SavedAdd your journal

Journals / Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Cilt: 12 Sayı: 1

Analyses on the Effects of Time Windows Choices on Sustainable Vehicle Allocation Problems

Aslıhan Aydın(HACETTEPE UNIVERSITY)0000-0003-4747-7618
Damla Benli(GEBZE TECHNICAL UNIVERSITY)0000-0002-3882-4115
Mustafa Çimen(HACETTEPE UNIVERSITY)0000-0001-8155-9145
Mehmet Soysal(HACETTEPE UNIVERSITY)0000-0002-1570-660X
Pages
157–175
Publication Date
2022-06-30
🏛️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)

The numerical results show that loosened time windows have the potential to provide economic and environmental benefits in the addressed problem.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to analyze the effects of the time windows choices in Vehicle Allocation Problems with respect to economic, social, and environmental indicators.
📊
Key Findings & Data:As far as we know, such an attempt does not exist in the related literature.
💡
Conclusion & Impact:The numerical results show that loosened time windows have the potential to provide economic and environmental benefits in the addressed problem.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Araç Tahsisi · Zaman Penceresi · Karışık Tam Sayılı Programlama

Abstract

This study aims to analyze the effects of the time windows choices in Vehicle Allocation Problems with respect to economic, social, and environmental indicators. As far as we know, such an attempt does not exist in the related literature. For this purpose, we first present a Mixed Integer Programming model for a generic Vehicle Allocation Problem with profit maximization objective under time windows constraints. This model is used to conduct analyses for demonstrating the applicability of the model in practice and to reveal potential effects of the time windows choices on economic, social, and environmental outcomes of allocation decisions in Vehicle Allocation Problems. The numerical results show that loosened time windows have the potential to provide economic and environmental benefits in the addressed problem. We also propose a collaboration policy attractive to both the transportation company and its customers through price discounts, which improves economic and social performances of the addressed transportation logistics network.

Özet

Bu çalışma, Araç Tahsis Problemlerinde zaman penceresi seçimlerinin ekonomik, sosyal ve çevresel göstergeler açısından etkilerini incelemeyi amaçlamaktadır. Bildiğimiz kadarıyla ilgili literatürde böyle bir çalışma bulunmamaktadır. Bu amaçla, ilk olarak, zaman pencereleri kısıtlamaları altında kar maksimizasyonu hedefi olan genel bir Araç Tahsis Problemi için bir Karma Tam sayılı Programlama modeli sunuyoruz. Bu model, modelin pratikte uygulanabilirliğini ortaya koymak için analizler yapmak ve Araç Tahsis Problemlerinde zaman penceresi seçimlerinin tahsis kararlarının ekonomik, sosyal ve çevresel sonuçları üzerindeki potansiyel etkilerini ortaya çıkarmak için kullanılmıştır. Sayısal sonuçlar, ele alınan problemde genişletilmiş zaman pencerelerinin ekonomik ve çevresel faydalar sağlama potansiyeline sahip olduğunu göstermektedir. Ayrıca, iskontolu fiyat uygulaması yoluyla hem nakliye şirketi hem de müşterileri için cazip, nakliye lojistik ağının ekonomik ve sosyal performanslarını iyileştiren bir iş birliği politikası önerilmektedir.

🔖

Cite This Article

Aydın, A., Benli, D., Çimen, M., Soysal, M. (2022). Analyses on the Effects of Time Windows Choices on Sustainable Vehicle Allocation Problems. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 12 Sayı: 1, 157–175. https://doi.org/10.18074/ckuiibfd.970762

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (16)

  1. Beaujon, G. J., & Turnquist, M. A. (1991). A model for fleet sizing and vehicle allocation. Transportation Science, 19-45.
  2. Chopra, S., Meind, P., & Kalra, D. V. (2013). Supply chain management: strategy, planning, and operation. Pearson.
  3. DEFRA, U. (2007). Guidelines to Defra’s GHG conversion factors for company reporting, annexes updated June 2007.
  4. Ghiani, G., Laporte, G., & Musmanno, R. (2004). Introduction to logistics systems planning and control. John Wiley & Sons.
  5. Godfrey, G. A., & Warren, B. P. (2002). An adaptive dynamic programming algorithm for dynamic fleet management, II: Multiperiod travel times. Transportation Science, 40-54.
  6. Godfrey, G. A., & Warren, B. P. (2002). An adaptive dynamic programming algorithm for dynamic fleet management, I: Single period travel times. Transportation Science, 21-39.
  7. Goodland, R. (2002). Sustainability: human, social, economic and environmental. Encyclopedia of global environmental change, 481-491.
  8. Holmberg, K., Joborn, M., & Lundgren, J. T. (1970). A model for allocation of empty freight cars with capacity restrictions and fixed costs. WIT Transactions on The Built Environment.
  9. Ichoua, S., Gendreau, M., & Potvin, J.-Y. (2003). Vehicle dispatching with time-dependent travel times. European journal of operational research, 379-396.
  10. Kaewpuang, R., Niyato, D., Tan, P.-S., & Wang, P. (2016). Cooperative management in full-truckload and less-than-truckload vehicle system. IEEE Transactions on Vehicular Technology, 5707-5722.
  11. Kazanç, H. C. (2019). Sürdürülebilir Yük Taşımacılığı İçin Bir Karar Destek Modeli Önerisi.
  12. MAN. (2020, July 27). Retrieved from MAN Web site: https://www.man.com.tr
  13. Ongarj, L., & Ongkunaruk, P. (2013). An integer programming for a bin packing problem with time windows: A case study of a Thai seasoning company. 10th International Conference on Service Systems and Service Management (pp. 826-830). IEEE.
  14. Philip, C. E., & Sussman, J. M. (1977). Inventory model of the railroad empty-car distribution process. Transportation Research Record.
  15. Shi, N., Song, H., & Powell, W. B. (2014). The dynamic fleet management problem with uncertain demand and customer chosen service level. International Journal of Production Economics, 110-121.
  16. White, W. W., & Bomberault, A. M. (1969). A network algorithm for empty freight car allocation. IBM Systems Journal, 147-169.