Routing for tourist and excursion bureaus based at parametric network models
Economic Annals-ХХI: Volume 191, Issue 7-8(1), Pages: 100-113
Citation information:
Maslyhan, O., Todierishko, E., Zhukov, S., & Kashka, M. (2021). Routing for tourist and excursion bureaus based at parametric network models. Economic Annals-XXI, 191(7-8(1)), 100-113. doi: https://doi.org/10.21003/ea.V191-08
Olena Maslyhan
PhD (Economics),
Associate Professor of the Department of Tourism and Geography,
Mukachevo State University
26 Uzhhorodska Str., Mukachevo, Zakarpattia Region, 89600, Ukraine
o.maslyhan@mail.msu.edu.ua
ORCID ID: https://orcid.org/0000-0002-8465-548X
Erika Todierishko
PhD Student (Economics),
Economics and Finance Department,
Mukachevo State University
26 Uzhhorodska Str., Mukachevo, Zakarpattia Region, 89600, Ukraine
erika.togyeriska@gmail.com
ORCID ID: https://orcid.org/0000-0002-3055-0244
Sviatoslav Zhukov
D.Sc. (Economics),
Senior Researcher,
Associate Professor of the
Department of Business Administration,
Marketing and Management,
Uzhhorod National University
14 University Str., Uzhhorod, 88000, Ukraine
zhukgiga@gmail.com
ORCID ID: https://orcid.org/0000-0002-0499-7990
Mariya Kashka
PhD (History),
Associate Professor of the Department of Tourism,
Uzhhorod National University
14 University Str., Uzhhorod, 88000, Ukraine
mariia.kashka@uzhnu.edu.ua
ORCID ID: https://orcid.org/0000-0001-7437-6156
Routing for tourist and excursion bureaus based at parametric network models
Abstract. This study is devoted to applying parametric network models for the process of defining a guided tour route within route networks on the example of Denmark. This is caused by difficulty in determining variations when organizing guided tours. Under the actual digitization conditions, tourist and excursion bureaus are being restructured from static organizations administering various excursions into dynamic ones. They are actually getting adjusted to the customers’ needs and demands, taking into account the actual possibilities for covering a certain topic by the tour party within a route. The main problem encountered by tourist and excursion bureaus is the following. Although the nomenclature of presented guided tours is established by the economic entity independently, those are not always carried out according to a clearly defined itinerary and on the same conditions for all participants. When providing such services, customers’ demands and service peculiarities are not known in advance. The purpose of the present study is to provide a substantive basis for routing in tourist and excursion bureaus, based on parametric network models and taking into account the peculiarities of dynamically adaptable tables containing the best routes. To achieve the research goal, network planning methods were used, such as analytical, tabular, cloud computing in the AnyLogic Cloud environment.
As a result of the study, a substantive basis of routing of the tourist route was presented for tourist and excursion bureaus, through their parametric network models. The study was implemented at the sample of the Denmark Tour -Your Guide Office, a company founded within cooperation with Russian, Ukrainian, and Denmark partners and providing travel services within the Denmark tourist market. The Office includes about 20 affiliates in Denmark, where route networks have already been adapted to designing tours in practice and parameterization of such networks is well underway, in particular by shifting the focus from the route distance rate to minimization of transfers between attraction sites. However, to provide a substantive basis for the routing in a tourist office, parameters of the routing networks should be determined not only based on the list of actions (activities) to be carried out, but also on their minimum and maximum possible duration. A lack of due attention to the servicing time for the tour groups will lead to breaking tour schedules. Thus, in 2020, as a result of the inefficient parameterization at Denmark Tour – Your Guide, about 5-6 tours around Aalborg and its vicinity were cancelled monthly. Denmark Tour- Your Guide incurs monthly profit losses at 15% in 4-6 tours around Aarhus and its surroundings, Jursland peninsula, rated at a fixed cost, as the result of payment of a fixed cost for the selected excursions. A similar situation, with breaking tour schedules and monthly losses incurred, is common with tourist and excursion bureaus in various countries around the world, including Ukraine.
