Journals / Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi / 2026 / Cilt: 7 Sayı: 1

Kampüs içi ring seferi gerçekleştirecek servis filosunun tamsayılı bir programlama modeli ile optimizasyonu

Ömer Algorabi(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ)0000-0002-2016-8674
Yusuf Sait Türkan(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ)0000-0001-7240-183X
Amir Foroumandı(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA)0000-0001-6382-1314
Pages
43–55
Publication Date
2024-03-25
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Ticari çözücü ile elde edilen sonuçlara göre; üniversite yönetiminin ring seferlerine olan talebi karşılayabilmesi için toplamda 13 araç kiralaması gerekmektedir.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Bu çalışmada, İstanbul’daki bir üniversitenin ana kampüsünde gerçekleştirilen ring seferlerinin en ekonomik şekilde gerçekleştirilebilmesi adına bir matematiksel model geliştirilmiş, farklı araç kısıtları ve maliyetleri gözetilerek tanımlı seferlere atamalar yapılmıştır.
📊
Key Findings & Data:Bu amaçla tanımlı saatlerde ve güzergahlardaki taşıma talebinin eksiksiz sağlanmasına yönelik en uygun otobüs filosunun seçilmesi ve filodaki araçların ataması gerçekleştirilmiştir.
💡
Conclusion & Impact:Ticari çözücü ile elde edilen sonuçlara göre; üniversite yönetiminin ring seferlerine olan talebi karşılayabilmesi için toplamda 13 araç kiralaması gerekmektedir.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Üniversite otobüs çizelgeleme · tamsayılı programlama · toplu taşımada optimizasyon

Özet

Bu çalışmada, İstanbul’daki bir üniversitenin ana kampüsünde gerçekleştirilen ring seferlerinin en ekonomik şekilde gerçekleştirilebilmesi adına bir matematiksel model geliştirilmiş, farklı araç kısıtları ve maliyetleri gözetilerek tanımlı seferlere atamalar yapılmıştır. Bu amaçla tanımlı saatlerde ve güzergahlardaki taşıma talebinin eksiksiz sağlanmasına yönelik en uygun otobüs filosunun seçilmesi ve filodaki araçların ataması gerçekleştirilmiştir. Tam sayılı bir programlama modeli ile optimum çözüm aranan problemde, tüm taleplerin karşılandığı minimum maliyetli bir çözüm elde edilmiştir. Ele alınan problemde, otobüs filosunun seçilmesi ve çizelgelemesi için deterministik bir optimizasyon modeli önerilmiştir. Minimum otobüs kiralama maliyetini veren çözümü elde etmek için bir ticari çözücü kullanılmıştır. Ticari çözücü ile elde edilen sonuçlara göre; üniversite yönetiminin ring seferlerine olan talebi karşılayabilmesi için toplamda 13 araç kiralaması gerekmektedir.

🔖

Cite This Article

Algorabi, Ö., Türkan, Y. S., Foroumandı, A. (2024). Kampüs içi ring seferi gerçekleştirecek servis filosunun tamsayılı bir programlama modeli ile optimizasyonu. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, Cilt: 7 Sayı: 1, 43–55. https://doi.org/10.51513/jitsa.1330207

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (45)

