Tabu Search Applied to Community-based Rural Tourism
Community-based Rural Tourism (CBRT) has been consolidated as a socially and environmentally responsible alternative in Peru for generating income opportunities for the rural sector. The development of CBRT is still incipient in Peru, and therefore the information generated by public and private ins...
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| Autores principales: | , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2017
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| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| _version_ | 1881508832422658048 |
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| author | Maquera Sosa, Nelida Gladys Mendoza, Óscar |
| author_browse | Maquera Sosa, Nelida Gladys Mendoza, Óscar |
| author_facet | Maquera Sosa, Nelida Gladys Mendoza, Óscar |
| author_sort | Maquera Sosa, Nelida Gladys |
| collection | Producción científica |
| description | Community-based Rural Tourism (CBRT) has been consolidated as a socially and environmentally responsible alternative in Peru for generating income opportunities for the rural sector. The development of CBRT is still incipient in Peru, and therefore the information generated by public and private institutions is insufficient and in some cases does non-exist. Worldwide, 91% of people use the Internet to find places to visit. However, the use of the Internet brings new challenges for the tourism industry. Different types of consumers, such as Baby Boomers, Generation X and Generation Y, present different challenges. Generation Y is the most active and much more involved in Internet travel planning. With regard to information on ecotourism and / or historic tourism in the Puno region of Peru, there is no information on the web, much less routes according to the needs of tourists. The objective of this work is to develop an Intelligent Digital Platform to apply data science and big data to find smart routes that satisfy tourists' requirements. To obtain the route, a nearest neighbor heuristic was implemented, using 2-opt and the Tabu Search metaheuristic implemented in Python programming language. Computational results demonstrate the efficiency of the Tabu Search algorithm when using data found in the literature and when using real data, guaranteeing the reduction of distances traveled in the shortest time possible, reducing travel costs. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/700 |
| institution | UPeU |
| language | English |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/7002026-10-30T16:14:46ZTabu Search Applied to Community-based Rural TourismMaquera Sosa, Nelida GladysMendoza, ÓscarTabu searchTourismRural tourismComputer scienceGeographyArtificial intelligenceTourism geographyArchaeologySocial Sciences Social Sciences Sociology and Political Science Community-based Tourism Development and SustainabilityCommunity-based Rural Tourism (CBRT) has been consolidated as a socially and environmentally responsible alternative in Peru for generating income opportunities for the rural sector. The development of CBRT is still incipient in Peru, and therefore the information generated by public and private institutions is insufficient and in some cases does non-exist. Worldwide, 91% of people use the Internet to find places to visit. However, the use of the Internet brings new challenges for the tourism industry. Different types of consumers, such as Baby Boomers, Generation X and Generation Y, present different challenges. Generation Y is the most active and much more involved in Internet travel planning. With regard to information on ecotourism and / or historic tourism in the Puno region of Peru, there is no information on the web, much less routes according to the needs of tourists. The objective of this work is to develop an Intelligent Digital Platform to apply data science and big data to find smart routes that satisfy tourists' requirements. To obtain the route, a nearest neighbor heuristic was implemented, using 2-opt and the Tabu Search metaheuristic implemented in Python programming language. Computational results demonstrate the efficiency of the Tabu Search algorithm when using data found in the literature and when using real data, guaranteeing the reduction of distances traveled in the shortest time possible, reducing travel costs.Articlehttps://hdl.handle.net/20.500.15037/700W2613615444enDigital Commons - Andrews University (Andrews University)2017-01-012026-06-13T09:31:59Z2026-06-13T09:31:59Z |
| spellingShingle | Tabu search Tourism Rural tourism Computer science Geography Artificial intelligence Tourism geography Archaeology Social Sciences Social Sciences Sociology and Political Science Community-based Tourism Development and Sustainability Maquera Sosa, Nelida Gladys Mendoza, Óscar Tabu Search Applied to Community-based Rural Tourism |
| title | Tabu Search Applied to Community-based Rural Tourism |
| title_full | Tabu Search Applied to Community-based Rural Tourism |
| title_fullStr | Tabu Search Applied to Community-based Rural Tourism |
| title_full_unstemmed | Tabu Search Applied to Community-based Rural Tourism |
| title_short | Tabu Search Applied to Community-based Rural Tourism |
| title_sort | tabu search applied to community based rural tourism |
| topic | Tabu search Tourism Rural tourism Computer science Geography Artificial intelligence Tourism geography Archaeology Social Sciences Social Sciences Sociology and Political Science Community-based Tourism Development and Sustainability |
| topic_facet | Tabu search Tourism Rural tourism Computer science Geography Artificial intelligence Tourism geography Archaeology Social Sciences Social Sciences Sociology and Political Science Community-based Tourism Development and Sustainability |
| url | https://hdl.handle.net/20.500.15037/700 |
| work_keys_str_mv | AT maquerasosanelidagladys tabusearchappliedtocommunitybasedruraltourism AT mendozaoscar tabusearchappliedtocommunitybasedruraltourism |