Araştırma Makalesi
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Fuzzy Goal Programming Problem Based on Minmax Approach for Optimal System Design

Yıl 2018, Cilt 6, Sayı 1, 2018, 177 - 192, 27.06.2018
https://doi.org/10.17093/alphanumeric.404680

Öz

Every system in nature evolved in order to carry on their existence and reach their targets with minimal losses. The fundamental condition of a system’s success lies on making the correct decision by evaluating multiple, complicated, and conflicting goals based on the present constraints. Many mathematical programming problems are make up of objective functions combined by the decision maker based on the constrains. This study investigates how an optimal design can be reached based on Minmax approach. Goal Programming and a Fuzzy Goal Programming known as MA approach are used in this study. The solution of a problem organized as a Multiple De novo programming in order to determine the resource amounts for a business in handcrafts is carried out based on these two approaches. Budget constrain is organized as a goal to solve the problem based on MA approach, and a solution is proposed accordingly. The acquired results suggest that the solution results of Minmax Goal Programming and MA approach are the same.

Kaynakça

  • Aköz, O. & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European journal of operational research 181,1427–1433.
  • Aouni B., Marter JM. & Hassaine, A. (2009). Fuzzy Goal programming model: An Overview of the Current State-of-theart. J. Multi-crit. Decis. Anal. 16,149–161.
  • Babić, Z., & Pavić, I. (1996). Multicriterial production programming by de novo programming approach. International journal of production economics, 43(1), 59-66.
  • Bellman, R.E. & Zadeh, L.A. (1970). Decision-making in a fuzzy environment. Management science B.17, 141-164.
  • Chakraborty, S. & Bhattacharya, D. (2013). Optimal system design under multi-objective decision making using de-novo concept: a new approach. International journal of computer applications, 63(12), 20-27.
  • Chanas, S. (1983). The use of parametric programming in FLP. Fuzzy sets and systems 11, 243-251.
  • Chanas,S. & Gupta, K.(2002). Fuzzy goal programming-one notion, may meanings, Control and cybernteics, 31(4), 871-890.
  • Charnes, A. & W.W. Cooper,W. W. (1961). Management Models and Industrial Applications of Linear Programming, Wiley, New York.
  • Charnes A., Cooper W. W. & Ferguson R. (1955). Optimal estimation of executive compensation by linear programming. Management science, 1, 138-151.
  • Charnes, A. & Cooper, W. W. (1977). Goal programming and multiple objective optimizations, Eur. J. Oper. Res., 1, 39–54.
  • Chen, YW. & Hsieh, H.-E. (2006). Fuzzy multi-stage De Novo programming problem. Applied Mathematics and Computation 181(2), 1139-1147.
  • Chen, L. H. & Tsai, F.C. (2001). Fuzzy goal programming with different importance and priorities. European journal of operational research, 133, 548–556.
  • Chen, J.K.C. & Tzeng, G-H. (2009). Perspective strategic alliances and resource allocation in supply chain systems through the de novo programming approach. Int. J. Sustainable strategic management, 1(3), 320-339.
  • Cheng, HW. (2013). A satisficing method for fuzzy goal programming problems with different importance and priorities. Qual. quant, 47,485–498.
  • Flavell, R.B. (1976). A new goal programming formulation. Omega, the international journal of management science 4, 731–732.
  • Gupta, M. & Bhattacharjee, D. (2012). Two weighted fuzzy goal programming methods to solve multiobjective goal programming problem. Journal of applied mathematics volume 2012,1-20.
  • Hannan, E. L. (1981a). On fuzzy goal programming. Decision sciences, 12, 522–531.
  • Hannan, E. L. (1981b). Linear programning with multiple fuzzy goals. Fuzzy sets and systems. Vol. 6. 235-248.
  • Huang, J-J., Tzeng, G-H. & Ong, C-S. (2006). Choosing best alliance partners and allocating optimal alliance resources using the fuzzy multi-objective dummy programming model. Journal of the operational research society, 57, 1216–1223.
  • Ignizio, J.P. ve Cavalier, T.M. (1994). Linear programming, Prentice-Hall, Inc.,Englewood Cliffs, New Jersey.
