Ayşe Tuğba DOSDOĞRU, Aslı BORU İPEK, Mustafa GÖÇKEN, Tolunay GÖÇKEN

ERTELENMİŞ SİPARİŞ DURUMUNU ELE ALAN PERİYODİK STOK KONTROL SİSTEMİ İÇİN SİMÜLASYON OPTİMİZASYONU YAKLAŞIMI

Günümüzün rekabetçi iş dünyasında, şirketler yüksek kaliteli ürünler sunarken maliyetleri en aza indirgemelidir. Şirketler genellikle maliyeti en aza indirmek için stok seviyesini azaltmaya çalışmaktadır ve bu nedenle genellikle uygulamada eksiklikler gözlemlenmektedir. Bu noktada, doğru stok kontrol politikasının kullanılması, stok eksikliğinin azaltılmasında en etkili ve verimli yoludur. Stok kontrol politikalarında temel soru, tedarik zinciri üyelerinde siparişin boyutunun ve zamanlamasının belirlenmesidir. Yıllar boyunca, bu soruları yanıtlamak için birçok gelişmiş yöntem uygulanmıştır. Tedarik zincirlerinde bulunan belirsizlikler ile başa çıkmanın zor olması nedeniyle, tedarik zincirlerinde hedeflere ulaşmak için çalışmamızda simülasyon optimizasyonu (SO) kullanılmıştır. SO, stok kontrol sistemi ile ilgili büyük bilgi birikimi gerektirse de, SO kullanımı yöneticilerin bu karmaşık sistemi anlamasında kolaylık sağlamaktadır. Bu çalışmada, tedarikçi seçimini ve stok kontrol sistemini aynı anda analiz etmek için SO kullanılmaktadır. Çalışmanın sonuçları, ertelenmiş sipariş durumunu ele alan iki aşamalı tedarik zinciri modelinde, stok kontrol değişkenlerinin optimal değerinin ve en uygun tedarikçilerin SO tarafından belirlenebileceğini açıkça ortaya koymaktadır.

SIMULATION OPTIMIZATION APPROACH TO PERIODIC REVIEW INVENTORY CONTROL SYSTEM WITH BACKORDERS

In today's competitive world, companies should minimize cost while providing high quality goods.Companies generally try to reduce the level of inventory to minimize the cost and therefore they usuallyobserve shortage in practice. At this point, using of the right inventory control policy is the most effective andefficient way to reduce shortage. In inventory control policies, the basic question is to specify the size and thetiming of a replenishment order in supply chain members. Over the years, many advanced methods have beenapplied to answer these questions. Due to the difficulty of dealing with the uncertainties in supply chainenvironment, simulation optimization (SO) is used in this study to get the application of goals in supply chain.Although SO requires a great deal of understanding related with inventory control system, the use of SObrings such complex system within the grasp of managers. In this paper, SO is used to analyze the supplierselection and inventory control system simultaneously. The system results clearly reveals that the best valuesof inventory control variables and the most suitable suppliers can be determined by SO in a two echelonsupply chain model with backorder.

