Narasimhan incorporated fuzzy set theory within goal programming formulation in 1980. Since then numerous research has been carried out in this field. One of the well-known models for solving fuzzy goal programming pr...
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Narasimhan incorporated fuzzy set theory within goal programming formulation in 1980. Since then numerous research has been carried out in this field. One of the well-known models for solving fuzzy goal programming problems was proposed by Hannan in 1981. In this paper the conventional MINMAX approach in goal programming is applied to solve fuzzy goal programming problems. It is proved that the proposed model is an extension to Hannan model that deals with unbalanced triangular linear membership functions. In addition, it is shown that the new model is equivalent to a model proposed in 1991 by Yang et al. Moreover, a weighted model of the new approach is introduced and is compared with Kim and Whang's model presented in 1998. A numerical example is given to demonstrate the validity and strengths of the new models. (c) 2005 Elsevier B.V. All rights reserved.
This paper presents a model for determining an optimal blend of ingredients for livestock feed by application of goal programming. Besides the standard problem of livestock feed where the requirements for basic nutrie...
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This paper presents a model for determining an optimal blend of ingredients for livestock feed by application of goal programming. Besides the standard problem of livestock feed where the requirements for basic nutrients have to be met at minimized costs and which is solved mainly by linear programming, the authors also introduce the goals of meal quality where different requirements of decision makers are modeled by goal programming. (C) 2010 Elsevier B.V. All rights reserved.
This paper proposes several goal programming (GP) models for estimating the performance measure weights of firms by means of constrained regression. Since some single-criterion performance measures are usually in conf...
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This paper proposes several goal programming (GP) models for estimating the performance measure weights of firms by means of constrained regression. Since some single-criterion performance measures are usually in conflict, we propose two opposed alternatives for determining multiple-criterion performance: the first is to calculate a consensus performance that reflects the majority trend of the single-criterion measures and the other is to calculate a performance that is biased towards the measures that show the most discrepancy with the rest. GP makes it possible to model both approaches as well as a compromise between the two extremes. Using two case studies reported in the literature and introducing another one examining non-financial companies listed in Ibex-35, we compare our proposal with other methods such as CRITIC and a modified version of TOPSIS. In order to improve the comparisons a Montecarlo simulation has been performed in all three case studies. Scope and purpose: The study falls into the area of multiple-criteria analysis of business performance. Firms are obliged to report a vast amount of financial information at regular intervals, and for this there is a wide range of performance measures. Multicriteria performance is calculated from the single-criterion measures and is then used to draw up rankings of firms. As a complement to the other multicriteria methods described in the literature, we propose the use of GP for implementing two quite different strategies: overweighting the measures in line with the general trend or overweighting the measures that conflict with the rest. Besides the use of Spearman's correlation, we introduce two other measures for comparing the solutions obtained. (C) 2009 Elsevier Ltd. All rights reserved.
Piecewise linear function (PLF) is an important technique for solving polynomial and/or posynomial programming problems since the problems can be approximately represented by the PLF. The PLF can also be solved using ...
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Piecewise linear function (PLF) is an important technique for solving polynomial and/or posynomial programming problems since the problems can be approximately represented by the PLF. The PLF can also be solved using the goal programming (GP) technique by adding appropriate linearization constraints. This paper proposes a modified GP technique to solve PLF with n terms. The proposed method requires only one additional constraint, which is more efficient than some well-known methods such as those proposed by Charnes and Cooper's, and Li. Furthermore, the proposed model (PM) can easily be applied to general polynomial and/or posynomial programming problems. (C) 2002 Elsevier Science B.V. All rights reserved.
This paper develops a general formulation of dependent-chance goal programming (DCGP) which is an extension of stochastic goal programming in a complex stochastic system, and gives an example of water allocation and s...
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This paper develops a general formulation of dependent-chance goal programming (DCGP) which is an extension of stochastic goal programming in a complex stochastic system, and gives an example of water allocation and supply to show the application of DCGP. A genetic algorithm based approach is also presented to solve such a model. DCGP is available to the systems in which there are multiple stochastic inputs and multiple outputs with their own reliability levels. The characteristic of DCGP is that the chances of some probabilistic goals are dependent, i.e., the goals cannot be considered in isolation or converted to their deterministic equivalents. Finally, Monte Carlo simulation is also discussed for calculating the chance functions in complex stochastic constraints.