  • Ijiri Y. (1965). Management Goals and Accounting for control, Amsterdam, North-Holland Publishing Co,
  • Kim, J.S. & Whang, K-S. (1998). A tolerance approach to the fuzzy goal programming problems with unbalanced triangular membership function. European journal of operational research 107, 614-624.
  • Lai, Y.J. & Hwang C.L. (1992). Fuzzy Mathematical Programming Springer-Verlag, Berlin.
  • Li, R.J & Lee, E.S. (1990). Approaches to multicriteria de novo programs. Journal of mathematical analysis and applications, 153, 97-111.
  • Lin, C.C. (2004). A weighted max–min model for fuzzy goal programming. Fuzzy sets and systems 142, 407–420.
  • Lee, E.S & Li, R.J. (1993). Fuzzy multiple objective programming and compromise programming with pareto optimum. Fuzzy sets and systems, 53, 275-288.
  • Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision Sciences, 11, 325–336.
  • Narasimhan, R. (1981). On Fuzzy goal programming—some comments, Decision Sci. 12, 532–538.
  • Romero, C. (1985). Multi-objective and goal programming as a distance function model. JORS, 36(3), 249-251.
  • Rubin, P.A.& Narasimhan, R. (1984). Fuzzy goal programming with nested priorities. Fuzzy sets and systems 14, 115-129.
  • Shi, Y. (1995). Studuies on optimum-path ratios in multicriteria de novo programming problems, Computers. Math. Applic. 29(5), 43-50.
  • Shi, Y. (1999). Optimal system design with multiple decision makers and possible debt: a multicriteria de novo programming approach. Operations research 47(5), 723-729.
  • Tabucanon, M. T. (1988). Multiple Criteria Decision Making In Industry, Elsevier, New York
  • Tiwari, R.N., Dharmar, S. & Rao, J.R. (1986). Priority structure in fuzzy goal programming. Fuzzy sets and systems 19, 251-259.
  • Tiwari, R.N., Dharmar, S. &Rao, J.R. (1987). Fuzzy goal programming—an additive model. Fuzzy sets syst. 24, 27–34.
  • Umarusman, N. (2013). Min-max goal programming approach for solving multi-objective de novo programming problems. International journal of operations research, 10(2), 92-99.
  • Umarusman, N & Türkmen, N. (2013). Building optimum production settings using de novo programming with global criterion method. international journal of computer applications, 82(18), 12-14.
  • Wang, HF. & Fu,C.C. (1997). A Generalization of fuzzy goal programming with preemptive structure. Computers Ops Res. 24(9), 819-828.
  • Yaghoobi, M.A. & Tamiz, M. (2007). A method for solving fuzzy goal programming problems based on minmax approach. European journal of operational research 177, 1580–1590.
  • Yaghoobi, M. A., Jones, D. F. & Tamiz, M. (2008). Weighted additive models for solving fuzzy goal programming problems. Asia-pacific journal of operational research, 25(5), 715–733.
  • Yang, T., Ignizio, J. P. & Kim, H. J. (1991). Fuzzy programming with nonlinear membership functions: piecewise linear approximation. Fuzzy sets and systems, 41, 39−53.
  • Zadeh, L.A. (1965). Fuzzy Sets. Information and control 8, 338-353.
  • Zeleny, M. (1976). Multi-objective design of high-productivity systems, In.Proc. Joint Automatic Control Conf., paper, APPL9-4, New York.
  • Zeleny, M. (1982). Multiple Criteria Decision Making, McGraw-Hill Book Company, New York
  • Zeleny, M. (1984). Multicriterion design of high-productivity systems: extension and application, decision making with multiple objective pp. 308- 321, Edit by: Yacov Y. Haimes and Vira Chankong, Springer-Verlag, New York.
  • Zeleny, M. (1986). Optimal system design with multiple criteria: De Novo programming approach, Engineering Costs and Production Economics, 10, 89–94.
  • Zeleny, M. (1990). Optimizing given systems vs. Designing optimal systems: the de novo programming approach. İnt. J. General System, 17, 295-307.
  • Zhang, Y.M., Huang, G.H. and Zhang, X.D. (2009). Inexact de Novo programming for water resources systems planning. European journal of operational research, 199,531–541.
  • Zhuang, ZY & Hocine, A. (2018). Meta goal programing approach for solving multi-criteria de Novo programing problem. European journal of operational research, 265(1), 228-238.
  • Zimmerman, H.J. (1978). Fuzzy programming and linear programming with several objective functions, Fuzzy sets and systems 1 (1978) 45–55.

