A mobile application-based decision support system for routing and decision making problems
Yıl 2023,
Cilt: 9 Sayı: 3, 637 - 647, 20.09.2023
Metehan Bolat
,
Oğuz Bedel
,
Kutay Çetinkurt
,
Fehmi Özsoydan
Öz
Industry 4.0 applications and related topics open up new opportunities in problem solving for states, companies, production systems and individuals. These developments make data gathering and processing easier. Moreover, mobile devices offer more sophisticated approaches since they can gather data and process the collected data by various algorithms, which are embedded to devices via applications. Appro-priate problem related algorithms can be coded and they can be deployed to mobile devices to increase mobility and ease of use for end users. In the present paper, a prototype of mobile application-based decision support system is developed for industrial systems including routing and multi-criteria decision making problems. The developed application is comprised of several modules including registry and signup modules as well as problem solving modules. Problem solving modules can gather data from both user and outer sources such as GPS in order to solve both routing problems and multi-criteria decision-making problems. The mentioned application adopts a Simulated Annealing Algorithm to find promising routes for the users, while multi-criteria decision-making module uses both Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the choice of the users. The proposed application is coded on Java and Python due to their strong integration capabilities with Android. According the results of the developed prototype, the proposed application offer promising results and ease of use.
Kaynakça
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İlhan, İ. (2017). An Application on Mobile Devices with Android. Applied Artificial Intelligence, 332-345. https://doi.org/10.1080/08839514.2017.1339983
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Yıl 2023,
Cilt: 9 Sayı: 3, 637 - 647, 20.09.2023
Metehan Bolat
,
Oğuz Bedel
,
Kutay Çetinkurt
,
Fehmi Özsoydan
Kaynakça
- Andronie, M., Lăzăroiu, G., Ștefănescu, R., Ionescu, L., & Cocoșatu, M. (2021). Neuromanagement decision-making and cognitive algorithmic processes in the technological adoption of mobile commerce apps. Oeconomia Copernicana, 12(4), 1033-1062. https://doi.org/10.24136/oc.2021.034
- Baykasoğlu, A., & Özsoydan, F. B. (2016). An improved approach for determination of index positions on CNC magazines with cutting tool duplications by integrating shortest path algorithm. International Journal of Production Research, 742-760. https://doi.org/10.1080/00207543.2015.1055351
- Baykasoğlu, A., & Özsoydan, F. B. (2017). Minimizing tool switching and indexing times with tool duplications in automatic machines. The International Journal of Advanced Manufacturing Technology, 1775-1789. https://doi.org/10.1007/s00170-016-9194-z
- Baykasoğlu, A., & Özsoydan, F. B. (2018). Minimisation of non-machining times in operating automatic tool changers of machine tools under dynamic operating conditions. International Journal of Production Research, 1548-1564. https://doi.org/10.1080/00207543.2017.1357861
- Belanche, D., Flavián, M., & Pérez-Rueda, A. (2020). Mobile apps use and WOM in the food delivery sector: the role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability, 12(10), 4275. https://doi.org/10.3390/su12104275
- Berkeley. (2021). Berkeley Extension. Retrieved from: https://bootcamp.berkeley.edu/blog/most-in-demand-programming-languages/
- Bourouis, A., Feham, M., Hossain, M. A., & Zhang, L. (2014). An intelligent mobile based decision support system for retinal disease diagnosis. Decision Support Systems, 341-350. https://doi.org/10.1016/j.dss.2014.01.005
- Darko, A., Chan, A. P., Effah, E. A., Owusu, E. K., Pärn, E. A., & Edwards, D. J. (2018). Review of application of analytic hierarchy process (AHP) in construction. International Journal of Construction Management, 436-452. https://doi.org/10.1080/15623599.2018.1452098
- Dizman, H., & Özen, E. (2017). Küçük İşletmelerde Karar Destek Sistemlerinin Farkındalığı Üzerine Bir Araştırma: Yerel Bir Yaklaşım (Ege Bölgesi). Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 137-152. https://doi.org/10.29106/fesa.344962