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Abuizam, R. (2011). Optimization of (s, S) periodic review inventory model with uncertain demand and lead time using simulation. International Journal of Management and Information System, 15(1), 67–80.
Bensoussan, A., Çakanyıldırım, M., Minjarez-Sosa, J. A., Sethi, S. P., & Shi, R. (2010). An incomplete information inventory model with presence of inventories or backorders as only observations. Journal of Optimization Theory and Applications, 146(3), 544–580.
Cárdenas-Barrón, L. E. (2011). The derivation of EOQ/EPQ inventory models with two backorders costs using analytic geometry and algebra. Applied Mathematical Modelling, 35(5), 2394–2407.
Chen, J., Huang, S., Hassin, R., & Zhang, N. (2015). Two backorder compensation mechanisms in inventory systems with impatient customers. Production and Operations Management, 24(10), 1640–1656.
Chung, K-J., & Cárdenas-Barrón, L. E. (2012). The complete solution procedure for the EOQ and EPQ inventory models with linear and fixed backorder costs. Mathematical and Computer Modelling, 55(11-12), 2151–2156.
Göçken, M., Dosdoğru, A. T., Boru, A., & Geyik, F. (2017). Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization.Simulation, 93(5), 379–396.
Gurgur, C. Z., & Altiok, T. (2008). Decentralized multi-product multi-stage systems with backorders. IIE Transactions, 40(3), 238–251.
Haq, A. N., & Kannan, G. (2006). Design of an integrated supplier selection and multiechelon distribution inventory model in a built-to-order supply chainenvironment. International Journal of Production Research, 44(10), 1963– 1985.
Jaggi, C. K., & Arneja, N. (2011). Application of Chebyshev inequality in stochastic inventory model with controllable lead time and backorder discount. International Journal of Inventory Control & Management, 1(1), 13–28.
Jalali, H., & Nieuwenhuyse, I. V. (2015). Simulation optimization in inventory replenishment: a classification. IIE Transactions, 47(11), 1217–1235.
Jawahar, N., Gunasekaran, A., & Balaji, N. (2012). A simulated annealing algorithm to the multi-period fixed charge distribution problem associated with backorder and inventory. International Journal of Production Research, 50(9), 2533–2554.
Johansson, L., & Olsson, F. (2017). Quantifying sustainable control of inventory systems with non-linear backorder costs. Annals of Operations Research, 259 (1- 2), 217–239.
Johansson, L., & Olsson, F. (2018). Age-based inventory control in a multi-echelon system with emergency replenishments. European Journal of Operational Research, 265(3), 951–961.
Kok, A. G. (1993). Backorder lead time behaviour in (s, Q)-inventory models with compound renewal demand. (Memorandum COSOR; Vol. 9333). Eindhoven: Technische Universiteit Eindhoven.
Mart, T., Duran, S., & Bakal, İ. S. (2013). Tactical inventory and backorder decisions for systems with predictable production yield. International Journal of Production Economics, 143(2), 294–303.
Mercado, E. C. (2008). Hands-on inventory management. Taylor & Francis Group, LLC. Pegden, C. D. (2007). SIMIO: a new simulation system based on intelligent objects. In: Winter Simulation Conference, Washington, DC, 2293–2300.
Rabbani, M., Oliaei, M. T. B., Farrokhi-Asl, H., & Mobini, M. (2017). A new mathematical model in cell formation problem with consideration of inventory and backorder: Genetic and particle swarm optimization algorithms. Iranian Journal of Management Studies, 10, 819–852.
Rad, M. A., Khoshalhan, F., & Glock, C. H. (2014). Optimizing inventory and sales decisions in a two-stage supply chain with imperfect production and backorders. Computers & Industrial Engineering, 74, 219–227.
Rogers, D. F., & Tsubakitani, S. (1991). Newsboy-style results for multi-echelon inventory problems: backorders optimization with intermediate delays. Journal of the Operational Research Society, 42(1), 57–68.
Santis, R. B., de Aguiar, E. P., & Goliatt, L. (2017). Predicting material backorders in inventory management using machine learning. In: IEEE Latin American Conference on Computational Intelligence (LA-CCI), Arequipa, 1–6. Samouei, P., Kheirkhah, A. S., & Fattahi, P. (2015). A network approach modeling of multi-echelon spare-part inventory system with backorders and quantity discount. Annals of Operations Research, 226, 551–563.
Sarker, B. R., Rochanaluk, R., & Egbelu, P. J. (2014). Improving service rate for a treetype three-echelon supply chain system with backorders at retailer's level. Journal of the Operational Research Society, 65, 57–72.
Srivastav, A., & Agrawal, S. (2016). Multi-objective optimization of hybrid backorder inventory model. Expert Systems with Applications, 51, 76–84.
Tripathi, M., Kuriger, G., & Wan, H. (2009). An ant based simulation optimization for vehicle routing problem with stochastic demands. In: Proceedings of the 2009 Winter Simulation Conference (WSC), Austin, TX, USA, 2476–2487.
Yao, Y., Dong, Y., & Dresner, M. (2010). Managing supply chain backorders under vendor managed inventory: An incentive approach and empirical analysis. European Journal of Operational Research, 203(2), 350–359.
Zhang, G. P., Patuwo, B. E., & Chu, C-W. (2003). A hybrid inventory system with a time limit on backorders. IIE Transactions, 35(7), 679–687.