This study attempts to develop a quasi type-2 fuzzy regression model in full quasi type-2 fuzzy environment. To estimate the parameters of the proposed model, first, a weighted distance between quasi type-2 fuzzy numb...
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This study attempts to develop a quasi type-2 fuzzy regression model in full quasi type-2 fuzzy environment. To estimate the parameters of the proposed model, first, a weighted distance between quasi type-2 fuzzy numbers is defined based on L1-norm. Then some approximations for multiplication of two quasi type-2 fuzzy numbers (QT2FNs) are introduced. The problem of estimation of the parameters relies on a non-linear optimization problem, which is converted to a linear optimization problem. The method can handel both symmetric and asymmetric data. Two real world examples demonstrate the feasibility and efficiency of the proposed method. The predictive performance of the model is examined by cross-validation, and a similarity measure is used to compare our model with a similar model.
A decision support model to help public water agencies allocate surface water among farmers and authorize the use of groundwater for irrigation (especially in Mediterranean dry regions) is developed. This is a stochas...
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A decision support model to help public water agencies allocate surface water among farmers and authorize the use of groundwater for irrigation (especially in Mediterranean dry regions) is developed. This is a stochastic goal programming approach with two goals, the first concerning farm management while the other concerns environmental impact. Targets for both goals are established by the agency. This model yields three reduction factors to decide the different reductions in available surface water, standard groundwater and complementary groundwater that the agency should grant/authorize for irrigation, this depending on if it is a dry or wet year. In drought periods, the model recommends using more groundwater (in percentage) than in wet periods. A case study using year-to-year statistical information on available water over the period 1941-2005 is developed through numerical tables. A step-by-step computational process is presented in detail. (c) 2008 Elsevier B.V. All rights reserved.
During the last two decades there has been wide application of goal programming. Despite this popularity, goal programming theories have been extensively critized. Although some criticism may originate from the valid ...
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During the last two decades there has been wide application of goal programming. Despite this popularity, goal programming theories have been extensively critized. Although some criticism may originate from the valid assessment of goal programming, large portions of the criticism seem to stem from confusions and misconceptions in underlying goal programming theories. In this paper, the origin of critical views on goal programming is uncovered and synthesized, to set the stage for a balanced discussion of the pros and cons of goal programming. Through such a discussion, the critical points are validated and, if necessary, some possible remedies for the weakness of goal programming are sought. Hence, the major intent of this paper is to provide a variety of future research ideas which, it is hoped, lend themselves to the further development and application of goal programming rather than provoking any controversies surrounding goal programming.
For the Internet of Things (IoT) that have low processing power, fog computing can play an essential role as a processing resource to execute their tasks. Timely execution of the service and the optimal service cost a...
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For the Internet of Things (IoT) that have low processing power, fog computing can play an essential role as a processing resource to execute their tasks. Timely execution of the service and the optimal service cost are two major challenges of task scheduling in fog computing. In this article, a privacy architecture is proposed for task scheduling in IoT, and based on this architecture, a multi-objective algorithm is presented to minimize the service time and service cost. We simulate the problem with four scenarios: easy, medium, semi-hard, and hard, and then compare it with other multi-objective algorithms. Since the proposed algorithm is multi-objective, the goal programming approach (GPA) is used to choose one possible solution. The simulation results show that our proposed algorithm has better performance and higher convergence speed to the optimal solution than other algorithms while considering the privacy requirements of IoT devices.
The main aim of this paper is to investigate the weight vectors of interval fuzzy preference relations and interval multiplicative preference relations. In view of the excellent properties of multiplicative transitivi...
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The main aim of this paper is to investigate the weight vectors of interval fuzzy preference relations and interval multiplicative preference relations. In view of the excellent properties of multiplicative transitivity in modeling the consistency of fuzzy preference relations, based on the multiplicative consistency property, a goal programming model of obtaining the priority weights from an interval fuzzy preference relation is introduced along with some of its desired properties. The priority vector of a fuzzy preference relation can also be derived using this goal programming model, where the optimal value of the objective function is always equal to zero. Numerical examples are provided to illustrate the validity of the proposed model. (C) 2016 Elsevier Inc. All rights reserved.
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