BÜTÜNLEŞİK ÜRETİM PLANLAMASINDA ETKİLEŞİMLİ OLABİLİRLİKÇİ DOĞRUSAL PROGRAMLAMA MODELİ VE BİR UYGULAMA

Abstract

Bütünleşik Üretim Planlaması (BÜP), orta dönemli planlama kararlarının alınmasında işgücü ve stok düzeylerinin, normal ve fazla mesai üretim miktarlarının, ertelenen sipariş miktarlarının ve taşeron gereksiniminin bir bütün olarak değerlendirilmesini ve dengelenmesini amaçlamaktadır. Ancak değişen çevre koşulları altında piyasa talepleri, mevcut kaynaklar, kapasiteler ve ilgili üretim maliyetleri çoğunlukla belirsizdir. Dolayısıyla bu çalışmada, gerçek hayatta karşılaşılan durumları yansıtabilen, belirsizlikleri göz ardı etmeyen, karar verici ile çözüm süreci boyunca etkileşerek onun da karar sürecine katılımını sağlayan çok amaçlı, çok ürünlü ve çok dönemli bulanık bir BÜP problemi dikkate alınmıştır. Problemin çözümü için bir Etkileşimli Olabilirlikçi Doğrusal Programlama (EODP) modeli önerilmiştir. Son olarak önerilen modelin gerçek hayatta uygulanabilirliği gösterilmiştir

Keywords

• Bütünleşik Üretim Planlaması (BÜP), Bulanık Mantık, Bulanık BÜP, Etkileşimli Olabilirlikçi Doğrusal Programlama (EODP)

INTERACTIVE POSSIBILISTIC LINEAR PROGRAMMING MODEL AT AGGREGATE PRODUCTION PLANNING AND AN APPLICATION

Abstract

Aggregate Production Planning (APP) aims at evaluating and balancing the work force and inventory levels, regular and overtime production quantities, backordering levels and subcontract requirement as a whole in the process of taking midterm planning decisions. However market demands, available resources, capacities and related production costs are often uncertain under the changing environmental conditions. Therefore, in this study multi-objective, multi-product and multi-period fuzzy APP problem that is able to reflect real-world features and which does not ignore its uncertainties and ensures decision makers’ participation in decision making process by interacting with them during the solution process, has been considered. Interactive Possibilistic Linear Programming (i-PLP) model has been proposed for solving the problem. Finally the feasibility of applying the proposed model in real world has been demonstrated

Keywords

• Aggregate Production Planning (APP), Fuzzy Logic, Fuzzy APP, Interactive Possibilistic Linear Programming (i-PLP)

References

1. Baykoç, Ö.F. & Sakallı, Ü.S. (2009). An Aggregate Production Planning Model for Brass Casting Industry in Fuzzy Environment. International Journal of Mathematical and Statistical Sciences, 1(3): 154-158.
2. Bellman, R. & Zadeh, L. (1970). Decision Making in a Fuzzy Environment. Management Science, 17(4): 141-164.
3. Bitran, G.R. & Yanassee, H.H. (1984). Deterministic Approximations to Stochastic Production Problem. Operations Research, 32(5): 999- 1018.
4. Buckley, J.J. (1988). Possibilistic Linear Programming with Triangular Fuzzy Numbers. Fuzzy Sets and Systems, 26, 135-138.
5. Chen, S.J. & Hwang, C.L. (1992). Fuzzy Multiple Attribute Decision Making: Methods and Applications. New York: Springer.
6. Chen, L.H. & Tsai, F.C. (2001). Fuzzy Goal Programming with Different Importance and Priorities. European Journal of Operational Research, 133(3): 548-556.
7. Çubukçu, R. (2008). Proje Yönetiminde Zaman ve Maliyet Risklerinin Çizelgeleme Yöntemiyle Minimize Edilmesi. Yayınlanmamış Doktora Tezi, Adana: Çukurova Üniversitesi Fen Bilimleri Enstitüsü.
8. Guiffrida, A.L. & Nagi, R. (1998). Fuzzy Set Theory Applications in Production Management Research: A Literature Survey, Journal of Intelligent Manufacturing. 9(1): 39-56.

9. Hausman, W.H. & McClain, J.D. (1971). A Note on the Bergstrom-Smith Multi-Item Production Planning Model. Management Science, 17(11): 783-785.
10. Holt, C.C., Modigliani, F. & Simon, H.A. (1955). A Linear Decision Rule for Production and Employment Scheuling. Management Science, 2(1): 1-30.
11. Hsu, H.M. & Wang, W.P. (2001). Possibilistic Programming in Production Planning of Assemble-to-Order Environments. Fuzzy Sets and Systems, 119(1): 59-70.
12. Kanyalkar, A.P. & Adil, G.K. (2005). An Integrated Aggregate and Detailed Planning in a Multi-Site Production Environment Using Linear Programming. International Journal of Production Research, 43(20): 4431-4454.
13. Lai, Y.J. & Hwang, C.L. (1992). A New Approach to Some Possibilistic Linear Programming Problems. Fuzzy Sets and Systems, 49(2): 121-133.
14. Liang, T.F. (2007a). Application of Interactive Possibilistic Linear Programming to Aggregate Production Planning with Multiple Imprecise Objectives. Production Planning and Control, 18(7): 548-560.
15. Liang, T.F. (2007b). Application of Possibilistic Linear Programming to Multi-Objective Distribution Planning Decisions. Journal of the Chinese Institute of Industrial Engineers, 24(2): 97-109.
16. Liou, T.S. & Wang, M.J.J. (1992). Ranking Fuzzy Numbers with Integral Value. Fuzzy Sets and Systems, 50(3): 247-255.
17. Marler, R.T., Yang, J. & Rao, S.S. (2004). A Fuzzy Approach for Determining a Feasible Point in a Constrained Problem. ASME/JSME Pressure Vessels and Piping Conference, July 25-29, San Diego, CA, 115-124.
18. Tang, J., Wang, D. & Fung, R.Y.K. (2001). Formulation of General Possibilistic Linear Programming Problems for Complex Industrial Systems. Fuzzy Sets and Systems , 119(1): 41-48.
19. Tiwari, R.N., Dharmar, S. & Rao, J.R. (1986). Priority Structure in Fuzzy Goal Programming. Fuzzy Sets and Systems, 19(3): 251-259.
20. Wang, R.C. & Liang, T.F. (2004). Application of Fuzzy Multi-Objective Linear Programming to Aggregate Production Planning. Computers and Industrial Engineering, 46(1): 17-41.
21. Wang, R.C. & Liang, T.F. (2005). Applying Possibilistic Linear Programming to Aggregate Production Planning. International Journal of Production Economics, 98(3): 328-341.
22. Zadeh, L.A. (1965). Fuzzy Sets. Information and Control, 8 (3), 338-353.
23. Zadeh, L.A. (1978). Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems, 1(1):3-28.
24. Zimmermann, H.J. (1976). Description and Optimization of Fuzzy Systems. International Journal of General System, 2(4): 209–215.
25. Zimmermann, H.J. (1978). Fuzzy Programming and Linear Programming with Several Objective Functions. Fuzzy Sets and Systems, 1(1):. 45–55.