Kısa ömürlü ürünler için koordineli bir stok ve fiyat yönetimi modeli

Abstract

Bu çalışmada süt, yoğurt, yumurta, ekmek, taze meyve-sebze gibi kısa ömürlü dayanıksız ürünler için, zamana bağlı bir talep fonksiyonu gözetilerek, koordineli stok yönetimi ve fiyatlandırma kararları konu alınmaktadır. Dayanıklı ürünlerden farklı olarak, bu ürünler eskidikçe müşteriler tarafından daha az tercih edilmeye başlamakta ve kısa bir zaman içerisinde tamamen kullanılamaz hale gelebilmektedir. Dolayısıyla, herhangi bir anda elde bulunan ürünlerin sadece miktarları değil, durumları veya yaşları da stok ve fiyatlandırma kararlarına etki etmekte ve problemi zorlaştırmaktadır. Yeni ve taze ürünlere olan talep fazla iken, ürünler eskidikçe müşteriler tarafından daha az tercih edilmekte ve bazı müşteriler başka ürünlere yönelebilmektedir. Bu çalışmada kısa ömürlü ürünlerin stok ve fiyat yönetimi için bir model oluşturulmuş ve bu modelin analizi ile en iyi çözümü araştırılmıştır. Oluşturulan modelin en iyi çözümünün bulunmasının mümkün olmadığı durumlar için bir yaklaşım algoritması da geliştirilmiştir. Ayrıca, sayısal çalışmalar ile farklı durumlarda uygulanması gereken yöntemler ortaya çıkarılmış ve öneriler geliştirilmiştir.

Keywords

• Stok yönetimi, Gelir yönetimi, Fiyatlandırma, Zamana bağlı talep

References

1. Minner, S, Transchel, S. Periodic review inventory-control for perishable products under service- level constraints. OR Spectrum, 2010; 32(4): 979-996.
2. Broekmeulen RACM, van Donselaar KH. A heuristic to manage perishable inventory with batch ordering, positive lead-times, and time-varying demand. Computers and Operations Research, 2009; 36: 3013–3018.
3. Nahmias, S. Perishable inventory theory: a review. Operations Research, 1982; 30: 680–708.
4. Rafaat, F. Survey of literature on continuously deteriorating inventory models. Journal of Operations Research Society, 1991; 42(1): 27–37.
5. Goyal SK, Giri BC. Recent trends in modeling of deteriorating inventory. European Journal of Operational Research, 2001; 134(1): 1–16.
6. Karaesmen I, Scheller-Wolf A, Deniz B. Managing perishable and aging inventories: review and
7. future research directions. In: Kempf K, Keskinocak P, Uzsoy R, editors. Planning Production and
8. Inventories in the Extended Enterprise, Volume 151 of the series International Series in Operations