  1. Bao, D., Gu, J., Di, Z., ve Zhang, T. (2018). Optimization of airport shuttle bus routes based on travel time reliability. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/2369350
  2. Beaudry, A., Laporte, G., Melo, T., ve Nickel, S. (2010). Dynamic transportation of patients in hospitals. OR spectrum, 32(1), 77-107. https://doi.org/10.1007/s00291-008-0135-6
  3. Becker, A. J., ve Teal, R. F. (2011). Next-Generation General Public Demand Responsive Transportation. Transportation Research Board 90th Annual MeetingTransportation Research Board, January, (11-4065).
  4. Bögl, M., Doerner, K. F., & Parragh, S. N. (2015). The school bus routing and scheduling problem with transfers. Networks, 65(2), 180-203. https://doi.org/10.1002/net.21589
  5. Caceres, H., Batta, R., ve He, Q. (2017). School bus routing with stochastic demand and duration constraints. Transportation science, 51(4), 1349-1364. https://doi.org/10.1287/trsc.2016.0721 Chang, S. K. (1990). Analytic optimization of bus systems in heterogeneous environments. University of Maryland, College Park.
  6. Chang, S. K., ve Schonfeld, P. M. (1991a). Optimization models for comparing conventional and subscription bus feeder services. Transportation Science, 25(4), 281-298. https://doi.org/10.1287/trsc.25.4.281
  7. Chang, S. K., ve Schonfeld, P. M. (1991b). Integration of fixed-and flexible-route bus systems. Transportation Research Record, (1308). https://trid.trb.org/view/364491
  8. Chang, S. K., ve Schonfeld, P. M. (1991c). Multiple period optimization of bus transit systems. Transportation Research Part B: Methodological, 25(6), 453-478. https://doi.org/10.1016/0191-2615(91)90038-K
  9. Chen, X., Kong, Y., Dang, L., Hou, Y., ve Ye, X. (2015). Exact and metaheuristic approaches for a bi-objective school bus scheduling problem. PloS one, 10(7), e0132600. https://doi.org/10.1371/journal.pone.0132600
  10. Chen, W., Yang, C., Feng, F., ve Chen, Z. (2012). An improved model for headway-based bus service unreliability prevention with vehicle load capacity constraint at bus stops. Discrete Dynamics in Nature and Society, 2012. https://doi.org/10.1155/2012/313518
  11. Ciaffi, F., Cipriani, E., ve Petrelli, M. (2012). Feeder bus network design problem: A new metaheuristic procedure and real size applications. Procedia-Social and Behavioral Sciences, 54, 798-807. https://doi.org/10.1016/j.sbspro.2012.09.796
  12. Dantzig, G., Fulkerson, R., ve Johnson, S. (1954). Solution of a large-scale traveling-salesman problem. Journal of the operations research society of America, 2(4), 393-410. https://doi.org/10.1287/opre.2.4.393
  13. de Souza Lima, F. M., Pereira, D. S., da Conceição, S. V., ve de Camargo, R. S. (2017). A multi-objective capacitated rural school bus routing problem with heterogeneous fleet and mixed loads. 4OR, 15(4), 359-386. https://doi.org/10.1007/s10288-017-0340-8
  14. Detti, P., Papalini, F., ve de Lara, G. Z. M. (2017). A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare. Omega, 70, 1-14. https://doi.org/10.1016/j.omega.2016.08.008
  15. Diana, M., Quadrifoglio, L., ve Pronello, C. (2009). A methodology for comparing distances traveled by performance-equivalent fixed-route and demand responsive transit services. Transportation planning and technology, 32(4), 377-399. https://doi.org/10.1080/03081060903119618
  16. Euchi, J., ve Mraihi, R. (2012). The urban bus routing problem in the Tunisian case by the hybrid artificial ant colony algorithm. Swarm and Evolutionary Computation, 2, 15-24. https://doi.org/10.1016/j.swevo.2011.10.002
  17. Feillet, D., Garaix, T., Lehuédé, F., Péton, O., ve Quadri, D. (2014). A new consistent vehicle routing problem for the transportation of people with disabilities. Networks, 63(3), 211-224. https://doi.org/10.1002/net.21538
  18. Fu, L., ve Ishkhanov, G. (2004). Fleet size and mix optimization for paratransit services. Transportation Research Record, 1884(1), 39-46. https://doi.org/10.3141/1884-05
  19. Horn, M. E. (2002). Multi-modal and demand-responsive passenger transport systems: a modelling framework with embedded control systems. Transportation Research Part A: Policy and Practice, 36(2), 167-188. https://doi.org/10.1016/S0965-8564(00)00043-4
  20. Jung, J., ve Jayakrishnan, R. (2011). High-coverage point-to-point transit: study of path-based vehicle routing through multiple hubs. Transportation Research Record, 2218(1), 78-87. https://doi.org/10.3141/2218-09
  21. Ker, K. U. R. T., Kuo, H. F., ve Schondeld, M. (1995). Optimal mixed bus fleet for urban operations. Transportation research record, 1503, 39-48. https://onlinepubs.trb.org/Onlinepubs/trr/1995/1503/1503-006.pdf
  22. Kergosien, Y., Gendreau, M., Ruiz, A., ve Soriano, P. (2014). Managing a fleet of ambulances to respond to emergency and transfer patient transportation demands. In Proceedings of the international conference on health care systems engineering (pp. 303-315). Springer, Cham.
  23. Kergosien, Y., Lenté, C., ve Billaut, J. C. (2008A tabu search algorithm for solving a transportation problem of patients between care units. In International Workshop on Scheduling in Healthcare systems (SCHEALS'08), Proceedings of the 1st International Conference on Applied Operational Research (ICAOR 2008), September, Lecture Notes in Management Science (Vol. 1, pp. 18-31).
