اخبار و اعلانات

 آمار

تعداد نشریات283
تعداد نشریات سازمان81
تعداد شماره‌ها2,728
تعداد مقالات31,061
تعداد مشاهده مقاله28,429,817
تعداد دریافت فایل اصل مقاله16,278,236
ریاحی درچه, فرشید, چیذری, امیر حسین, اسفنجاری کناری, رضا. (1398). الگوی مسیریابی بهینه ناوگان حمل‏ ونقل توزیع گوشت مرغ شهر تهران در حالت چندقرارگاهی با محدودیت پنجره زمانی. سامانه مدیریت نشریات علمی, 27(4), 239-272. doi: 10.30490/aead.2020.252609.0
فرشید ریاحی درچه; امیر حسین چیذری; رضا اسفنجاری کناری. "الگوی مسیریابی بهینه ناوگان حمل‏ ونقل توزیع گوشت مرغ شهر تهران در حالت چندقرارگاهی با محدودیت پنجره زمانی". سامانه مدیریت نشریات علمی, 27, 4, 1398, 239-272. doi: 10.30490/aead.2020.252609.0
ریاحی درچه, فرشید, چیذری, امیر حسین, اسفنجاری کناری, رضا. (1398). 'الگوی مسیریابی بهینه ناوگان حمل‏ ونقل توزیع گوشت مرغ شهر تهران در حالت چندقرارگاهی با محدودیت پنجره زمانی', سامانه مدیریت نشریات علمی, 27(4), pp. 239-272. doi: 10.30490/aead.2020.252609.0
ریاحی درچه, فرشید, چیذری, امیر حسین, اسفنجاری کناری, رضا. الگوی مسیریابی بهینه ناوگان حمل‏ ونقل توزیع گوشت مرغ شهر تهران در حالت چندقرارگاهی با محدودیت پنجره زمانی. سامانه مدیریت نشریات علمی, 1398; 27(4): 239-272. doi: 10.30490/aead.2020.252609.0

الگوی مسیریابی بهینه ناوگان حمل‏ ونقل توزیع گوشت مرغ شهر تهران در حالت چندقرارگاهی با محدودیت پنجره زمانی

اقتصاد کشاورزی و توسعه
مقاله 10، دوره 27، شماره 4 - شماره پیاپی 108، اسفند 1398، صفحه 239-272    اصل مقاله (551.5 K)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.30490/aead.2020.252609.0
نویسندگان
فرشید ریاحی درچه* 1؛ امیر حسین چیذری2؛ رضا اسفنجاری کناری3
1کارشناس ارشد اقتصاد کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، ایران
2استادیار گروه اقتصاد کشاورزی، دانشگاه تهران، ایران
3استادیار گروه اقتصاد کشاورزی، دانشکده علوم کشاورزی‬، دانشگاه گیلان، رشت، ایران
چکیده
حمل‏ونقل یک رشتة خدماتی است که از تولید تا مصرف تمامی کالاها و خدمات نقش و سهم به‏سزایی دارد. این مسئله برای کالاهای فسادپذیر همانند گوشت مرغ و همچنین، در شهرهای بزرگ اهمیت دوچندان پیدا می‌کند. در همین راستا، مطالعه حاضر با هدف طراحی الگوی مسیریابی ناوگان حمل‏ ونقل ناهمگن در حالت چندقرارگاهی و با در نظر گرفتن محدودیت پنجره زمانی در قالب الگوهای برنامه‌ریزی خطی عدد صحیح مختلط انجام شد. بدین منظور، با توجه به تقاضای روزانه گوشت مرغ در بازارهای روز سازمان میادین میوه و تره‌بار شهرداری تهران، الگوی بهینه حمل‌ونقل، طراحی و تحلیل شد. در همین راستا، الگوی مسیریابی برای سه روز مختلف مورد آزمون قرار گرفت. نتایج نشان داد که مدل طراحی‏شده می‌تواند هزینه‌های حمل ‏ونقل را به میزان قابل توجهی کاهش دهد، به‏گونه‏ای که برای سه روز مورد آزمون، هزینه‌ها به‏ترتیب 5/29، 9/27 و 5/32 درصد (معادل 16698، 14596 و 14116 هزار ریال) نسبت به شرایط حمل‏ونقل موجود کاهش می‌یابد. بر این اساس، پیشنهاد می‌شود که سازمان میادین میوه و تره‌بار شهرداری تهران، با به‏ کارگیری سامانه ثبت تقاضا و توزیع و همچنین، استفاده از مدل طراحی ‏شده برای توزیع گوشت مرغ، به کاهش هزینه‌های حمل‌ونقل بپردازد.