According to the results of the study, it is marked that the routing of tourist itineraries designed by tourist agencies, based on parametric network models turns their static time reserves and operational metrics into dynamic values depending on the duration of the tour activities. This not only ensures following schedules properly in all tours but also minimizes monthly profit loss, at an estimated EUR 2,250 for the Aalborg and its surroundings routes. Meanwhile, there may be situations where it is not possible to change the total tour cost. For example, in the company Denmark Tour – Your Guide, when working with intermediate parties, this price is fixed. To prevent incurring monthly losses within 15% of the profits for 4-6 tours of Aarhus and its surroundings, Jursland peninsula, Aalborg and Surroundings, it is necessary to make some quite specific adjustments in some activities at the sites. These should take into account the time reserve values on the longest route. A special tour activity complex is to be completed, with a maximum difference in early and late schedule times, standard and urgent pricing for the site operations). A procedure is compiled for minimizing losses in routes (over 8K euro annually), providing for completion of the activity complex within the schedule with a minimum additional charge to the operating metric (the route price), since it is not reimbursed by the tourists. It is important that the results presented should identify the path adjustments of each route simultaneously, taking into account the actual time reserve (available based on the tour group location and the previouisly completed schedule items on the tour).
Prospects for practical implementation of the presented substantiation basis for the itinerary routing to be used in tourist and excursion bureaus, based on parametric network models, are in establishing facilities for creating dynamic graphic images of the whole tour procedure, in the form of a directed graph of the route network.
Keywords: Excursion; Bureau; Routing; Route; Network Model; Route Network; Excursion Design; Network Analysis
JEL Classіfіcatіon: C46; C61; С80
Acknowledgements and Funding: The authors received no direct funding for this research.
Contribution: The authors contributed equally to this work.
Data Availability Statement: The dataset is available from the authors upon request.
DOI: https://doi.org/10.21003/ea.V191-08
References
- Bangwayo-Skeete, P. F., & Skeete, R. W. (2015). Can google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46, 454-464.
https://doi.org/10.1016/j.tourman.2014.07.014 - Bonavear, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: from Natural to Artificial Systems. Oxford University Press.
https://doi.org/10.1093/oso/9780195131581.001.0001 - Caicedo-Torres, W., & Payares, F. (2016). A Machine Learning Model for Occupancy Rates and Demand Forecasting in the Hospitality Industry. In M. M. Gómez, Escalante, H. J., Segura, A., & Murillo, J. D. (Eds.), Advances in Artificial Intelligence – IBERAMIA 2016 (pp. 201-211).
https://doi.org/10.1007/978-3-319-47955-2_17 - Chou, X., Gambardella, L. M., & Montemanni, R. (2018). Monte Carlo Sampling for the Probabilistic Orienteering Problem. In New Trends in Emerging Complex Real Life Problems, (pp. 169-177).
https://doi.org/10.1007/978-3-030-00473-6_19 - Chou, X., Gambardella, L. M., & Montemanni, R. (2019). Monte Carlo sampling for the tourist trip design problem. Millenium, 10(2), 83-90.
https://doi.org/10.29352/mill0210.09.00259 - Cormen,T. H., Leiserson, Ch. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. The MIT Press, Cambridge, Massachusetts.
https://edutechlearners.com/download/Introduction_to_algorithms-3rd%20Edition.pdf - Daniatours travel agency. (2020). Daniatur-Your guide, Database.
https://daniatours.com - Dorigo, M. (1992). Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Politecnico di Milano, 45-57.
https://www.scienceopen.com/document?vid=c4544fb2-dbe6-466e-815d-b58d83ff0965 - Dorigo, M., & Stützle, Th. (2004). Ant Colony Optimization. Massachusetts Institute of Technology.