9. Research & Management Science, Springer, 2008: 393-436.
10. van Zyl, G. Inventory control for perishable commodities. Ph.D. thesis, University of North Carolina, Chapel Hill, NC, 1964.
11. Nahmias, S. Optimal ordering policies for perishable inventory – II. Operations Research, 1975; 23(4): 735-749.
12. Fries B. Optimal ordering policy for a perishable commodity with fixed lifetime. Operations Research, 1975; 23(1): 46–61.
13. Ferguson N, Ketzenberg ME. Informations Sharing to Improve Retail Product Freshness of Perishable Products. Production and Operations Management, 2006; 15(1): 57-73.
14. van Donselaar KH, de Kok AG., Rutten WGMM. Two replenishment strategies for the lost sales inventory model: a comparison. International Journal of Production Economics, 1996; 47(1): 285–295. [12] Parlar M, Perry D, Stadje W. FIFO Versus LIFO Issuing Policies for Stochastic Perishable Inventory Systems. Methodology & Computing in Applied Probability, 2010; 12: 1-13.
15. Berk E, Gürler Ü. Analysis of the (Q, r) inventory model for perishables with positive lead times and non-negligible ordering costs. Operations Research, 2008; 56: 1238–1246.
16. Schultz CR. Replenishment delays for expensive slow moving items. Management Science, 1989; 35: 1454-1462.
17. Tekin, E, Gürler, Ü, Berk, E. Age-based vs. stock level control policies for a perishable inventory system. European Journal of Operational Research, 2001; 134: 309-329.
18. Gallego, G, van Ryzin, G. Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science, 1994; 40(8): 999–1020.
19. Gallego, G, van Ryzin, G. A multiproduct dynamic pricing problem and its applications to network yield management. Operations Research, 1997; 45(1): 24–41.
20. Bitran, GR., Mondschein, SV. An application of yield management to the hotel industry considering multiple day stays. Operations Research, 1995; 43(3): 427–443.
21. Bitran GR, Mondschein SV. Periodic pricing of seasonal products in retailing. Management Science, 1997; 43: 64-79.
22. Feng Y, Gallego G. Optimal starting times for end-of-season sales and optimal stopping times for promotional fares. Management Science, 1995; 41: 1371-1391.
23. Feng, Y, Gallego G. Perishable asset revenue management with Markovian time dependent demand intensities. Management Science, 2000; 46: 941-956. [22] Zhao, W, Zheng, Y-S. Optimal dynamic pricing for perishable assets with nonhomogeneous demand. Management Science, 2000; 46(3): 375-388. [23] Feng, Y, Xiao, B. A continuous-time yield management model with multiple prices and reversible price changes. Management Science, 2000; 46(5): 644–657.
24. Hall, J, Kopalle, P, Pyke, D. Static and dynamic pricing of excess capacity in a make-to-order environment. Production and Operations Management, 2009; 18(4): 411-425.
25. Elmaghraby, W, Keskinocak, P. Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions. Management Science, 2003; 49(10): 1287-1309.
26. Bitran GR, Caldentey R. An Overview of Pricing Models for Revenue Management. Management Science and Operations Management, 2003; 5(3): 203-229.
27. Chen, X, Pang, Z, Pan, L. Coordinating inventory control and pricing strategies for perishable products. Operations Research, 2014; 62(2): 284-300. [28] Chen, LM., Sapra, A. Joint inventory and pricing decisions for perishable products with two-period lifetime. Naval Research Logistics, 2013; 60(5): 343-366.
28. Chew Peng, E, Chulung, L. Joint inventory allocation and pricing decisions for perishable products. International Journal of Production Economics, 2009; 120: 139-150.
29. Benkherouf, L. On an inventory model with deteriorating items and decreasing time-varying demand and shortages. European Journal of Operational Research, 1995; 86: 293-299.
30. Mishra, VK., Singh, SL. Deteriorating Inventory Model with Time Dependent Demand and Partial Backlogging. Applied Mathematical Sciences, 2010; 4(72): 3611-3619.
31. Rajan, A, Rakesh, R, Steinberg, R. Dynamic pricing and ordering decisions by a monopolist. Management Science, 1992; 38: 240–262.
32. Abad PL. Optimal pricing and lot-sizing under conditions of perishability and partial backordering. Management Science, 1996; 42: 1093–1104.
33. Abad, PL. Optimal policy for a reseller when the supplier offers a temporary reduction in price. Decision Sciences, 1997; 28: pp. 637.
34. Abad, PL. Optimal price and order size for a reseller under partial backordering. Computers and Operations Research, 2001; 28: 53–65.
35. Abad, PL. Optimal pricing and lot sizing under conditions of perishability and partial backordering and lost sale. European Journal of Operational Research, 2003; 144: 677-685.
36. Transchel, S, Minner, S. The impact of dynamic pricing on the economic order decision. European Journal of Operational Research, 2009; 198: 773-789. [38] Mukhopadhyaya, S, Mukherjee, RN., Chaudhuri, KS. Joint pricing and ordering policy for a deteriorating inventory. Computers and Industrial Engineering, 2004; 47: 339-349.
37. Sana, SS. Optimal selling price and lot size with time varying deterioration and partial backlogging. Applied Mathematics and Computations, 2010; 217(1): 185-194.
38. P-S You. Inventory policy for products with price and time-dependent demands. Journal of the Operational Research Society, 2005; 56: 870-873.