scholarly journals Interactive Fuzzy Goal Programming Based on Taylor Series to Solve Multiobjective Nonlinear Programming Problems With Interval Type-2 Fuzzy Numbers

2018 ◽  
Vol 26 (4) ◽  
pp. 2434-2449 ◽  
Author(s):  
Hasan Dalman ◽  
Mustafa Bayram
Keyword(s):  
Nonlinear Programming ◽  
Goal Programming ◽  
Taylor Series ◽  
Fuzzy Numbers ◽  
Fuzzy Goal Programming ◽  
Multiobjective Nonlinear Programming ◽  
Fuzzy Goal ◽  
Interval Type ◽  
Interactive Fuzzy Goal Programming

Fuzzy Goal Programming With Interval Type-2 for Solving Multi-Objective Sustainable Supplier Selection Problems

2021 ◽  
pp. 164-197
Author(s):  
Nurullah UMARUSMAN
Keyword(s):  
Supply Chain ◽  
Goal Programming ◽  
Supplier Selection ◽  
De Novo ◽  
Fuzzy Goal Programming ◽  
Multi Objective ◽  
Sustainable Supplier Selection ◽  
Fuzzy Goal ◽  
Interval Type

Supply chain management is going on changing and developing in line with the needs of the growing global supply chain. Performance of supply chain, considered as a whole so that businesses can accommodate these evolvements and change, needs to be improved in the long run. Actually, businesses work with suppliers complying with their policies from past to present. However, other dimensions of sustainability should be considered, as well as economic criteria when selecting suppliers. With the right supplier selection made in this respect, by contributing to the efficient functioning of the supply chain, it will increase customer satisfaction, and therefore, the enterprises will reach the goals they set. The solution of the multi-objective sustainable supplier selection problem has been realized by using the “satisfied optimal supplier design” algorithm, also called fuzzy goal programming, with de novo-based interval type-2 proposed in this study.

On Solving Multiobjective Transportation Problems with Fuzzy Random Supply and Demand Using Fuzzy Goal Programming

2017 ◽  
Vol 8 (3) ◽  
pp. 54-81 ◽  
Author(s):  
Animesh Biswas ◽  
Nilkanta Modak
Keyword(s):  
Goal Programming ◽  
Fuzzy Numbers ◽  
Optimal Allocation ◽  
Programming Model ◽  
Fuzzy Goal Programming ◽  
Chance Constrained Programming ◽  
Transportation Problems ◽  
Goal Programming Model ◽  
Fuzzy Goal ◽  
Fuzzy Random

In this article a fuzzy goal programming model is developed to solve multiobjective unbalanced transportation problems with fuzzy random parameters. In model formulation process the cost coefficients of the objectives are considered as fuzzy numbers and the supplies and demands are considered as fuzzy random variables with known fuzzy probability distribution from the view point of probabilistic as well as possibilistic uncertainties involved with the model. A fuzzy programming model is first constructed by applying chance constrained programming methodology in fuzzy environment. Then, the model is decomposed on the basis of the tolerance ranges of the fuzzy numbers associated with it. The individual optimal solution of each decomposed objectives is found in isolation to construct the membership goals of the objectives. Finally, priority based fuzzy goal programming technique is used to achieve the highest degree of each of the defined membership goals to the extent possible by minimizing the under deviational variables and thereby obtaining optimal allocation of products by using distance function in a cost minimizing decision making environment. An illustrative example is solved and compared with existing technique to explore the potentiality of the proposed methodology.

On Development of a Fuzzy Stochastic Programming Model with Its Application to Business Management

2017 ◽  
pp. 353-378
Author(s):  
Animesh Biswas ◽  
Arnab Kumar De
Keyword(s):  
Production Planning ◽  
Goal Programming ◽  
Fuzzy Numbers ◽  
Programming Model ◽  
Random Variables ◽  
Fuzzy Goal Programming ◽  
Fuzzy Random Variables ◽  
Fuzzy Goal ◽  
Constrained Programming ◽  
Fuzzy Random

This chapter expresses efficiency of fuzzy goal programming for multiobjective aggregate production planning in fuzzy stochastic environment. The parameters of the objectives are taken as normally distributed fuzzy random variables and the chance constraints involve joint Cauchy distributed fuzzy random variables. In model formulation process the fuzzy chance constrained programming model is converted into its equivalent fuzzy programming using probabilistic technique, a-cut of fuzzy numbers and taking expectation of parameters of the objectives. Defuzzification technique of fuzzy numbers is used to find multiobjective linear programming model. Membership function of each objective is constructed depending on their optimal values. Afterwards a weighted fuzzy goal programming model is developed to achieve the highest degree of each of the membership goals to the extent possible by minimizing group regrets in a multiobjective decision making context. To explore the potentiality of the proposed approach, production planning of a health drinks manufacturing company has been considered.

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