Personel Servisi Rotalama Problemi: Bir Vaka Çalışması

Öz

Bu makale, bir personel servisi rotalama probleminin gerçek hayattaki uygulamasını açıklamaktadır. Söz konusu problem, özel kısıtlamaları olan bir tür araç rotalama problemidir. Problemi çözmek için, servis duraklarına yürüme süreleri ve servislerde geçirilen süreler de dahil olmak üzere çalışanların toplam seyahat sürelerini en aza indirmeyi amaçlayan bir matematiksel model geliştirilmiştir. Belirlenen duraklar arasındaki süreler, her bir servisin her bir durakta geçirdiği süre ve servislerin başlangıç noktalarından varış noktasına kadar geçirdiği toplam seyahat süreleri göz önünde bulundurularak, bu süreler modele dahil edilmiştir. Hedef programlama modeli, ticari çözücü IBM ILOG CPLEX Optimization Studio kullanılarak kodlanmış ve çözülmüştür. Duraklar arasındaki gerçek süreler ile çalışanların yürüme süreleri, şirket tarafından sağlanan gerçek hayat verilerine göre hesaplanmıştır. Modele küme örtüleme kısıtlamaları dahil edilerek çalışanların otobüs duraklarına yürüme süreleri de düzenlenmiştir. Modelden elde edilen sayısal sonuçlar şirketin mevcut uygulaması ile karşılaştırıldığında, toplam seyahat süresindeki tasarrufun oldukça çarpıcı olduğu gözlemlenmiştir.

Anahtar Kelimeler

• Personel servisi rotalama problemi
• Ağırlıklıklı-öncelikli hedef programlama
• Matematiksel modelleme
• Optimizasyon

Employee Shuttle Bus Routing Problem: A Case Study

Abstract

This paper describes the real-life application of a personnel service shuttle routing problem. The problem in question is a type of vehicle routing problem with special constraints. To solve the problem, a mathematical model was developed, which aims to minimize the total travel time of employees, including the walking times to the shuttle-stops and the times spent on the shuttles. These times were added in the model by considering the times between the designated stops, the times each shuttle spends on each stop and the total travel times of the shuttles from the starting points to the destination point. The goal programming model was coded and solved using the commercial solver IBM ILOG CPLEX Optimization Studio. The actual times between the shuttle bus stops and the employee walking times were calculated according to the real-life data provided by the company. The walking times of the employees to the bus stops were also regulated via the inclusion of some set covering constraints in the model. When the numerical results from the model were compared to the current practice of the company, it has been observed that the savings in total travel time were quite significant.

Keywords

• Employee service shuttle routing problem
• Weighted-preemptive goal programming
• Mathematical modeling
• Optimization

Kaynakça

1. Baldacci, R., Maniezzo, V., & Mingozzi, A. (2004). An exact method for the car pooling problem based on lagrangean column generation. Operations Research, 52(3), 422–439. http://doi.org/10.1287/opre.1030.0106
2. Boffey, B., García, F. R. F., Laporte, G., Mesa, J. A., & Pelegrín, B. P. (1995). Multiobjective routing problems. Top, 3(2), 167–220. http://doi.org/10.1007/BF02568585
3. Calvete, H. I., Galé, C., Oliveros, M. J., & Sánchez-Valverde, B. (2007). A goal programming approach to vehicle routing problems with soft time windows. European Journal of Operational Research, 177(3), 1720–1733. http://doi.org/10.1016/j.ejor.2005.10.010
4. Charnes, A., & Cooper, W. W. (1977). Goal programming and multiple objective optimizations: Part 1. European Journal of Operational Research, 1(1), 39–54. http://doi.org/10.1016/S0377-2217(77)81007-2
5. Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal Estimation of Executive Compensation by Linear Programming. Management Science, 1(2), 138–151. http://doi.org/10.1287/mnsc.1.2.138
6. Chitty, D. M., & Hernandez, M. L. (2004). A hybrid ant colony optimisation technique for dynamic vehicle routing. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3102, 48–59. http://doi.org/10.1007/978-3-540-24854-5_5
7. Ghoseiri, K., & Ghannadpour, S. F. (2010). Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Applied Soft Computing Journal, 10(4), 1096–1107. http://doi.org/10.1016/j.asoc.2010.04.001
8. Giannikos, I. (1998). A multiobjective programming model for locating treatment sites and routing hazardous wastes. European Journal of Operational Research, 104(2), 333–342. http://doi.org/10.1016/S0377-2217(97)00188-4

