Fuzzy goal programming model for classification problems

2015 ◽  
Vol 251 (1-2) ◽  
pp. 141-160 ◽  
Author(s):  
Soulef Smaoui ◽  
Belaid Aouni
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Fuzzy Goal Programming ◽  
Classification Problems ◽  
Goal Programming Model ◽  
Fuzzy Goal

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.

Optimizing performance of oil filling processes using fuzzy goal programming

2017 ◽  
Vol 232 (3) ◽  
pp. 380-390 ◽  
Author(s):  
Abbas Al-Refaie ◽  
Yaser Abu Ghazaleh ◽  
Ming-Hsien Li
Keyword(s):  
Production Rate ◽  
Goal Programming ◽  
Control Charts ◽  
Programming Model ◽  
Fuzzy Goal Programming ◽  
Statistical Control ◽  
Quality Costs ◽  
Goal Programming Model ◽  
Fuzzy Goal ◽  
Filling Line

This research aims at improving the performance of the filling line machine using fuzzy goal programming. Two main quality responses including the number of defective products and the production rate of the filling machine are of main interest. Initially, the control charts for number of nonconforming and production rate, np and I-MR, respectively, are established and indicate that the process is in statistical control. However, the process is found incapable. The fuzzy goal programming model is adopted to identify the combination of optimal factor settings utilizing the Taguchi’s L16 array. The optimal factor settings are 5.6 s, 5.6 s, 6.4 s, 6.0 s, 75°, 75°, 1.9 cm, 2.5 cm, 1.0 s, 0.9 s, 5.8 s, and 0.11 s for timing nozzle # 1, timing nozzle # 2, timing nozzle # 3, timing nozzle # 4, weighing valve # 1, weighing valve # 2, crimping head # 1 height, crimping head # 2 height, crimping time # 1, crimping time # 2, crimping delay, and conveyer, respectively. Confirmation experiments are conducted at optimal factor settings. Results showed reduction in the number of defective cans and significant enhancement of the oil filling process capability. In conclusions, the fuzzy goal programming model is found to be an efficient technique in supporting the process engineers of oil filling line for obtaining significant yearly savings in quality costs and considerable productivity gains.

scholarly journals Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposal

2017 ◽  
Vol 10 (5) ◽  
pp. 853
Author(s):  
Narong Wichapa ◽  
Porntep Khokhajaikiat
Keyword(s):  
Genetic Algorithm ◽  
Goal Programming ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Fuzzy Goal Programming ◽  
Location Routing Problem ◽  
Location Routing ◽  
Goal Programming Model ◽  
Fuzzy Goal ◽  
Infectious Waste

Purpose: Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management.Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move.Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively.Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

A fuzzy goal programming model for skill-based personnel assignments

2012 ◽  
Vol 2 (4) ◽  
pp. 313 ◽  
Author(s):  
Luis Daniel Otero ◽  
Grisselle Centeno ◽  
Carlos E. Otero ◽  
Alex J. Ruiz Torres
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Fuzzy Goal Programming ◽  
Goal Programming Model ◽  
Fuzzy Goal

scholarly journals A fuzzy goal programming model for biodiesel production

2016 ◽  
Vol 693 ◽  
pp. 012007 ◽  
Author(s):  
D S Lutero ◽  
EMU Pangue ◽  
J M Tubay ◽  
S P Lubag
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Fuzzy Goal Programming ◽  
Biodiesel Production ◽  
Goal Programming Model ◽  
Fuzzy Goal

scholarly journals Fuzzy Goal Programming Procedure to Bilevel Multiobjective Linear Fractional Programming Problems

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Mahmoud A. Abo-Sinna ◽  
Ibrahim A. Baky
Keyword(s):  
Goal Programming ◽  
Fractional Programming ◽  
Programming Model ◽  
Fuzzy Goal Programming ◽  
Decision Makers ◽  
Membership Functions ◽  
Linear Fractional Programming ◽  
Goal Programming Model ◽  
Fuzzy Goals ◽  
Fuzzy Goal

This paper presents a fuzzy goal programming (FGP) procedure for solving bilevel multiobjective linear fractional programming (BL-MOLFP) problems. It makes an extension work of Moitra and Pal (2002) and Pal et al. (2003). In the proposed procedure, the membership functions for the defined fuzzy goals of the decision makers (DMs) objective functions at both levels as well as the membership functions for vector of fuzzy goals of the decision variables controlled by first-level decision maker are developed first in the model formulation of the problem. Then a fuzzy goal programming model to minimize the group regret of degree of satisfactions of both the decision makers is developed to achieve the highest degree (unity) of each of the defined membership function goals to the extent possible by minimizing their deviational variables and thereby obtaining the most satisfactory solution for both decision makers. The method of variable change on the under- and over-deviational variables of the membership goals associated with the fuzzy goals of the model is introduced to solve the problem efficiently by using linear goal programming (LGP) methodology. Illustrative numerical example is given to demonstrate the procedure.

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