- Enrique, M. & Pereyra-Rojas, M. (2017). Group Decision-Making in AHP. In Practical Decision Making, by Enrique Mu, 81-90. Pittsburgh. https://doi.org/10.1007/978-3-319-33861-3
- Filippopulos, F. M., Huppert, D., Brandt, T., Hermann, M., Franz, M., Fleischer, S., & Grill, E. (2020). Computerized clinical decision system and mobile application with expert support to optimize management of vertigo in primary care: study protocol for a pragmatic cluster-randomized controlled trial. Journal of neurology, 267, 45-50. https://doi.org/10.1007/s00415-020-10078-0
- Gülenç, İ. F., & Bilgin, G. (2010). Yatırım Kararları İçin Bir Model Önerisi: Ahp Yöntemi - A Model Proposal For Investment Decisions: Ahp Method. Öneri Dergisi, 97-107. https://doi.org/10.14783/od.v9i34.1012000233
Harper, D., & Stockman, L. M. (2014). obliquity. obliquity. Retrieved from: https://www.obliquity.com/computer/fortran/history.html
- Hyun, S., Yang, S. M., Kim, J., Kim, K. S., Shin, J. H., Lee, S. M., ... & Fleisher, D. H. (2021). Development of a mobile computing framework to aid decision-making on organic fertilizer management using a crop growth model. Computers and Electronics in Agriculture, 181, 105936. https://doi.org/10.1016/j.compag.2020.105936
İlhan, İ. (2017). An Application on Mobile Devices with Android. Applied Artificial Intelligence, 332-345. https://doi.org/10.1080/08839514.2017.1339983
- James, A. T., Vaidya, D., Sodawala, M., & Verma, S. (2021). Selection of bus chassis for large fleet operators in India: An AHP-TOPSIS approach. Expert Systems with Applications, 186, 115760. https://doi.org/10.1016/j.eswa.2021.115760
- Kirkpatrick, Scott, C. D. Jr. Gelatt, and M. P. & Jr. Vecchi. (1983). Optimization by Simulated Annealing. Science 671-680. https://doi.org/10.1126/science.220.4598.671
- Küçükoğlu, İ., Dewil, R., & Cattrysse, D. (2019). Hybrid simulated annealing and tabu search method for the electric travelling salesman problem with time windows and mixed charging rates. Expert Systems with Applications, 279-303. https://doi.org/10.1016/j.eswa.2019.05.037
- Liang, Y., Marchese, M., Shi, X., & Yang, J. (2008). An ant colony optimization method for generalized TSP problem. Progress in Natural Science, 1417-1422. https://doi.org/10.1016/j.pnsc.2008.03.028
- Mathirajan, M., Devadas, R., & Ramanathan, R. (2021). Transport analytics in action: A cloud-based decision support system for efficient city bus transportation. Journal of Information and Optimization Sciences, 42(2), 371-416. https://doi.org/10.1080/02522667.2019.1688948
- Nuanmeesri, S. (2023). Mobile application for the purpose of marketing, product distribution and location-based logistics for elderly farmers. Applied Computing and Informatics, 19(1/2), 2-21. https://doi.org/10.1016/j.aci.2019.11.001
- Rajak, M., & Shaw, K. (2019). Evaluation and selection of mobile health (mHealth) applications using AHP and fuzzy TOPSIS. Technology in Society, 59, 101186. https://doi.org/10.1016/j.techsoc.2019.101186
- Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., & Bosnic, Z. (2018). AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture, 260-271. https://doi.org/10.1016/j.compag.2018.04.001
- Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research 9-26. https://doi.org/10.1016/0377-2217(90)90057-I
- Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021). Mobile data science and intelligent apps: concepts, AI-based modeling and research directions. Mobile Networks and Applications, 26, 285-303. https://doi.org/10.1007/s11036-020-01650-z
- Shah, A. M., Yan, X., Shah, S. A. A., & Ali, M. (2020). Customers' perceived value and dining choice through mobile apps in Indonesia. Asia Pacific Journal of Marketing and Logistics, 1-28. https://doi.org/10.1108/APJML-03-2019-0167
- Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y
- Thede, Scott. (2004). An introduction to genetic algorithms. Journal of Computing Sciences in Colleges. 20. htts://doi.org/10.5555/1040231.1040247
- Vitale, A., Festa, D. C., Guido, G., & Rogano, D. (2014). A Decision Support System based on smartphone probes as a tool to promote public transport. Procedia - Social and Behavioral Sciences, 224-231. https://doi.org/10.1016/j.sbspro.2014.01.055
- Xu, X., Yuan, H., Liptrott, M., & Trovati, M. (2018). Two phase heuristic algorithm for the multiple-travelling salesman problem. Methodologies and Application, 6567-6581. https://doi.org/10.1007/s00500-017-2705-5