کلیدواژه‌ها
مسیریابی؛ گوشت مرغ؛ FSMTW؛ MILP؛ سازمان مدیریت میادین میوه و تره ‏بار شهرداری تهران
عنوان مقاله [English]
Optimal Model of Fleet Vehicle Routing for Poultry Meat Distribution in Tehran City with Multi-Depot Mode and Time Window Constraint
نویسندگان [English]
F. Riahi Dorcheh1؛ A.H. Chizari2؛ R. Esfanjari Konari3
1MSc. In Agricultural Economics, Campus of Agriculture and Natural Resources, University of Tehran, Iran
2Assistant Professor, Department of Agricultural Economics, University of Tehran, Iran.
3Assistant Professor, Department of Agricultural Economics, University of Guilan, Rasht, Iran
چکیده [English]
Transportation is a field of service with a significant role in the cycle of all goods and services from production to consumption. This is especially true for perishable goods like poultry meat, the distribution of which becomes even more important for major cities. The purpose of this study was to design a model for heterogeneous multi-depot transport fleet routing with due consideration of the limited time window (FSMTW) in the form of patterns mixed integer linear programming (MILP). For this purpose, the model was designed according to the daily demand of poultry markets of Tehran Municipality in 2013. Routing pattern was examined for three different days. The results showed that the model designed could significantly reduce transportation costs, so that it reduced costs for three studied testing days by 29.56, 27.91 and 32.58 percent (i.e. 16698, 14596 and 14116 thousand IR rials), respectively, compared to the existing transportation conditions. Therefore, it was suggested that Tehran Municipality use the system for registering and distribution of the demand as well as using the designed models for the distribution of poultry meat to reduce the logistics costs.
کلیدواژه‌ها [English]
Routing, Poultry, FSMTW, MILP, Tehran Municipality Management of Fruit and Vegetables Organization
مراجع
  1. Aggarwal, D., Kumar, V.G. and Girdhar, A.G. (2017). Development of Efficient Approaches for Solving the Vehicle Routing Problem with Time Windows (Doctoral Dissertation).
  2. Amiri, A. (2006). Designing a distribution network in a supply chain system: formulation and efficient solution procedure. European Journal of Operational Research, 171(2): 567-576.
  3. Apaiah, R.K. and Hendrix, E.M.T. (2003). Linear programming for supply chain design: a case on novel protein foods. Product Design and Quality Management Group,Department of Agro Technology and Food Science, Wageningen University, Netherlands.
  4. Bettinelli, A., Ceselli, A. and Righini, G. (2011). A branch-and-cut-and-price algorithm for the multi-depot heterogeneous vehicle routing problem with time windows. Transportation Research Part C: Emerging Technologies, 19(5): 723-740.
  5. Bettinelli, A., Ceselli, A. and Righini, G. (2014). A branch-and-price algorithm for the multi-depot heterogeneous-fleet pickup and delivery problem with soft time windows. Mathematical Programming Computation, 6(2): 171-197.
  6. Bravo, J.J. and Vidal, C.J. (2013). Freight transportation function in supply chain optimization models: a critical review of recent trends, Expert Systems with Applications, 40: 6742-6757.