Optimal Sistem Tasarımı İçin Minmaks Tabanlı Bulanık Hedef Programlama Kullanımı

Yıl 2018, Cilt 6, Sayı 1, 2018, 177 - 192, 27.06.2018
https://doi.org/10.17093/alphanumeric.404680

Öz

Tabiattaki bütün sistemler, varlıklarını devam ettirmek ve hedeflerine en az kayıpla ulaşmak için zaman içerisinde değişim geçirmişlerdir. Sistemlerin başarıya ulaşabilmelerinin temel şartı birden fazla, ihtilaflı ve karmaşık amaçları mevcut kısıtlara göre değerlendirip en doğru kararı verebilmektir. Birçok matematiksel programlama problemi, karar verici tarafından kısıtlara bağlı olarak amaç fonksiyonlarının bir araya getirilmesinden oluşmaktadır. Bu çalışmada Minmaks tabanlı yaklaşımla optimal sistemin tasarımının nasıl yapılacağı araştırılmıştır. Araştırmada Minmaks Hedef Programlama ile MA yaklaşımı olarak da bilinen bir Bulanık Hedef yaklaşımı kullanılmıştır. El sanatları üretimi yapan bir işletmede kaynak miktarlarının optimal seviyede belirlenebilmesi için Çok Amaçlı De novo programlama olarak kurulan problemin çözümü bu iki yaklaşıma göre yapılmıştır. MA yaklaşımına göre problemin çözülebilmesi için bütçe kısıtı bir hedef olarak düzenlenmiş ve bir çözüm önerisi yapılmıştır. Elde edilen sonuçlara göre Minmaks Programming ve MA yaklaşımının çözüm sonuçlarının aynı olduğu belirlenmiştir.

