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Marketplace Location Decision Making and Tourism Route Planning

Author

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  • Worapot Sirirak

    (Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand)

  • Rapeepan Pitakaso

    (Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand)

Abstract

This research addresses the problem of planning tourism routes and finding appropriate shopping (market place) locations for agricultural product transportation. Generally, tourists visit popular tourism attractions; and generally, unpopular tourism attractions do not stimulate the economy, trade, or local income. Popular tourism attractions that are located far away from each other require the transportation of local products, and tourists must make decisions as to which locations to visit when planning their vacation. Planning a tourism route while balancing tourism attractions and shopping markets is important for the economic stimulation of tourism. This work presents a problem-solving method for tourism route-planning for a particular case study in Chiang Rai province, Thailand, using the Adaptive Large Neighborhood Search (ALNS) method. Six main destruction and five repair cycles in the ALNS method were applied to solve the tourism route design problem and to find the best solution so that tourists can visit all of the main attractions. We found that 13 tourism routes provide the shortest travel distance for each travel route. The total distance traveled was 2538.02 km for all routes. To balance the tourism on all routes, the popular and less popular tourism attractions were combined. For all routes, the shopping market location is the best place for tourism products to be sold and where tourist relaxation occurs. The results from ALNS were compared with the results from those obtained by the exact Lingo program V11. The ALNS algorithm results were not significantly different from the Lingo results. For the computational results for all examined cases, the ALNS algorithm was shown to be competitive, with short processing times given the sizes of the problems. For the traveling distance, the ALNS result significantly differs from the exact method by approximately 1.12%, and had a better effect than the exact method by approximately 99% in terms of processing time. Therefore, the proposed methodology provides an effective and high-quality solution for tourism route planning.

Suggested Citation

  • Worapot Sirirak & Rapeepan Pitakaso, 2018. "Marketplace Location Decision Making and Tourism Route Planning," Administrative Sciences, MDPI, vol. 8(4), pages 1-25, November.
    • Handle: RePEc:gam:jadmsc:v:8:y:2018:i:4:p:72-:d:184054
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    References listed on IDEAS

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    1. Potvin, Jean-Yves & Rousseau, Jean-Marc, 1993. "A parallel route building algorithm for the vehicle routing and scheduling problem with time windows," European Journal of Operational Research, Elsevier, vol. 66(3), pages 331-340, May.
    2. Nagy, Gabor & Salhi, Said, 2007. "Location-routing: Issues, models and methods," European Journal of Operational Research, Elsevier, vol. 177(2), pages 649-672, March.
    3. Kotiloglu, S. & Lappas, T. & Pelechrinis, K. & Repoussis, P.P., 2017. "Personalized multi-period tour recommendations," Tourism Management, Elsevier, vol. 62(C), pages 76-88.
    4. Andrades, Lidia & Dimanche, Frederic, 2017. "Destination competitiveness and tourism development in Russia: Issues and challenges," Tourism Management, Elsevier, vol. 62(C), pages 360-376.
    5. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    6. Alinaghian, Mahdi & Shokouhi, Nadia, 2018. "Multi-depot multi-compartment vehicle routing problem, solved by a hybrid adaptive large neighborhood search," Omega, Elsevier, vol. 76(C), pages 85-99.
    7. Yan, Libo & Gao, Bo Wendy & Zhang, Meng, 2017. "A mathematical model for tourism potential assessment," Tourism Management, Elsevier, vol. 63(C), pages 355-365.
    8. Gullhav, Anders N. & Cordeau, Jean-François & Hvattum, Lars Magnus & Nygreen, Bjørn, 2017. "Adaptive large neighborhood search heuristics for multi-tier service deployment problems in clouds," European Journal of Operational Research, Elsevier, vol. 259(3), pages 829-846.
    9. Zheng, Weimin & Huang, Xiaoting & Li, Yuan, 2017. "Understanding the tourist mobility using GPS: Where is the next place?," Tourism Management, Elsevier, vol. 59(C), pages 267-280.
    10. Prodhon, Caroline & Prins, Christian, 2014. "A survey of recent research on location-routing problems," European Journal of Operational Research, Elsevier, vol. 238(1), pages 1-17.
    11. Dayarian, Iman & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2016. "An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 95-123.
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    Cited by:

    1. Beata Bieszk-Stolorz & Krzysztof Dmytrów, 2021. "Marketplace Trade in Large Cities in Poland," Land, MDPI, vol. 10(9), pages 1-15, September.
    2. Zyrianov Aleksandr I. & Zyrianova Inna S., 2021. "Planning of the Interregional Tourist Route in the Urals," Quaestiones Geographicae, Sciendo, vol. 40(2), pages 109-118, June.
    3. Cristina Maria Păcurar & Ruxandra-Gabriela Albu & Victor Dan Păcurar, 2021. "Tourist Route Optimization in the Context of Covid-19 Pandemic," Sustainability, MDPI, vol. 13(10), pages 1-17, May.

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