Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm

Pannee Suanpang (), Pitchaya Jamjuntr, Kittisak Jermsittiparsert and Phuripoj Kaewyong
Additional contact information
Pannee Suanpang: Faculty of Science & Technology, Suan Dusit University, Bangkok 10300, Thailand
Pitchaya Jamjuntr: Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Kittisak Jermsittiparsert: Faculty of Education, University of City Island, Famagusta 9945, Cyprus
Phuripoj Kaewyong: Faculty of Science & Technology, Suan Dusit University, Bangkok 10300, Thailand

Sustainability, 2022, vol. 14, issue 23, 1-21

Abstract: The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.

Keywords: service scheduling; hybrid genetic algorithms; simulated annealing algorithms; tourism services; sustainability tourism (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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