9. Hashi, E. K., Hasan, M. R., & Zaman, M. S. U. (2015). A heuristic solution of the vehicle routing problem to optimize the office bus routing and scheduling using Clarke&Wright’s savings algorithm. In 1st International Conference on Computer and Information Engineering, ICCIE 2015 (pp. 13–16). http://doi.org/10.1109/CCIE.2015.7399306
10. Ignizio, J. P. (1976). Goal programming and extensions. Lexington Books.
11. Jozefowiez, N., Semet, F., & Talbi, E. G. (2002). Parallel and hybrid models for multi-objective optimization: Application to the vehicle routing problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2439, pp. 271–280). http://doi.org/10.1007/3-540-45712-7_26
12. Lee, S. M., & Clayton, E. R. (1972). a Goal Programming Model for Academic Resource Allocation. Management Science. http://doi.org/10.1287/mnsc.18.8.b395
13. Leksakul, K., Smutkupt, U., Jintawiwat, R., & Phongmoo, S. (2017). Heuristic approach for solving employee bus routes in a large-scale industrial factory. Advanced Engineering Informatics, 32, 176–187. http://doi.org/10.1016/j.aei.2017.02.006
14. Park, Y. B., & Koelling, C. P. (1986). A solution of vehicle routing problems in a multiple objective environment. Engineering Costs and Production Economics, 10(1), 121–132. http://doi.org/10.1016/0167-188X(86)90033-9
15. Peker, G., & ELİİYİ, D. T. (n.d.). Shuttle bus service routing: A systematic literature review. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 160–172.
16. Perugia, A., Moccia, L., Cordeau, J. F., & Laporte, G. (2011). Designing a home-to-work bus service in a metropolitan area. Transportation Research Part B: Methodological, 45(10), 1710–1726. http://doi.org/10.1016/j.trb.2011.05.025
17. Pitakaso, R., Sethanan, K., & Srijaroon, N. (2019). Modified differential evolution algorithms for multi-vehicle allocation and route optimization for employee transportation. Engineering Optimization, 52(7), 1225–1243. http://doi.org/10.1080/0305215X.2019.1640691
18. Purba, A. P., Siswanto, N., & Rusdiansyah, A. (2020). Routing and scheduling employee transportation using tabu search. In AIP Conference Proceedings (Vol. 2217). http://doi.org/10.1063/5.0000766
19. Sa’Adah, S., Ross, P., & Paechter, B. (2004). Improving vehicle routing using a customer waiting time colony. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3004, 188–198. http://doi.org/10.1007/978-3-540-24652-7_19
20. Tan, K. C., Chew, Y. H., & Lee, L. H. (2006). A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Computational Optimization and Applications, 34(1), 115–151. http://doi.org/10.1007/s10589-005-3070-3
21. Wanigasooriya, J., & G I Fernando, T. (2013). Multi-Vehicle Passenger Allocation and Route Optimization for Employee Transportation using Genetic Algorithms. International Journal of Computer Applications, 64(20), 1–9. http://doi.org/10.5120/10747-5712
22. Wolfler Calvo, R., de Luigi, F., Haastrup, P., & Maniezzo, V. (2004). A distributed geographic information system for the daily car pooling problem. Computers and Operations Research, 31(13), 2263–2278. http://doi.org/10.1016/S0305-0548(03)00186-2
23. Yalçındağ, S. (2020). Employee shuttle bus routing problem. Mugla Journal of Science and Technology. http://doi.org/10.22531/muglajsci.691517