ارائه ی یک مدل موجودی برای خرابی غیر آنی کالا در یک زنجیره تامین دوسطحی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشکده مهندسی صنایع، دانشگاه علوم و فنون مازندران، مازندران

2 کارشناسی ارشد، دانشکده مهندسی صنایع، دانشگاه علوم و فنون مازندران، مازندران

3 کارشناسی ارشد، دانشکده مهندسی صنایع، دانشگاه پردیسان فریدون‌کنار، مازندران

چکیده

در این تحقیق یک مدل ریاضی برای زنجیره‌تامین دوسطحی متشکل از یک خریدار و یک تولیدکننده برای یک سیستم یکپارچه موجودی با خرابی غیر آنی اقلام ارائه می­شود که تقاضا احتمالی است و از توزیع نرمال پیروی می­کند. فرض می­شود خرابی از توزیع وایبول سه پارامتر پیروی می­کند. در شرایط واقعی در نظر گرفتن کمبود هم به‌صورت پس­افت و هم به‌صورت فروش از­ دست­رفته الزامی است، بنابراین هر دو نوع کمبود نیز در مدل بکار گرفته می­شود. هدف از این مدل تعیین سیاست بهینه سفارش دهی است بطوری­که مجموع هزینه­های زنجیره­تامین کمینه گردد. برای حل مدل از الگوریتم­ ژنتیک بهره برده شده است. همچنین جهت تحلیل مدل و بررسی تأثیر برخی از پارامترهای مهم و تأثیرگذار بر جواب بهینه مدل موجودی از تحلیل حساسیت نرخ خرابی و سطح اطمینان استفاده می‌نماییم. در نهایت مقدار بهینه هزینه مورد انتظار زنجیره­تامین تحت تصمیم­گیری یکپارچه و غیر یکپارچه تعیین و مقایسه می­گردد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

An inventory model for non-instantaneous deterioration items in a two‌-echelon Supply chain

نویسندگان [English]

  • Javad Rezaeian 1
  • Moghaddaseh Akbarpoor 2
  • Hadiseh Akbarpoor 3
چکیده [English]

Most of the inventory control models assume that items can be stored indefinitely to meet the future demands. However, certain types of commodities either deteriorate or become obsolete in the course of time and hence are unstable. In this study, a mathematical model is presented for a two-echelon supply chain including a buyer and a producer for an inventory integrated system with non-instantaneous of items that demand is probable and follows a normal distribution. Since, the rate of deterioration describes the condition deterioration the goods and regarding the relation between time and deterioration rate is probable rather than the fixed rate of deterioration. In reality, considering the shortages is necessary in both forms of backlogging and lost sales. Therefore, both kinds of shortages are used in the model.
   The main goal of this model is determining the optimal ordering policy so that the total cost of supply chain is minimized. The proposed model is solved for some problems by Lingo software. The validity of model is determined by sensitive analysis and the problem is known a NP-hard one, hence a genetic algorithm has been used in order to solve the model problem. The rates of deterioration and confidence level sensitivity analysis have been applied to analyze effect of some important parameters affecting on optimal solution of the inventory model.
   Finally, the optimal value of the expected cost of supply chain under integrated and non-integrated decision-making has been determined and compared. The results show the efficiency of algorithm

کلیدواژه‌ها [English]

  • two-echelon supply chain
  • non-instantaneous deterioration
  • inventory model

