مدل طراحی شبکه حمل‌ونقل ترکیبی تحت شرایط عدم قطعیت (موردمطالعه: حمل‌ونقل سیمان در کشور ایران)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشگاه علم و صنعت ایران.

2 دانشیار، دانشگاه علم و صنعت ایران.

چکیده

امروزه با جهانی‌شدن زنجیره‌های تأمین، استفاده از سیستم‌های حمل‌ونقل کارآمد برای توزیع کالاها می‌تواند تأثیر زیادی بر کاهش هزینه‌های لجستیکی و افزایش رضایت­مندی مشتریان بگذارد. در این راستا مراکز لجستیک بار علاوه بر اینکه زیرساخت‌های موردنیاز برای انتقال جریان بار از جاده به شبکه ریلی را فراهم می‌کنند، نقش بسزایی در کاهش هزینه‌های کل حمل‌ونقل با استفاده از اقتصاد در مقیاس و ایجاد ارزش‌افزوده برای کالاهای خام دارند. ازاین‌رو در این پژوهش با توجه به عدم قطعیت موجود در تقاضاها و هزینه های حمل‌ونقل، یک مدل ریاضی استوار برای طراحی شبکه حمل‌ونقل ترکیبی بار با استفاده از دو روش حمل‌ونقل ریل و جاده در سطح ملّی ارائه شده است. در مدل احتمالی مبتنی بر سناریوی ارائه‌شده، دو هدف موردتوجه قرار گرفته است؛ هدف نخست بر حداقل­کردن هزینه‌ها و هدف دوم از دیدگاه کاهش ریسک تصمیم‌گیری بر حداقل­کردن حداکثر تأسف سناریوهای محتمل در چارچوب برنامه‌ریزی استوار متمرکز است. در ادامه، برای نمایش اعتبار مدل ارائه شده و کارایی آن، حمل‌ونقل ترکیبی سیمان در ایران مورد بررسی قرار گرفته است. خروجی‌ها نشان می‌دهد که توسعه تعدادی از ایستگاه‌های راه‌آهن در کشور و انتقال حجم قابل‌توجهی از سیمان حمل­شده از جاده به شبکه ریلی، موجب کاهش قیمت این محصول استراتژیک می‌شود.

کلیدواژه‌ها

عنوان مقاله [English]

Multimodal Transportation Network Design Model under Uncertainty Conditions (Case Study: Cement Transportation in Iran)

نویسندگان [English]

  • Minoo Farazmand 1
  • Mir Saman Pishvaee 2
1 M.Sc. Student, Iran University of Science & Technology.
2 Associate Professor, Iran University of Science & Technology.
چکیده [English]

Today, with supply chain globalization, the use of efficient transportation systems to distribute goods have a significant impact on reducing logistics costs and increasing customer satisfaction. In this regard, logistics centers in addition to providing the necessary infrastructure for the flow of freight from the road to the rail network, play a significant role in Reduce total transportation costs and create surplus value for raw materials; Therefore, in this research, due to uncertainties in demand and transportation costs, a robust mathematical model is presented for designing a multimodal rail - road freight transportation network at the national level. In the scenario-based stochastic model, two objectives have been considered. The first objectives focuses on minimizing costs, and the second objective focuses on reducing risk-taking decisions to minimize the maximum relative regret of possible scenarios within the framework of robust mathematical programming. In order to demonstrate the validity of the model and its efficiency, the cement multimodal transportation in Iran has been investigated. Outputs show that the development of a number of railway stations and transfer of a significant amount of Cement shipped by road to the rail network will reduce the price of this strategic product.

کلیدواژه‌ها [English]

  • Multimodal Transportation Network Design
  • Multimodal Terminals Location
  • Scenario-Based Two-Stage Stochastic Programming
  • Minimizing Maximum Relative Regret