  7. Bräysy, O., Dullaert, W., Hasle, G., Mester, D. and Gendreau, M. (2008). An effective multirestart deterministic annealing metaheuristic for the fleet size and mix vehicle-routing problem with time windows. Transportation Science, 42(3): 371-386.
  8. Central Bank of The Islamic Republic of Iran (2016). Time series database of Central Bank of The Islamic Republic of Iran. Available at http://tsd.cbi.ir/Display/Content.aspx. (Persian)
  9. Chizari, A.H., Riahi Dercheh, F. and Rafiei, H. (2016). Investigating the structure of transport pattern and supplying poultry meat in Tehran. Agricultural Economics, 10(2): 69-91. (Persian)
  10. Dantzig, G.B. and Ramser, J.H. (1959). The truck dispatching problem. Management Science, 6(1): 80-91.
  11. Dehbari, S., Pourosta, A.R., Naderi Bani, M., Ghobadian, A. and ;holi Moghaddam, R. (2012). Routing multi-purpose vehicles with probable service time and fuzzy demand under time window constraints. Investigating Operations and Applications, 9(4): 85-106. (Persian)
  12. Dondo, R. and Cerdá, J. (2007). A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows. European Journal of Operational Research, 176(3): 1478-1507.
  13. Forghani, M. and Jafari, A.A. (2013). A new metamorphic algorithm for routing transport vehicles along with the division of demand and access restrictions to vehicles. Business Research, 66: 21-48. (Persian)
  14. Golden, B., Assad, A., Levy, L. and Gheysens, F. (1984). The fleet size and mix vehicle routing problem. Computers and Operations Research, 11(1): 49-66.
  15. Hoff, A., Andersson, H., Christiansen, M., Hasle, G. and Lokketangen, A. (2010). Industrial aspects and literature survey: fleet composition and routing. Computers and Operations Research, 37(12): 2041-2061.
  16. Hosseinpour, H.A., Mosaddeqhkhah, V. and Tavakoli Moghaddam, R. (2007). Design of two mathematical models for probabilistic location-probability mapping. 5th International Conference on Industrial Engineering, Tehran. (Persian)
  17. Hosseinpour, H.A., Mosaddeqhkhah, V. and Tavakoli Moghaddam, R. (2009). Solving the vehicle routing problem in multidimensional and probable multipurpose mode using simulated annealing. Industrial Engineering, 43(1): 25-36. (Persian)
  18. Jawahara, N. and Balaji, N. (2012). A genetic algorithm based heuristic to the multiperiod fixed charge distribution problem. Applied Soft Computing, 12: 682-699.
  19. Jha, J.K. and Shanker, K. (2013). Single-vendor multi-buyer integrated production inventory model with control lable lead time and service level constraints. Applied Mathematical Modelling, 37(4): 1753-1767.
  20. Karabuk, S. (2007). Modeling and optimizing transportation decisions in a manufacturing supply chain. Transportation Research Part E: Logistics and Transportation Review, 43(4): 321-337.
  21. Koç, Ç., Bektaş, T., Jabali, O. and Laporte, G. (2015). A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems. Computers and Operations Research, 64: 11-27.
  22. Koç, Ç., Bektaş, T., Jabali, O. and Laporte, G. (2016). Thirty years of heterogeneous vehicle routing. European Journal of Operational Research, 249(1): 1-21.
  23. Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4): 408-416.
  24. Li, F., Golden, B. and Wasil, E. (2007). A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem. Computers and Operations Research, 34(9): 2734-2742.
  25. Liang, T. (2008). Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Computers and Industrial Engineering, 55(3): 676-694.
  26. Liu, F.H. and Shen, S.Y. (1999). The fleet size and mix vehicle routing problem with time windows. Journal of the Operational Research Society, 12(5): 721-732.
  27. Melo, M.T., Nickel, S. and Saldanha-da-Gama, F. (2012). A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon. International Journal of Production Economics, 136: 218-230.
