This paper provides a methodology for the optimization of an existing electrical distribution network when upgraded by renewable energies. The contribution of renewable energy in electricity generation is decided upon...
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This paper provides a methodology for the optimization of an existing electrical distribution network when upgraded by renewable energies. The contribution of renewable energy in electricity generation is decided upon through both network design optimization and proper load management whereby applications that can be satisfied by non-electrical means are separated from the main load. The remaining load will then be satisfied by an optimal mix of renewable energy which will be injected to the existing grid. The proposed problem will be formulated using multiobjective linear programming in conjunction with fuzzy logic. It will be shown that optimization using fuzzy logic can provide decision makers with more flexibility that would assist them in the allocation of various energy resources to optimally meet the various end uses and solve the problem of renewable energy connection to existing distribution networks. Copyright (C) 1999 John Wiley & Sons, Ltd.
An interactive multiple objective system technique (IMOST) is investigated to improve the flexibility and robustness of multiple objective decision making (MODM) methodologies. The interactive concept provides a learn...
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An interactive multiple objective system technique (IMOST) is investigated to improve the flexibility and robustness of multiple objective decision making (MODM) methodologies. The interactive concept provides a learning process about the system, whereby the decision maker can learn to recognize good solutions, the relative importance of factors in the system, and then design a high-productivity and zero-buffer system instead of optimizing a given system. This interactive technique provides integration-oriented, adaptation and dynamic learning features by considering all possibilities of a specific domain of MODM problems which are integrated in logical order. It encompasses the decision-making processes of formulating problems, constructing a model, solving the model, testing/examining its solution, and improving/reshaping the model and its solution in a specific problem domain. Although IMOST deals with multiple objective programming problems, it also provides some valuable orientation of integrated system methodologies.
Consider the problem: （P） minF（x） =(f;（x）, f;（x）,…, f;（x）) s.t. x∈S This is the model of multiobjective *** m=1, （P） is a single-objective prograrmming. The algorithm and its convergence for single-objec...
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Consider the problem: （P） minF（x） =(f;（x）, f;（x）,…, f;（x）) s.t. x∈S This is the model of multiobjective *** m=1, （P） is a single-objective prograrmming. The algorithm and its convergence for single-objective programming have been discussed in Bazaraa and Shetty’s book. In this paper,
A linear programming model is introduced to solve cooperative games. The solution is always Pareto optimal. it is based on the idea of the core but instead of requiring rationality for all groups, a multiobjective app...
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A linear programming model is introduced to solve cooperative games. The solution is always Pareto optimal. it is based on the idea of the core but instead of requiring rationality for all groups, a multiobjective approach is proposed including the importance weights of the players. A case study illustrates the application of this method. (C) 2009 Elsevier Ltd. All rights reserved.
Upmanyu and Saxena (Applied Soft Computing 40 (2016) 64-69) proposed a method for solving a multiobjective fixed charge problem having multiple fractional objective functions which are all of a fuzzy nature. The aim o...
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Upmanyu and Saxena (Applied Soft Computing 40 (2016) 64-69) proposed a method for solving a multiobjective fixed charge problem having multiple fractional objective functions which are all of a fuzzy nature. The aim of this note is to aware the researchers that the method, proposed by Upmanyu and Saxena, is not valid and hence, to propose a method for solving this type of fixed charge problem is still an open challenging research problem. (C) 2017 Elsevier B.V. All rights reserved.
A key enabler for the smart grid is the fine-grained monitoring of power utilization. Although such a mechanism is helpful in the optimization of the whole electricity generation, distribution, and consumption cycle, ...