مراجع

1. Alem Tabriz, A., Roghanian, I., Hosseinzadeh, M. (2012). Design and optimization of reverse logistics network under uncertainty conditions using genetic algorithm. Industrial Management Perspective, (In Persian).
2. Alumur, S. A., Nickel, S., Rohrbeck, B., & Saldanha-da-Gama, F. (2018). Modeling congestion and service time in hub location problems. Applied Mathematical Modelling, 55, 13-32.
3. Arnold, P., Peeters, D., Thomas, I., & Marchand, H. (2001). Pour une localisation optimale des centres de transbordement intermodaux entre réseaux de transport: formulation et extensions. Canadian Geographer/Le Géographe canadien, 45(3), 427-436.
4. Assavapokee, T., Realff, M. J., Ammons, J. C., & Hong, I. H. (2008). Scenario relaxation algorithm for finite scenario-based min–max regret and min–max relative regret robust optimization. Computers & operations research, 35(6), 2093-2102.
5. Baykasoğlu, A., & Subulan, K. (2016). A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application. Transportation Research Part E: Logistics and Transportation Review, 95, 207-247.
6. Caris, A., Macharis, C., & Janssens, G. K. (2008). Planning problems in intermodal freight transport: accomplishments and prospects. Transportation Planning and Technology, 31(3), 277-302.
7. Correia, I., Nickel, S., & Saldanha-da-Gama, F. (2018). A stochastic multi-period capacitated multiple allocation hub location problem: Formulation and inequalities. Omega, 74, 122-134.
8. Dai, Q., Yang, J., & Li, D. (2018). Modeling a Three-Mode Hybrid Port-Hinterland Freight Intermodal Distribution Network with Environmental Consideration: The Case of the Yangtze River Economic Belt in China. Sustainability, 10(9), 3081.
9. Fazayeli, S., Eydi, A., & Kamalabadi, I. N. (2018). Location-routing problem in multimodal transportation network with time windows and fuzzy demands: Presenting a two-part genetic algorithm. Computers & Industrial Engineering, 119, 233-246.
10. Fotuhi, F., & Huynh, N. (2017). Reliable intermodal freight network expansion with demand uncertainties and network disruptions. Networks and Spatial Economics, 17(2), 405-433.
11. Fotuhi, F., & Huynh, N. (2018). A reliable multi-period intermodal freight network expansion problem. Computers & Industrial Engineering, 115, 138-150.
12. Gelareh, S., Monemi, R. N., & Nickel, S. (2015). Multi-period hub location problems in transportation. Transportation Research Part E: Logistics and Transportation Review, 75, 67-94.
13. Ghane-Ezabadi, M., & Vergara, H. A. (2016). Decomposition approach for integrated intermodal logistics network design. Transportation Research Part E: Logistics and Transportation Review, 89, 53-69.
14. Hu, L., Zhu, J. X., Wang, Y., & Lee, L. H. (2018). Joint design of fleet size, hub locations, and hub capacities for third-party logistics networks with road congestion constraints. Transportation Research Part E: Logistics and Transportation Review, 118, 568-588.
15. Ishfaq, R., & Sox, C. R. (2011). Hub location–allocation in intermodal logistic networks. European Journal of Operational Research, 210(2), 213-230.
16. Ji, S. F., & Luo, R. J. (2017). A Hybrid Estimation of Distribution Algorithm for Multi-Objective Multi-Sourcing Intermodal Transportation Network Design Problem Considering Carbon Emissions. Sustainability, 9(7), 1133.
17. Kouvelis, P., & Yu, G. (2013). Robust discrete optimization and its applications (Vol. 14). Springer Science & Business Media.
18. Li, S. X., Sun, S. F., Wang, Y. Q., Wu, Y. F., & Liu, L. P. (2017). A two-stage stochastic programming model for rail-truck intermodal network design with uncertain customer demand. Journal of Interdisciplinary Mathematics, 20(3), 611-621.
19. Lin, C. C., & Lee, S. C. (2018). Hub network design problem with profit optimization for time-definite LTL freight transportation. Transportation Research Part E: Logistics and Transportation Review, 114, 104-120.
20. Mostert, M., Caris, A., & Limbourg, S. (2017). Intermodal network design: a three-mode bi-objective model applied to the case of Belgium. Flexible Services and Manufacturing Journal, 1-24.
21. Nikbakhsh, E., Zegordi, S.H. (2015). Covering Hub location Problem under Disruption Conditions.  Industrial Management Perspective, 29-9 (In Persian).
22. Serper, E. Z., & Alumur, S. A. (2016). The design of capacitated intermodal hub networks with different vehicle types. Transportation Research Part B: Methodological, 86, 51-65.
23. Seifbarghy, M., Mortezavi, S. (2018). Two-objective modeling of location allocation problem in a green supply chain considering the transportation and CO2 emissions. Industrial Management Perspective, 185-163 (In Persian).
24. Sirikijpanichkul, A., van Dam, K. H., Ferreira, L., & Lukszo, Z. (2007). Optimizing the location of intermodal freight hubs: an overview of agent based modelling approach. Journal of Transportation Systems Engineering and Information Technology, 7(4), 71-81.
25. Sörensen, K., Vanovermeire, C., & Busschaert, S. (2012). Efficient metaheuristics to solve the intermodal terminal location problem. Computers & Operations Research, 39(9), 2079-2090.
26. SteadieSeifi, M., Dellaert, N. P., Nuijten, W., Van Woensel, T., & Raoufi, R. (2014). Multimodal freight transportation planning: A literature review. European journal of operational research, 233(1), 1-15.
27. Wang, R., Yang, K., Yang, L., & Gao, Z. (2018). Modeling and optimization of a road rail intermodal transport system under uncertain information. Engineering Applications of Artificial Intelligence, 72, 423-436.

فایل ها

هم رسانی

ارجاع به این مقاله

آمار