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Goal programming

About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.


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TL;DR: A new optimal model for time–cost trade-off problem in a fuzzy environment is presented and a new solution method for possibility goal programming problems is developed, the significant feature of which is the determination of optimal duration for each activity in the form of triangular fuzzy numbers.
Abstract: Time–cost trade-off problem is one of the main aspects of project scheduling. Due to variations in the real world, usually, risks in estimation of project parameters are considerably high. Therefore, use of uncertain models, which is capable of formulating vagueness in the real world, to solve time–cost trade-off problems, gives a scheduling with more stability against environmental variations. On the other hand, crisp decision making in uncertain environment causes loss of some parts of information. This paper presents a new optimal model for time–cost trade-off problem in a fuzzy environment. In order to solve this problem, a new solution method for possibility goal programming problems is developed. The significant feature of this model is the determination of optimal duration for each activity in the form of triangular fuzzy numbers. To validate the algorithm developed here, a case study will be presented.

31 citations

Journal ArticleDOI
TL;DR: This paper shows a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach, and provides sensitivity analysis with variation of tolerance values on decision vectors to show how the solution is sensitive to the change ofolerance values.
Abstract: In this paper, we show a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach. In the proposed method, the tolerance membership functions for the fuzzily described numerator and denominator part of the objective functions of all levels as well as the control vectors of the higher level decision makers are respectively defined by determining individual optimal solutions of each of the level decision makers. A possible relaxation of the higher level decision is considered for avoiding decision deadlock due to the conflicting nature of objective functions. Then, fuzzy goal programming approach is used for achieving the highest degree of each of the membership goal by minimizing negative deviational variables. We also provide sensitivity analysis with variation of tolerance values on decision vectors to show how the solution is sensitive to the change of tolerance values with the help of a numerical example.

31 citations

Journal ArticleDOI
TL;DR: 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 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.
Abstract: 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.

31 citations

Journal ArticleDOI
TL;DR: In this article, the authors employ the analytic network process (ANP) to model the interactions between eight SQM strategies and the three types of resources (human, organisational and technological) needed for effective strategy implementation, and then formulates a goal programming (GP) model to identify the extent to which each single strategy is inhibited by a lack of (or overloaded by) resources.
Abstract: Purpose – The purpose of this paper is to assist organizations in understanding the nature of quality management from a resource‐based perspective by investigating the relationship between strategies needed to drive quality enhancement, and resources being allocated to support effective strategy implementation. The resource‐based view of TQM elements led this case study research to deal with quality management from a strategic viewpoint, or what is known as Strategic Quality Management (SQM).Design/methodology/approach – The paper employs the analytic network process (ANP) to model the interactions between eight SQM strategies and the three types of resources (human, organisational and technological) needed for effective strategy implementation. The paper then formulates a goal programming (GP) model in order to identify the extent to which each single strategy is inhibited by a lack of (or overloaded by) resources. Using a case study approach, the hybrid ANP‐GP methodology is employed to illustrate the a...

31 citations

Journal ArticleDOI
TL;DR: In this article, a mathematical model is formulated in light of fuzzy and weighted goal programming using membership function to quantify the achievement level towards vision 2030; hence aims to bridge the existing literature gap.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202335
202271
2021151
2020138
2019160
2018145