- Ernst & Young Global Limited. (2021). Database.
https://www.ey.com/uk_ua (in Ukr.) - Filatova, T., Polhill, J. G., & van Ewijk, S. (2016). Regime shifts in coupled socio-environmental systems: review of modelling challenges and approaches. Environmental Modelling & Software, 75, 333-347.
https://doi.org/10.1016/j.envsoft.2015.04.003 - Gunter, U., & Önder, I. (2015). Forecasting international city tourism demand for paris: Accuracy of uni- and multivariate models employing monthly data. Tourism Management, 46, 123-135.
https://doi.org/10.1016/j.tourman.2014.06.017 - Jovanović, V., & Njeguš, A. (2008). The Application of GIS and its component in Tourism. Yugoslav Journal of Operations Research, 18(2), 261-272.
https://doi.org/10.2298%2Fyjor0802261j - Li, G., Song, H., & Witt, S. F. (2006). Time varying parameter and fixed parameter linear aids: An application to tourism demand forecasting. International Journal of Forecasting, 22, 57-71.
https://doi.org/10.1016/j.ijforecast.2005.03.006 - Li, X., Pan, B., Law, R., & Huang, X. (2017). Forecasting tourism demand with composite search index. Tourism Management, 59, 57-66.
https://doi.org/10.1016/j.tourman.2016.07.005 - Li, Y. (2017). Application of GIS technology in tourism route design. Boletin Tecnico / Technical Bulletin, 55(20), 599-604.
https://www.researchgate.net/publication/322365436_Application_of_GIS_technology_in_tourism_route_design - Lorscheid, I., Heine, B.-O., & Meyer, M. (2012). Opening the «black box» of simulations: increased transparency and effective communication through the systematic design of experiments. Computational and Mathematical Organization Theory, 18, 22-62.
https://doi.org/10.1007/s10588-011-9097-3 - Pai, P.-F., Hung, K.-C., & Lin, K.-P. (2014). Tourism demand forecasting using novel hybrid system. Expert Systems with Applications, 41(8), 3691-3702.
https://doi.org/10.1016/j.eswa.2013.12.007 - Papapanagiotou, V., Montemanni, R., & Gambardella, L. M. (2015). Hybrid sampling-based evaluators for the orienteering problem with stochastic travel and service times. Journal of Traffic and Logistics Engineering, 3(2), 108-114.
https://doi.org/10.12720/jtle.3.2.108-114 - Schwartz, Z., Uysal, M., Webb, T., & Altin, M. (2016). Hotel daily occupancy forecasting with competitive sets: A recursive algorithm. International Journal of Contemporary Hospitality Management, 28(2), 267-285.
https://doi.org/10.1108/IJCHM-10-2014-0507 - Vansteenwegen, P., Souffriau, W., Vanden Berghe, G., & Van Oudheusden, D. (2009). Metaheuristics for tourist trip planning. In Sörensen K., Sevaux M., Habenicht W., Geiger M. (Eds.), Metaheuristics in the Service Industry. Lecture Notes in Economics and Mathematical Systems Springer, Vol. 624, (pp. 15-31). Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-642-00939-6_2 - Vdovenko, N. M. (2015). Mechanisms of regulatory policy application in agriculture. Economic Annals-XXI, 5-6, 53-56.
https://ea21journal.world/index.php/ea-v151-13 - Weyland, D., Montemanni, R., & Gambardella, L. M. (2013). Heuristics for the probabilistic traveling salesman problem with deadlines based on quasi-parallel Monte Carlo sampling. Computers and Operations Research, 40(7), 1661-1670.
https://doi.org/10.1016/j.cor.2012.12.015 - Zhang, G., Wu, J., Pan, B., Li, J., Ma, M., Zhang, M., & Wang, J. (2017). Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model. Tourism Economics, 23(7), 1496-1514.
https://doi.org/10.1177/1354816617706852 - Zhang, M., Li, J., Pan, B., & Zhang, G. (2018). Weekly Hotel Occupancy Forecasting of a Tourism Destination. Sustainability, 10, 43-51.
https://doi.org/10.3390/su10124351
Received 24.04.2021
Received in revised form 20.05.2021
Accepted 30.05.2021
Available online 10.08.2021