IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p16293-d995248.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    Faculty of Social and Political Sciences, Universitas Muhammadiyah Sinjai, Kabupaten Sinjai 92615, Sulawesi Selatan, Indonesia
    Faculty of Social and Political Sciences, Universitas Muhammadiyah Makassar, Kota Makassar 90221, Sulawesi Selatan, Indonesia
    Publication Research Institute and Community Service, Universitas Muhammadiyah Sidenreng Rappang, Sidenreng Rappang Regency 91651, South Sulawesi, Indonesia)

  • Phuripoj Kaewyong

    (Faculty of Science & Technology, Suan Dusit University, Bangkok 10300, Thailand)

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.

Suggested Citation

  • Pannee Suanpang & Pitchaya Jamjuntr & Kittisak Jermsittiparsert & Phuripoj Kaewyong, 2022. "Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16293-:d:995248
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/16293/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/23/16293/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. Mateos & A. Jiménez-Martín, 2016. "Multiobjective Simulated Annealing for Collision Avoidance in ATM Accounting for Three Admissible Maneuvers," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-16, August.
    2. Ren Qing-dao-er-ji & Yuping Wang, 2013. "Inventory Based Bi-Objective Flow Shop Scheduling Model and Its Hybrid Genetic Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, April.
    3. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    4. Batabyal, Amitrajeet A. & Yoo, Seung Jick, 2010. "A probabilistic analysis of guided tours for tourists during the slack season," Tourism Management, Elsevier, vol. 31(4), pages 482-485.
    5. Luan, Jing & Yao, Zhong & Zhao, Futao & Song, Xin, 2019. "A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 294-309.
    6. Joseph Adams & Egon Balas & Daniel Zawack, 1988. "The Shifting Bottleneck Procedure for Job Shop Scheduling," Management Science, INFORMS, vol. 34(3), pages 391-401, March.
    7. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    8. Zhou, Hong & Cheung, Waiman & Leung, Lawrence C., 2009. "Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm," European Journal of Operational Research, Elsevier, vol. 194(3), pages 637-649, May.
    9. J. Carlier & E. Pinson, 1989. "An Algorithm for Solving the Job-Shop Problem," Management Science, INFORMS, vol. 35(2), pages 164-176, February.
    10. B. J. Lageweg & J. K. Lenstra & A. H. G. Rinnooy Kan, 1977. "Job-Shop Scheduling by Implicit Enumeration," Management Science, INFORMS, vol. 24(4), pages 441-450, December.
    11. Amaral Armentano, Vinicius & Rigao Scrich, Cintia, 2000. "Tabu search for minimizing total tardiness in a job shop," International Journal of Production Economics, Elsevier, vol. 63(2), pages 131-140, January.
    12. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qiang Wang & Dong Yu & Jinyu Zhou & Chaowu Jin, 2023. "Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm," Sustainability, MDPI, vol. 15(9), pages 1-18, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hamed Piroozfard & Kuan Yew Wong & Adnan Hassan, 2016. "A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems," Journal of Optimization, Hindawi, vol. 2016, pages 1-13, April.
    2. Valls, Vicente & Angeles Perez, M. & Sacramento Quintanilla, M., 1998. "A tabu search approach to machine scheduling," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 277-300, April.
    3. Francis Sourd & Wim Nuijten, 2000. "Multiple-Machine Lower Bounds for Shop-Scheduling Problems," INFORMS Journal on Computing, INFORMS, vol. 12(4), pages 341-352, November.
    4. Blazewicz, Jacek & Domschke, Wolfgang & Pesch, Erwin, 1996. "The job shop scheduling problem: Conventional and new solution techniques," European Journal of Operational Research, Elsevier, vol. 93(1), pages 1-33, August.
    5. Sels, Veronique & Craeymeersch, Kjeld & Vanhoucke, Mario, 2011. "A hybrid single and dual population search procedure for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 215(3), pages 512-523, December.
    6. Ganesan, Viswanath Kumar & Sivakumar, Appa Iyer, 2006. "Scheduling in static jobshops for minimizing mean flowtime subject to minimum total deviation of job completion times," International Journal of Production Economics, Elsevier, vol. 103(2), pages 633-647, October.
    7. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    8. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    9. Guinet, Alain & Legrand, Marie, 1998. "Reduction of job-shop problems to flow-shop problems with precedence constraints," European Journal of Operational Research, Elsevier, vol. 109(1), pages 96-110, August.
    10. Giuseppe Lancia & Franca Rinaldi & Paolo Serafini, 2011. "A time-indexed LP-based approach for min-sum job-shop problems," Annals of Operations Research, Springer, vol. 186(1), pages 175-198, June.
    11. El-Bouri, A. & Azizi, N. & Zolfaghari, S., 2007. "A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1894-1910, March.
    12. Da Col, Giacomo & Teppan, Erich C., 2022. "Industrial-size job shop scheduling with constraint programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    13. Ouenniche, J. & Bertrand, J. W. M., 2001. "The finite horizon economic lot sizing problem in job shops: : the multiple cycle approach," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 49-61, December.
    14. Egon Balas & Alkis Vazacopoulos, 1998. "Guided Local Search with Shifting Bottleneck for Job Shop Scheduling," Management Science, INFORMS, vol. 44(2), pages 262-275, February.
    15. Golenko-Ginzburg, Dimitri & Kesler, Shmuel & Landsman, Zinoviy, 1995. "Industrial job-shop scheduling with random operations and different priorities," International Journal of Production Economics, Elsevier, vol. 40(2-3), pages 185-195, August.
    16. Lourenco, Helena Ramalhinho, 1995. "Job-shop scheduling: Computational study of local search and large-step optimization methods," European Journal of Operational Research, Elsevier, vol. 83(2), pages 347-364, June.
    17. Gromicho, J.A.S. & Hoorn, J.J. van & Timmer, G.T., 2009. "Exponentially better than brute force: solving the jobshop scheduling problem optimally by dynamic programming," Serie Research Memoranda 0056, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    18. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
    19. Christian Artigues & Dominique Feillet, 2008. "A branch and bound method for the job-shop problem with sequence-dependent setup times," Annals of Operations Research, Springer, vol. 159(1), pages 135-159, March.
    20. Mati, Yazid & Dauzère-Pérès, Stèphane & Lahlou, Chams, 2011. "A general approach for optimizing regular criteria in the job-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 212(1), pages 33-42, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16293-:d:995248. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.