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Metaheuristics in Business Model Development for Local Tourism Sustainability Enhancement


[ 1 ] Instytut Logistyki, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[6.6] Management and quality studies

Year of publication


Published in


Journal year: 2022 | Journal volume: vol. 10 | Journal number: iss. 24

Article type

scientific article

Publication language


  • sustainable management
  • planning and scheduling
  • tourism supply chain
  • mathematical programming
  • multiple providers

EN This study focused on analyzing planning and scheduling services in the tourism industry. Because dealing with these issues necessitates consideration of several important factors and stakeholders in the tourism business, it is challenging to operate resources efficiently. The purpose of this research is to propose a novel approach that allows maximizing the profits of tourism-related service sectors while considering many real-life constraints, such as sequence-dependent travel time, tourist time windows, points of interest, and specific destination constraints. We test our mathematical model for solving first small-scale problems and then metaheuristics proposed for finding a solution for real-life size problems. Moreover, sensitivity analysis was used to analyze the case study’s worthiness when the total cost and the revenue factor were changed. A real case study from Thailand’s Khon Kaen and Kanchanaburi provinces were used to verify the proposed models. The results indicate that the proposed models can be applied to investment decisions and strategy development. Furthermore, the outputs of the proposed models (i.e., the mathematical and metaheuristics models) can be employed to enhance the sustainability of other supply chains.

Date of online publication


Pages (from - to)

4750-1 - 4750-21





Article number: 4750

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Release date


Date of Open Access to the publication

at the time of publication

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