Kaynakça

  • Aköz, O. & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European journal of operational research 181,1427–1433.
  • Aouni B., Marter JM. & Hassaine, A. (2009). Fuzzy Goal programming model: An Overview of the Current State-of-theart. J. Multi-crit. Decis. Anal. 16,149–161.
  • Babić, Z., & Pavić, I. (1996). Multicriterial production programming by de novo programming approach. International journal of production economics, 43(1), 59-66.
  • Bellman, R.E. & Zadeh, L.A. (1970). Decision-making in a fuzzy environment. Management science B.17, 141-164.
  • Chakraborty, S. & Bhattacharya, D. (2013). Optimal system design under multi-objective decision making using de-novo concept: a new approach. International journal of computer applications, 63(12), 20-27.
  • Chanas, S. (1983). The use of parametric programming in FLP. Fuzzy sets and systems 11, 243-251.
  • Chanas,S. & Gupta, K.(2002). Fuzzy goal programming-one notion, may meanings, Control and cybernteics, 31(4), 871-890.
  • Charnes, A. & W.W. Cooper,W. W. (1961). Management Models and Industrial Applications of Linear Programming, Wiley, New York.
  • Charnes A., Cooper W. W. & Ferguson R. (1955). Optimal estimation of executive compensation by linear programming. Management science, 1, 138-151.
  • Charnes, A. & Cooper, W. W. (1977). Goal programming and multiple objective optimizations, Eur. J. Oper. Res., 1, 39–54.
  • Chen, YW. & Hsieh, H.-E. (2006). Fuzzy multi-stage De Novo programming problem. Applied Mathematics and Computation 181(2), 1139-1147.
  • Chen, L. H. & Tsai, F.C. (2001). Fuzzy goal programming with different importance and priorities. European journal of operational research, 133, 548–556.
  • Chen, J.K.C. & Tzeng, G-H. (2009). Perspective strategic alliances and resource allocation in supply chain systems through the de novo programming approach. Int. J. Sustainable strategic management, 1(3), 320-339.
  • Cheng, HW. (2013). A satisficing method for fuzzy goal programming problems with different importance and priorities. Qual. quant, 47,485–498.
  • Flavell, R.B. (1976). A new goal programming formulation. Omega, the international journal of management science 4, 731–732.
  • Gupta, M. & Bhattacharjee, D. (2012). Two weighted fuzzy goal programming methods to solve multiobjective goal programming problem. Journal of applied mathematics volume 2012,1-20.
  • Hannan, E. L. (1981a). On fuzzy goal programming. Decision sciences, 12, 522–531.
  • Hannan, E. L. (1981b). Linear programning with multiple fuzzy goals. Fuzzy sets and systems. Vol. 6. 235-248.
  • Huang, J-J., Tzeng, G-H. & Ong, C-S. (2006). Choosing best alliance partners and allocating optimal alliance resources using the fuzzy multi-objective dummy programming model. Journal of the operational research society, 57, 1216–1223.
  • Ignizio, J.P. ve Cavalier, T.M. (1994). Linear programming, Prentice-Hall, Inc.,Englewood Cliffs, New Jersey.
  • Ijiri Y. (1965). Management Goals and Accounting for control, Amsterdam, North-Holland Publishing Co,
  • Kim, J.S. & Whang, K-S. (1998). A tolerance approach to the fuzzy goal programming problems with unbalanced triangular membership function. European journal of operational research 107, 614-624.
  • Lai, Y.J. & Hwang C.L. (1992). Fuzzy Mathematical Programming Springer-Verlag, Berlin.
  • Li, R.J & Lee, E.S. (1990). Approaches to multicriteria de novo programs. Journal of mathematical analysis and applications, 153, 97-111.
  • Lin, C.C. (2004). A weighted max–min model for fuzzy goal programming. Fuzzy sets and systems 142, 407–420.
  • Lee, E.S & Li, R.J. (1993). Fuzzy multiple objective programming and compromise programming with pareto optimum. Fuzzy sets and systems, 53, 275-288.
  • Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision Sciences, 11, 325–336.
  • Narasimhan, R. (1981). On Fuzzy goal programming—some comments, Decision Sci. 12, 532–538.
  • Romero, C. (1985). Multi-objective and goal programming as a distance function model. JORS, 36(3), 249-251.
  • Rubin, P.A.& Narasimhan, R. (1984). Fuzzy goal programming with nested priorities. Fuzzy sets and systems 14, 115-129.
  • Shi, Y. (1995). Studuies on optimum-path ratios in multicriteria de novo programming problems, Computers. Math. Applic. 29(5), 43-50.
  • Shi, Y. (1999). Optimal system design with multiple decision makers and possible debt: a multicriteria de novo programming approach. Operations research 47(5), 723-729.
  • Tabucanon, M. T. (1988). Multiple Criteria Decision Making In Industry, Elsevier, New York
  • Tiwari, R.N., Dharmar, S. & Rao, J.R. (1986). Priority structure in fuzzy goal programming. Fuzzy sets and systems 19, 251-259.
  • Tiwari, R.N., Dharmar, S. &Rao, J.R. (1987). Fuzzy goal programming—an additive model. Fuzzy sets syst. 24, 27–34.
  • Umarusman, N. (2013). Min-max goal programming approach for solving multi-objective de novo programming problems. International journal of operations research, 10(2), 92-99.
  • Umarusman, N & Türkmen, N. (2013). Building optimum production settings using de novo programming with global criterion method. international journal of computer applications, 82(18), 12-14.
  • Wang, HF. & Fu,C.C. (1997). A Generalization of fuzzy goal programming with preemptive structure. Computers Ops Res. 24(9), 819-828.
  • Yaghoobi, M.A. & Tamiz, M. (2007). A method for solving fuzzy goal programming problems based on minmax approach. European journal of operational research 177, 1580–1590.
  • Yaghoobi, M. A., Jones, D. F. & Tamiz, M. (2008). Weighted additive models for solving fuzzy goal programming problems. Asia-pacific journal of operational research, 25(5), 715–733.
  • Yang, T., Ignizio, J. P. & Kim, H. J. (1991). Fuzzy programming with nonlinear membership functions: piecewise linear approximation. Fuzzy sets and systems, 41, 39−53.
  • Zadeh, L.A. (1965). Fuzzy Sets. Information and control 8, 338-353.
  • Zeleny, M. (1976). Multi-objective design of high-productivity systems, In.Proc. Joint Automatic Control Conf., paper, APPL9-4, New York.
  • Zeleny, M. (1982). Multiple Criteria Decision Making, McGraw-Hill Book Company, New York
  • Zeleny, M. (1984). Multicriterion design of high-productivity systems: extension and application, decision making with multiple objective pp. 308- 321, Edit by: Yacov Y. Haimes and Vira Chankong, Springer-Verlag, New York.
  • Zeleny, M. (1986). Optimal system design with multiple criteria: De Novo programming approach, Engineering Costs and Production Economics, 10, 89–94.
  • Zeleny, M. (1990). Optimizing given systems vs. Designing optimal systems: the de novo programming approach. İnt. J. General System, 17, 295-307.
  • Zhang, Y.M., Huang, G.H. and Zhang, X.D. (2009). Inexact de Novo programming for water resources systems planning. European journal of operational research, 199,531–541.
  • Zhuang, ZY & Hocine, A. (2018). Meta goal programing approach for solving multi-criteria de Novo programing problem. European journal of operational research, 265(1), 228-238.
  • Zimmerman, H.J. (1978). Fuzzy programming and linear programming with several objective functions, Fuzzy sets and systems 1 (1978) 45–55.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Nurullah Umarusman 0000-0001-6535-5329

Yayımlanma Tarihi 27 Haziran 2018
Gönderilme Tarihi 12 Mart 2018
Yayımlandığı Sayı Yıl 2018 Cilt 6, Sayı 1, 2018

Kaynak Göster

APA Umarusman, N. (2018). Fuzzy Goal Programming Problem Based on Minmax Approach for Optimal System Design. Alphanumeric Journal, 6(1), 177-192. https://doi.org/10.17093/alphanumeric.404680

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