ÇOK AMAÇLI ÜRETİM SÜREÇLERİNİN BULANIK KARAR ORTAMINDA İNCELENMESİ: BULANIK HEDEF PROGRAMLAMA VE BİR UYGULAMA

Hedef Programlamada karar verici tarafından belirlenen hedeflerin arzu edilen seviyeleri eğer gerçekçi değil ise, çözümün sonucunda hedeflerden sapmalar çok yüksek değerde gerçekleşebilir. Bu durum karar vericinin yanlış karar almasına yol açar. Benzer durum Bulanık Hedef programlama için de geçerlidir. Çünkü bulanık hedefler ve bu hedeflerin tolerans değerleri doğru belirlenmezse hedeflerden sapmalar artacaktır. Ayrıca ister Hedef Programlama problemi ister Bulanık Hedef programlama probleminde eğer hedeflerin yanı sıra kısıt fonksiyonları da olursa, kısıtlara bağlı çözüm gerçekleşeceği için doğru tanımlanmamış hedef değerlerden sapmalar çok fazla olacaktır. Çünkü hedefler kısıtlar tarafından sınırlandırılır. Bu çalışmada Çok Amaçlı Doğrusal Programlama modelinde kurulan el yapımı mobilya üretimi yapan bir işletme problemi Bulanık Hedef Programlama modelinde çözülebilmesi için ilk olarak amaç fonksiyonlarının pozitif ve negatif ideal çözümleri belirlenmiştir. Daha sonra her bir amaç fonksiyonu pozitif ve negatif ideal çözümler kullanılarak bulanık hedeflere dönüştürülmüştür.

EVALUATION OF PRODUCTION PROCESSES IN FUZZY DECISION ENVIRONMENTS: FUZZY GOAL PROGRAMMING AND AN APPLICATION

If the aspiration levels of the goals are set unrealistically by the decision maker in Goal Programming, the deviations from the goals could occur too high as a result of the solution. It leads the decision maker to make incorrect decisions. It is also the case for Fuzzy Goal Programming. When the fuzzy goals and their tolerance levels are not defined properly, there will be deviations from the goals. Additionally, if there are constraint functions besides the goals in the problems of either Goal Programming or Fuzzy Goal Programming, the solutions will deviate greatly from the incorrectly defined goal values as the solutions are realized based on the constraints. It is because the goals are limited by the constraints. This study firstly defines the positive and negative ideal solutions of objective functions in the problem organized in Multiobjective Linear Programming model for a business which manufactures hand crafted furniture. Afterwards, each objective is transformed into fuzzy goals using positive and negative ideal solutions.

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  • Rubin, P.A., and Narasimhan, R. (1984). Fuzzy goal programming with nested priorities. Fuzzy sets and systems 14, 115-129.
  • Özkan, M. ve Bircan, H.(2016). “Bulanık Hedef Programlama ile Ürün Hedef Optimizasyonu: Yang, Ignizio ve Kim Modeli”, İstanbul Üniversitesi İşletme Fakültesi Dergisi, Vol/Cilt: 45, No/Sayı:2, November/Kasım, pp.109-119.
  • Narasimhan, R. (1980). “Goal programming in a fuzzy environment”, Decision Sciences, 11, 325–336.
  • Romero, C. (1991). Handbook of Critical Issues in Goal Programming, Perganom Press, New York .
  • Mokhtaria, H. and Hasani, A. (2017).”Multi-objective model for cleaner production-transportationplanning in manufacturing plants via fuzzy goal programming”, Journal of Manufacturing Systems 44, pp.230–242.
  • Min, H., and Storbeck,J. (1991). “On the Origin and Persistence of Misconceptions in Goal Programming”, Journal of the Operational Research Society, Vol. 42, pp.301-312.
  • Lutero, D.S., Pangue,E.M.U, Tubay,J.M., and Lubag, S.P. (2016). “A fuzzy goal programming model for biodiesel production”, Journal of Physics: Conference Series 693 (2016) 012007, doi:10.1088/1742-6596/693/1/012007
  • Lin, C.C. (2004). “A weighted max–min model for fuzzy goal programming”, Fuzzy Sets and Systems 142, 407– 420.
  • Liang, T-F. (2007). “Applying fuzzy goal programming to production/transportation planning decisions in a supply chain”, International Journal of Systems Science Vol. 38, No. 4, pp.293–304.
  • Lee, S.E., and Moore, L.J. (1975). Introduction to Decision Science, Petrocelli- Charter, New York.
  • Jones, D., and Tamiz, M. (2010). Practical goal programming, International Series in Operations Research and Management Science, 141 (141). Springer, New York.
  • Ijiri,Y. (1965). Management Goals and Accounting for Control, Amsterdam, North-Holland Publishing Co.
  • Hannan, E. L. (1981), “On fuzzy goal programming”, Decision Sciences, 12, 522–531.
  • Gupta, M., and Bhattacharjee, D. (2012). “Two weighted fuzzy goal programming methods to solve multiobjective goal programming problem”, Journal of applied mathematics, volume 2012,1-20.
  • Flavell, R.B. (1976). “A new goal programming formulation”, Omega, vol. 4, no. 6, 731–732, 1976.
  • Erpolat, S. (2010). “Üretim Planlamasında Hedef Programlama ve Bulanık Hedef Programlama Yöntemlerinin Karşılaştırılması”, Öneri, C.9.S.34. Temmuz, pp.233-246.
  • Silva, A.F., and Marins,F.A.S.(2014). “A Fuzzy Goal Programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill, Energy Economics 45, pp.196–204.
  • Cheng, H.W. (2013). “A satisficing method for fuzzy goal programming problems with different importance and priorities”, Qual. Quant, 47,485–498.
  • Chen, L. H., and Tsai, F.C. (2001). “Fuzzy goal programming with different importance and priorities”, European Journal of Operational Research, 133, 548–556.
  • Chen, L-H., Ko, W-C., and Yeh,F-T. (2017). “Approach based on fuzzy goal programing and quality function deployment for new product planning”, European Journal of Operational Research,259 pp. 654–663
  • Charnes, A., and Cooper, W.W. (1977). “Goal programming and multiple objective optimizations”, Eur. J. Oper. Res., vol. 1, issue 1, pp. 39–54.
  • Charnes, A., and Cooper, W.W. 1961, Management Models and Industrial Applications of Linear Programming, Wiley, New York.
  • Charnes, A., and Cooper, W. W. and Ferguson, R. (1955). “Optimal estimation of executive compensation by linear programming”, Management Science, vol. 1, no. 2, pp. 138-151.
  • Bellman,R.E., and Zadeh,L.A. (1970). “Decision-Making in A Fuzzy Environment”, Management Science B.17 , pp. 141-164.