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A key enabler for the smart grid is the fine-grained monitoring of power utilization. Although such a mechanism is helpful in the optimization of the whole electricity generation, distribution, and consumption cycle, it also creates opportunities for the potential adversaries in deducing the activities and habits of the subscribers. In fact, by utilizing the standard and readily available tools of nonintrusive load monitoring (NILM) techniques on the metered electricity data, many details of customers' personal lives can be easily discovered. Therefore, prevention of such adversarial exploitations is of utmost importance for privacy protection. One strong privacy preservation approach is the modification of the metered data through the use of on-site storage units in conjunction with renewable energy resources. In this study, we introduce a novel mathematical programming framework to model eight privacy-enhanced power-scheduling strategies inspired and elicited from the literature. We employ all the relevant techniques for the modification of the actual electricity utilization (i.e., on-site battery, renewable energy resources, and appliance load moderation). Our evaluation framework is the first in the literature, to the best of our knowledge, for a comprehensive and fair comparison of the load-shaping techniques for privacy preservation. In addition to the privacy concerns, we consider monetary cost and disutility of the users in our objective functions. Evaluation results show that privacy preservation strategies in the literature differ significantly in terms of privacy, cost, and disutility metrics.
A fuzzy regression model is used in evaluating the functional relationship between the dependent and independent variables in a fuzzy environment. Most fuzzy regression models are considered to be fuzzy outputs and pa...
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A fuzzy regression model is used in evaluating the functional relationship between the dependent and independent variables in a fuzzy environment. Most fuzzy regression models are considered to be fuzzy outputs and parameters but non-fuzzy (crisp) inputs. In general, there are two approaches in the analysis of fuzzy regression models: linear-programming-based methods and fuzzy least-squares methods. In 1992, Sakawa and Yano considered fuzzy linear regression models with fuzzy outputs, fuzzy parameters and also fuzzy inputs. They formulated multiobjective programming methods for the model estimation along with a linear-programming-based approach. In this paper, two estimation methods along with a fuzzy least-squares approach are proposed. These proposed methods can be effectively used for the parameter estimation. Comparisons are also made between them. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper, we propose a multiobjective credibilistic model with fuzzy chance constraints of the portfolio selection problem. The key financial criteria used are short-term return, long-term return, risk and liquid...
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In this paper, we propose a multiobjective credibilistic model with fuzzy chance constraints of the portfolio selection problem. The key financial criteria used are short-term return, long-term return, risk and liquidity. The model generates portfolios which are optimal to the extent of achieving the highest credibility values for the objective functions. The problem is solved using a hybrid intelligent algorithm that integrates fuzzy simulation with a real-coded genetic algorithm. The approach adopted here has advantage of handling the multiobjective portfolio selection problem where fuzzy parameters are characterized by general functional forms. Numerical examples are provided to demonstrate effectiveness of the solution approach and efficiency of the model. (C) 2012 Elsevier Inc. All rights reserved.
In the design of a new urban retail network, the size of each office can be determined-once the number and the location of outlets have been fixed-by means of a location-allocation model. In order to carry out this ta...
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In the design of a new urban retail network, the size of each office can be determined-once the number and the location of outlets have been fixed-by means of a location-allocation model. In order to carry out this task, two different solutions have been considered: the best solution in the opinion of the firm's managers and the solution obtained by maximizing the outlets' accessibility, based on a spatial interaction model. Our biobjective program bridges the gap between both solutions by enabling the generation of a finite set of non-inferior points, and constitutes, therefore, a valuable decision-support tool. The paper closes with a case study in the banking sector.
The Pareto (or nondominated set) for a multiobjective optimization problem is often of nontrivial size, and the decision maker may have a difficult time establishing objective criterion weights to select a solution. I...
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The Pareto (or nondominated set) for a multiobjective optimization problem is often of nontrivial size, and the decision maker may have a difficult time establishing objective criterion weights to select a solution. In light of these issues, clustering or partitioning methods can be of considerable value for pruning the Pareto set and limiting the decision to a few choice exemplars. A three-stage approach is proposed. In stage one, a variance-to-range measure is used to normalize the criterion function values. In stage two, maximum split partitioning and p-median partitioning are each applied to the normalized measures, thus producing two partitions of the Pareto set and two sets of exemplars. Finally, in stage three, the union of the exemplars obtained by the two partitioning methods is accepted as the final set of exemplars. The partitioning methods are compared within the context of multiobjective allocation of a cross-trained workforce to achieve both operational and human resource objectives. (C) 2017 Elsevier Ltd. All rights reserved.
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