  24. Kim, T., ve Haghani, A. (2011). Model and algorithm considering time-varying travel times to solve static multidepot dial-a-ride problem. Transportation research record, 2218(1), 68-77. https://doi.org/10.3141/2218-08
  25. Kim, M., ve Schonfeld, P. (2012). Conventional, flexible, and variable-type bus services. Journal of Transportation Engineering, 138(3), 263.: 10.1061/(ASCE)TE.1943-5436.0000326
  26. Köksal Ahmed, E., Li, Z., Veeravalli, B., & Ren, S. (2022). Reinforcement learning-enabled genetic algorithm for school bus scheduling. Journal of Intelligent Transportation Systems, 26(3), 269-283. https://doi.org/10.1080/15472450.2020.1852082
  27. Kuan, S. N., Ong, H. L., ve Ng, K. M. (2006). Solving the feeder bus network design problem by genetic algorithms and ant colony optimization. Advances in Engineering Software, 37(6), 351-359. https://doi.org/10.1016/j.advengsoft.2005.10.003
  28. Luo, Y., ve Schonfeld, P. (2011a). Online rejected-reinsertion heuristics for dynamic multivehicle dial-a-ride problem. Transportation research record, 2218(1), 59-67. https://doi.org/10.3141/2218-07
  29. Luo, Y., ve Schonfeld, P. (2011b) Performance metamodels for dial-a-ride services with time constraints. Transportation Research Board 90th Annual Meeting, online, compendium. MathWorks, 2011. MATLAB R2011b Documentation. http://www.mathworks.com.
  30. Melachrinoudis, E., Ilhan, A. B., ve Min, H. (2007). A dial-a-ride problem for client transportation in a health-care organization. Computers ve Operations Research, 34(3), 742-759. https://doi.org/10.1016/j.cor.2005.03.024
  31. Minocha, B., ve Tripathi, S. (2014). Solving school bus routing problem using hybrid genetic algorithm: a case study. In Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012 (pp. 93-103). Springer, New Delhi.
  32. Molenbruch, Y., Braekers, K., Caris, A., ve Berghe, G. V. (2017). Multi-directional local search for a bi-objective dial-a-ride problem in patient transportation. Computers ve Operations Research, 77, 58-71. https://doi.org/10.1016/j.cor.2016.07.020
  33. Osman, M. F. S., ve Al-Sanousi, M. M. (2015). A deterministic IP model for optimizing bus scheduling in a private transportation system. In 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), December, (pp. 244-248). IEEE.
  34. Peker, G., ve ELİİYİ, D. T. (2022). Shuttle bus service routing: A systematic literature review. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 160-172. https://dergipark.org.tr/en/pub/pajes/issue/68582/1079523
  35. Quadrifoglio, L., Dessouky, M. M., ve Ordóñez, F. (2008). Mobility allowance shuttle transit (MAST) services: MIP formulation and strengthening with logic constraints. European Journal of Operational Research, 185(2), 481-494. https://doi.org/10.1016/j.ejor.2006.12.030
  36. Quadrifoglio, L., Hall, R. W., ve Dessouky, M. M. (2006). Performance and design of mobility allowance shuttle transit services: bounds on the maximum longitudinal velocity. Transportation science, 40(3), 351-363. https://doi.org/10.1287/trsc.1050.0137
  37. Quadrifoglio, L., ve Li, X. (2009). A methodology to derive the critical demand density for designing and operating feeder transit services. Transportation Research Part B: Methodological, 43(10), 922-935. https://doi.org/10.1016/j.trb.2009.04.003
  38. Sarubbi, J. F., Mesquita, C. M., Wanner, E. F., Santos, V. F., & Silva, C. M. (2016, April). A strategy for clustering students minimizing the number of bus stops for solving the school bus routing problem. In NOMS 2016-2016 IEEE/IFIP network operations and management symposium (pp. 1175-1180). IEEE. doi: 10.1109/NOMS.2016.7502983.
  39. Schittekat, P., Kinable, J., Sörensen, K., Sevaux, M., Spieksma, F., ve Springael, J. (2013). A metaheuristic for the school bus routing problem with bus stop selection. European Journal of Operational Research, 229(2), 518-528. https://doi.org/10.1016/j.ejor.2013.02.025
  40. Shang, P., Yang, L., Zeng, Z., & Tong, L. C. (2021). Solving school bus routing problem with mixed-load allowance for multiple schools. Computers & Industrial Engineering, 151, 106916. https://doi.org/10.1016/j.cie.2020.106916
  41. Spada, M., Bierlaire, M., ve Liebling, T. M. (2005). Decision-aiding methodology for the school bus routing and scheduling problem. Transportation Science, 39(4), 477-490. https://doi.org/10.1287/trsc.1040.0096
  42. Ünsal, Ö., ve Yiğit, T. (2018). Yapay zeka ve kümeleme teknikleri kullanilarak geliştirilen yöntem ile okul servisi rotalama probleminin optimizasyonu. Mühendislik Bilimleri ve Tasarım Dergisi, 6(1), 7-20. https://doi.org/10.21923/jesd.340220
  43. Wanigasooriya, J., ve Fernando, T. G. I. (2013). Multi-vehicle passenger allocation and route optimization for employee transportation using genetic algorithms. International Journal of Computer Applications, 64(20), 1-9. 10.5120/10747-5712
  44. Yalçındağ, S. (2020). Employee shuttle bus routing problem. Mugla Journal of Science and Technology, 6(1), 105-111. https://doi.org/10.22531/muglajsci.691517
  45. Kim, M., ve Schonfeld, P. (2012). Conventional, flexible, and variable-type bus services. Journal of Transportation Engineering, 138(3), 263.: 10.1061/(ASCE)TE.1943-5436.0000326