مراجع

[1]     Guillena, G., Mele, F., Bagajewicz, M., Espuna, A., Puigjaner, L., (2005). “Multiobjective supply chain design under uncertainty”, Chemical Engineering Science, 60: 1535-1553.
[2]      Giannoccaro, I., Pontrandolfo, P., Scozzi, B., (2003). “A fuzzy echelon approach for inventory management in supply chains”, European Journal of Operational Research, 149: 185-196.
[3]      Goyal, S. K., Gupta, Y. P., (1989). “Integrated inventory models: the buyer-vendor coordination”, European Journal of Operational Research, 41: 261-269.
[4]     Huang, G.Q., Lau, J.S.K., Mak, K.L., (2003). “The impacts of sharing production information on supply chain dynamics: a review of the literature,” International Journal of Production Research, 41: 1483-1517.
[5]      Whitin, T.M., (1953). “The Theory of Inventory Management”, Princeton University Press, Princeton, NJ, USA, 62-72.
[6]     Ghare, P.M., Schrader, S.F., (1963). “A model for exponentially decaying inventory”, Journal of Industrial Engineering, 14: 238-243.
[7]      Covert, R.P., Philip, G.C., (1973). “An EOQ model for items with weibull distribution deterioration”, AIIE Transactions, 5: 323-326.
[8]     Goyal, S.K., Giri, B.C., (2001). “Recent trends in modeling of deteriorating inventory”, European Journal of Operational Research, 134: 1-16.
[9]      Mahata, G. C., (2012). “An EPQ-based inventory model for exponentially deteriorating items under retailer partial trade credit policy in supply chain”, Expert Systems with Applications, 39: 3537-3550.
[10] Wu, K.SH., Oyang, L.Y., Yang, CH.T., (2006). “An optimal replenishment policy for non-instantaneous deteriorating items with stock-dependent demand and partial backlogging”, Production Economics, 101: 369-384.
[11]  Geetha, K.V., Uthayakumar, R., (2010). “Economic design of an inventory policy for non-instantaneous deteriorating items under permissible delay in payments”, Journal of Computational and Applied Mathematics, 233: 2492-2505.
[12]  Shah, N.H., Soni, H.N., Patel, K.A., (2013). “Optimizing inventory and marketing policy for non-instantaneous deteriorating items with generalized type deterioration and holding cost rates”, Omega, 41: 421–430.
[13] Maihami, R., Karimi, B., (2014). “Optimizing the pricing and replenishment policy for non-instantaneous deteriorating items with stochastic demand and promotional efforts”, Computers & Operations Research, 51: 302-312.
[14]  Abad, P.L., (1996). “Optimal pricing and lot-sizing under conditions of perishability and partial backordering”, Management Science, 42: 1093-1197.
[15]  Abad, P.L., (2001). “Optimal price and order size for a reseller under partial backordering”, Computers & Operations Research, 28: 53-65.
[16]  Pentico, D.W., Drake, M.J., (2011). “A survey of deterministic models for the EOQ and EPQ with partial backordering”, European Journal of Operational Research, 214: 179-198.
[17] Clark, A.J., Scarf, H., (1960). “Optimal policies for a multi-echelon inventory problem”, Management Science, 6: 475-490.
[18] Taleizadeh, A.A., Niaki, S.T.A., Makui, A., (2012). “Multiproduct multiple-buyer single-vendor supply chain problem with stochastic demand, variable lead-time, and multi-chance constraint”, Expert Systems with Applications, 39: 5338–5348.
[19] Yanga, P.CH., Weeb, H.M., (2003). “An integrated multi-lot-size production inventory model for deteriorating item”, Computers & Operations Research, 30: 671-682.
[20] Bakker, M., Riezebos, J., Teunter, R.H., (2012). “Review of inventory systems with deterioration since 2001”, European Journal of Operational Research, 221: 275-284.
[21] Rau, H., Wu, M.Y., Wee, H.M., (2003). “Integrated inventory model for deteriorating items under a multi-echelon supply chain environment”, International Journal of Production Economics, 86: 155-168.
[22] Tiwari, S., Cardenas-Barron, L., Khanna, A., .Jaggi, C., (2016). “Impact of trade credit and inflation on retailer's ordering policies for non-instantaneous deteriorating items in a two-warehouse environment”, International Journal of Production Economics 176: 154-169.
[23] Rabbani, M., Pourmohammad Zia, N., Rafiei, H., (2016). “Joint optimal dynamic pricing and replenishment policies for items with simultaneous quality and physical quantity deterioration”, Applied Mathematics and Computation, 149-160.
[24] Guchhait, P., Maiti, M.K., Maiti,M., (2015). “An EOQ model of deteriorating item in imprecise environment with dynamic deterioration and credit linked demand”, Applied Mathematical Modelling, 1-15.
[25] Jaggi, C., Tiwari, S., Shafi, A., (2015). “Effect of deterioration on two-warehouse inventory model with imperfect quality”, Computers & Industrial Engineering 88: 378-385.
[26] Das, B.C., Dasb, B., Mondal, S., (2015). “An integrated production inventory model under interactive fuzzy credit period for deteriorating item with several markets”, Applied Soft Computing 28: 453-465.
[27] Philip, G.C., (1974). “A generalized EOQ model for items with Weibull distribution deterioration”, AIIE Transactions, 6: 159-162.
[28] Spiegel, M.R., “Applied differential equations, Englewood Cliffs”, N.J.: Prentice-Hall, 1960.
[29] Wang, K.J., Lin, Y.S., Jonas, C.P., (2011). “Optimizing inventory policy for products with time-sensitive deteriorating rates in a multi-echelon supply chain”, Production Economics, 130: 66-67.
[30] Wu, K.SH., Oyang, L.Y., Yang, CH.T., (2006). “An optimal replenishment policy for non-instantaneous deteriorating items with stock-dependent demand and partial backlogging”, Production Economics, 101: 369-384.
[31]  Annadurai, K., Uthayakumar, R., (2013). “Two-echelon inventory model for deteriorating items with credit period dependent demand including shortages under trade credit”, Optim. Lett, 7: 1227-1249.
[32] Valliathal, M., Uthayakumar, R., (2011). “Optimal pricing and replenishment policies of an EOQ model for non-instantaneous deteriorating items with shortages” The International Journal of Advanced Manufacturing Technology, 54: 361-371.
[33] Yanga, P.CH., Weeb, H.M., (2003). “An integrated multi-lot-size production inventory model for deteriorating item”, Computers & Operations Research, 30: 671-682.
[34]  Lo, SH.T., Wee, H.M., Huang, W.CH., (2007). “An integrated production-inventory model with imperfect production processes and Weibull distribution deterioration under inflation”, International Journal Production Economics, 106: 248-260.
[35] Nurani, A. (2001). “Designing cellular production system in conditions of probabilistic dynamic demand and solving it by simulation annealing and comparing with the optimum value”, Science and technology university of Mazandaran.
[36] Goldberg, D.E., (1989). “Genetic algorithms for search, Optimization and Machine Learning”, Reading, MA: Addingwesley.
[37] Rudelph, G., (1994), “Convergence analysis of canonical genetic algorithms”, IEEE Transactions on Neural Network, 5: 96-101.
[38] Vose, M.D., (1999). “Simple genetic algorithm: Foundation and Theory”, Ann Arbor, MI: MIT press.
[39] Augusto, O.B., Rabeau, S., Depince, Ph., Bennis, F., (2006). “Multi-objective genetic algorithms: A way to improve the convergence rate”, Engineering Applications of Artificial Intelligence, 19: 501-510.
[40] Yu, J.C.P., Wee, H.M., Wang, K.J, (2008). “An integrated three-echelon supply chain model for a deteriorating items via simulated annealing method”, Proceeding of the Seventh International Conference on Machine Learning and Cybemetics.
نشریه پژوهش های مهندسی صنایع در سیستم های تولید
دوره 4، شماره 8
اسفند 1395
صفحه 165-179

فایل ها

هم رسانی

ارجاع به این مقاله

آمار