B.
Anh, B.N.
Giang, T.T.
author_sort Son, N.T.
title Some Metaheuristics for Tourist Trip Design Problem
title_short Some Metaheuristics for Tourist Trip Design Problem
title_full Some Metaheuristics for Tourist Trip Design Problem
title_fullStr Some Metaheuristics for Tourist Trip Design Problem
title_full_unstemmed Some Metaheuristics for Tourist Trip Design Problem
title_sort some metaheuristics for tourist trip design problem
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37593/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170066052&doi=10.1109%2fISIEA58478.2023.10212154&partnerID=40&md5=e2d71ca2a334f862f23f95c1bfbb691b
_version_ 1781707929478496256
score 13.211869

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