  28. Mirzapour Al-e-Hashem, S.M.J., Malekly, H. and Aryanezhad, M.B. (2011). A multi objective robust optimization model for multiproduct multisite aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 134: 28-42.
  29. Mohammadi Zanjirani, D. and Asaadi Aghajery, M. (2009). Designing a math model for inventory routing in supply chain: a case study in Donar Khazer Co. Industrial Management, 1(3): 119-136. (Persian)
  30. Olivares-Benitez, E., Rios-Mercado, R.Z. and Gonzalez-Velarde, J.L. (2013). A metaheuristic algorithm to solve the selection of transportation channels in supply chain design. Production Economics, 145: 161-172.
  31. Paksoy, T., Bektas, T. and Ozceylan, E. (2011). Operational and environmental performance measures in a multi-product closed-loop supply chain. Transportation Research Part E, 47: 532-546.
  32. Paraskevopoulos, D.C., Repoussis, P.P., Tarantilis, C.D., Ioannou, G. and Prastacos, G.P. (2008). A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows. Heuristics, 14(5): 425-455.
  33. Peidro, D., Mula, J., Jiménez, M. and Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205: 65-80.
  34. Pisarski, A. (2008). The transportation challenge: moving the US economy. (Research Report), Cambridge Systematics, Inc. for National Chamber Foundation.
  35. Pratama, R.Y. and Mahmoudi, W.F. (2017). Optimization of vehicle routing problem with time window (VRPTW) for food product distribution using genetics algorithm. Information Technology and Computer Science, 2(2): 102-132.
  36. Prins, C. (2004). A simple and effective evolutionary algorithm for the vehicle routing problem. Computers and Operations Research, 31(12): 1985-2002.
  37. Saleh, I., Peykani, Gh.R. and Moghiseh, S. (2010). Dynamic optimization of transportation of soybean oil in Iran. Economics and Development, 18(70): 1-16. (Persian)
  38. Seixas, M.P. and Mendes, A.B. (2013). Column generation for a multi trip vehicle routing problem with time windows, driver work hours, and heterogeneous fleet. Mathematical Problems in Engineering, 14(27): 125-136.
  39. Shavandi, H. and Bozorgi, B. (2012). Developing a location-inventory model under fuzzy environment. International Journal of Advanced Manufacturing Technology, 63(1-4): 191-200. (Persian)
  40. Solomon, M.M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2): 254-265.
  41. Statistical Center of Iran (2011). Population and housing census. Tehran: Statistical Center of Iran. (Persian)
  42. Taillard, É.D. (1999). A heuristic column generation method for the heterogeneous fleet VRP. Revue Française d'Automatique, d'Informatique et de Recherche Opérationnelle, 33(1): 1-14.
  43. Tavakoli Moghaddam, R. and Mazloumi, S.Z. (2009). Mathematical modeling problem location - multi-objective routing in the three-level supply chain. The 2nd International Conference on Operation, Iran, Babolsar. (Persian)
  44. Tehran Municipality Management of Fruit and Vegetables Organization (2013). Central Market of Fruit and Vegetables, Tehran Municipality, System No. 2, Agricultural Products, Office of Monitoring of Information on Protein Products. Available at http://mayadin.tehran.ir/Default.aspx?tabid=434. (Persian)
  45. Teymouri, A. and Hafez Alkotob, A. (2008). Designing a Multi-product supply network using nonlinear dual-programming planning and using the AHP method, case study: automobile logistic network. Business Journal of Research, 47: 169-204. (Persian)
  46. Toth, P. and Vigo, D. (2002). The vehicle routing problem. SIAM Series, Philadelphia.
  47. Toth, P. and Vigo, D. (Eds) (2014). Vehicle routing: problems, methods, and applications (Vol. 18). SIAM Series on Optimization, Philadelphia.
آمار
تعداد مشاهده مقاله: 864
تعداد دریافت فایل اصل مقاله: 371


counter
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب