A case study of heterogeneous fleet vehicle routing problem: Touristic distribution application in Alanya

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Abstract

In this study, Fleet Size and Mix Vehicle Routing Problem is considered in order to optimize the distribution of the tourists who have traveled between the airport and the hotels in the shortest distance by using the minimum cost. The initial solution space for the related methods are formed as a combination of Savings algorithm, Sweep algorithm and random permutation alignment. Then, two well-known solution methods named as Standard Genetic Algorithms and random search algorithms are used for changing the initial solutions. Computational power of the machine and heuristic algorithms are used instead of human experience and human intuition in order to solve the distribution problem of tourists coming to hotels in Alanya region from Antalya airport. For this case study, daily data of tourist distributions performed by an agency operating in Alanya region are considered. These distributions are then modeled as Vehicle Routing Problem to calculate the solutions for various applications. From the comparisons with the decision of a human expert, it is seen that the proposed methods produce better solutions with respect to human experience and insight. Random search method produces a solution more favorable in terms of time. As a conclusion, it is seen that, owing to the distribution plans offered by the obtained solutions, the agencies may reduce the costs by achieving savings up to 35%.

Keywords

Tourism;tourist distribution;vehicle routing problem;fleet size and mix vehicle routing problem;heuristic algorithms.

Conflict of interest

The authors declare they have no competing interests.

References

[1] Cordeau, J. F., Laporte, G., Modelling and optimization of vehicle routing problems. In Handbook on Modelling for Discrete Optimization, Springer, 151-181 (2006).

[2] Şen, E., KOBİ'lerin Uluslararası Rekabet Güçlerini Artırmada Tedarik Zinciri Yönetiminin Önemi. T.C. Başbakanlık Dış Ticaret Müsteşarlığı İhracatı Geliştirme Etüd Merkezi, Ankara (2006).

[3] Kocaman, S., Destinasyon Yönetimi Kapsamında Marka Kimliğine Etki Eden Faktörlerin Marka İmajına Etkisi: Alanya Örneği. Akdeniz Üniversitesi, SBE, Yayımlanmamış Doktora Tezi, Antalya (2012).

[4] Baldacci, R., Battarra M., Vigo, D., Routing a heterogeneous fleet of vehicles, Operations Research / Computer Science Interfaces, 43, 3-27 (2008).

[5] Dell'Amico, M., Monaci, M., Pagani C., Vigo, D., Heuristic approaches for the fleet size and mix vehicle routing problem with time windows. Transportation Science, 41(4), 516-526 (2007).

[6] Gendreau, M., Potvin, J. Y., Braysy, O., Hasle G., Lokketangen, A., Metaheuristic for the Vehicle Routing Problem and its Extensions: A Categorized Bibliography (2007). Available from: https://www.cirrelt. ca/DocumentsTravail/CIRRELT-2007-27.pdf. Accessed 15 April 2013.

[7] Gendreau, M., Laporte, G., Musaraganyi C., Taillard, E. D., A Tabu search heuristic for the heterogeneous fleet vehicle routing problem. Computers and Operations Research, 26, 1153-1173 (1999).

[8] Wassan N., Osman, I., Tabu search variants for the mix fleet vehicle routing problem. Journal of the Operational Research Society. 53, 768-782 (2002).

[9] Choi E., Tcha, D. W., A column generation approach to the heterogeneous fleet vehiclerouting problem. Computers and Operations Research. 34, 2080–2095 (2007).

[10] Penna, P. H. V., Subramanian A., Ochi, L. S., An Iterated local search heuristic for the heterogeneous fleet vehicle routing problem. Journal of Heuristics, 19(2), 201-232 (2011).

[11] Tütüncü, G. Y., An interactive GRAMPS algorithm for the heterogeneous fixed fleet vehicle routing problem with and without backhauls. European Journal of Operational Research, 201(2) 593–600 (2010).

[12] Gencer, C., Kızılkaya Aydoğan E., Çetin, S., Simultaneous pick-up and delivery decision support systems. in Decision Support Systems, Advances. Croatia, INTECH, 203- 214 (2010).

[13] Çetin, S., Özkütük E., Gencer, C., Heterojen araç filolu eş zamanlı dağıtım ve toplamalı araç rotalama problemi için bir karar destek sistemi. International Journal of Research and Development. 3(1) 11-18 (2011).

[14] Ochi, L., Vianna, D., Drummond L. M., Victor, A., A Parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet. Parallel and Distributed Processing, Lecture Notes in Computer Science, 1388, 216-224 (1998).

[15] Lima, C., Goldbarg, M., Goldbarg, E., A Memetic algorithm for heterogeneous fleet vehicle routing problem. Electronic Notes in Discrete Mathematics, 18, 171-176 (2004).

[16] Liu, S., Huang W., Ma, H., An Effective genetic algorithm for the fleet size and mix vehicle routing problems. Transportation Research Part E, 45, 434-445 (2009).

[17] www.alanya.com.tr, Alanya Haritası. (2012) Available from: http://www.alanya.cc/map? lang=tr&catid=hotels&itemid=77. Accessed 29 December 2012.

[18] ALTSO, Alanya Ekonomik Rapor (2011), Alanya Ticaret ve Sanayi Odası, Alanya (2012).

[19] Kay, M. G., Matlog: Logistics Engineering Matlab Toolbox (2013) Available from: http://www. ise. ncsu. edu/ kay/ matlog/ Accessed 16 February 2013.

[20] TÜİK, Türkiye Turizm İstatistikleri, Türkiye İstatistik Kurumu, Ankara (2012).

[21] Güleş, H. K., Paksoy, T., Bülbül, H., Özceylan, E., Tedarik Zinciri Yönetimi, Gazi Kitabevi, Ankara (2010).

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