Utilities frequently use remote load control as an effective means to achieve suitable network operational conditions. This procedure, usually designated Load Management (LM), is a part of the resources considered und...
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Utilities frequently use remote load control as an effective means to achieve suitable network operational conditions. This procedure, usually designated Load Management (LM), is a part of the resources considered under the general designation of Demand-Side Management (DSM), However, the use of LM in electric distribution network management is a problem that involves different conflicting aspects such as reducing peak demand, maximizing utility profits and minimizing discomfort caused to consumers. Hence, the problem is multiobjective in nature: economical, technical and quality of service aspects must all be explicitly accounted for in mathematical models, This paper presents a multiobjective decision support model which allows the consideration of the main concerns that have an important role in LM: minimize peak demand as perceived by the distribution network dispatch center, maximize utility profit corresponding to the energy services delivered by the controlled loads, maximize quality of service in the context of LM.
In this paper, we have formulated a second-order mixed symmetric dual programs for a class of nondifferentiable multiobjective programming problem. Weak, strong and converse duality theorems are then proved for the af...
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In this paper, we have formulated a second-order mixed symmetric dual programs for a class of nondifferentiable multiobjective programming problem. Weak, strong and converse duality theorems are then proved for the aforementioned pair using the notion of second-order F-convexity/pseudoconvexity assumptions. Further, special cases are discussed to show that this paper extends some known results of the literature. (C) 2012 Elsevier Inc. All rights reserved.
This paper presents an application of multicriteria decision making in system level design of printed wire boards, The main decision variable is the number of signal layers, and the criteria being considered are the c...
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This paper presents an application of multicriteria decision making in system level design of printed wire boards, The main decision variable is the number of signal layers, and the criteria being considered are the cost, electrical performance, and reliability. The weighting method is first applied to find the most satisfactory solution, In the numerical example a complete description of the solution is given in terms of the weights or importance factors, The numerical results also show the stability of the solution. The computations are then repeated by applying compromise programming and the Nash solution concept. In the case of compromise programming the l(1), l(2), and l(infinity) norms are used, The numerical results obtained by the different methods are compared.
The fixed charge problem is a special type of nonlinear programming problem which forms the basis of many industry problems wherein a charge is associated with performing an activity. In real world situations, the inf...
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The fixed charge problem is a special type of nonlinear programming problem which forms the basis of many industry problems wherein a charge is associated with performing an activity. In real world situations, the information provided by the decision maker regarding the coefficients of the objective functions may not be of a precise nature. This paper aims to describe a solution algorithm for solving such a fixed charge problem having multiple fractional objective functions which are all of a fuzzy nature. The enumerative technique developed not only finds the set of efficient solutions but also a corresponding fuzzy solution, enabling the decision maker to operate in the range obtained. A real life numerical example in the context of the ship routing problem is presented to illustrate the proposed method. (C) 2015 Elsevier B.V. All rights reserved.
Hanson and Mend have given sets of necessary and sufficient conditions for optimality and duality in constrained optimization by introducing classes of generalized convex functions, called type I and type II functions...
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Hanson and Mend have given sets of necessary and sufficient conditions for optimality and duality in constrained optimization by introducing classes of generalized convex functions, called type I and type II functions. Recently, Bector defined univex functions, a new class of functions that unifies several concepts of generalized convexity. In this paper, optimality and duality results for several mathematical programs are obtained combining the concepts of type I and univex functions. Examples of functions satisfying these conditions are given.
In this article, we focus on a class of a fractional interval multivalued programming problem. For the solution concept, LU-Pareto optimality and LS-Pareto, optimality are discussed, and some nontrivial concepts are a...
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In this article, we focus on a class of a fractional interval multivalued programming problem. For the solution concept, LU-Pareto optimality and LS-Pareto, optimality are discussed, and some nontrivial concepts are also illustrated with small examples. The ideas of LU-V-invex and LS-V-invex for a fractional interval problem are introduced. Using these invexity suppositions, we establish the Karush-Kuhn-Tucker optimality conditions for the problem assuming the functions involved to begH-differentiable. Non-trivial examples are discussed throughout the manuscript to make a clear understanding of the results established. Results obtained in this paper unify and extend some previously known results appeared in the literature.
Seismic design problem of a steel moment-resisting frame is formulated as a multiobjective programming problem. The total structural (material) volume and the plastic dissipated energy at the collapse state against se...
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Seismic design problem of a steel moment-resisting frame is formulated as a multiobjective programming problem. The total structural (material) volume and the plastic dissipated energy at the collapse state against severe seismic motions are considered as performance measures. Geometrically nonlinear inelastic time-history analysis is carried out against recorded ground motions that are incrementally scaled to reach the predefined collapse state. The frame members are chosen from the lists of the available standard sections. Simulated annealing (SA) and tabu search (TS), which are categorized as single-point-search heuristics, are applied to the multiobjective optimization problem. It is shown in the numerical examples that the frames that collapse with uniform interstorey drift ratios against various levels of ground motions can be obtained as a set of Pareto optimal solutions. Copyright (c) 2007 John Wiley & Sons, Ltd.
The nonconvex programming problem of minimizing a quasi-concave function over an efficient (or weakly efficient) set of a multiobjective linear program is studied. A cutting plane algorithm which finds an approximate ...
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The nonconvex programming problem of minimizing a quasi-concave function over an efficient (or weakly efficient) set of a multiobjective linear program is studied. A cutting plane algorithm which finds an approximate optimal solution in a finite number of steps is developed. For the particular ''all linear'' case the algorithm performs better, finding an optimal solution in a finite time, and being more easily implemented.
An interesting variant of the assignment problem is the case where each partial assignment of an individual to a job involves multiple inputs and outputs. In this paper, three issues about this problem are discussed: ...
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An interesting variant of the assignment problem is the case where each partial assignment of an individual to a job involves multiple inputs and outputs. In this paper, three issues about this problem are discussed: finding an efficient assignment, verifying the efficiency of a solution and restoring the efficiency of an inefficient assignment. For the first issue, a current method, proposed by Chen and Lu, is compared with a proposed multiobjective formulation and for the second and third ones, a two-phase method is developed, which is based on the simplex method and the Dantzig-Wolfe decomposition algorithm.
This paper applies artificial neural network ( ANN) to model the observed effluent quality data. The ANN's structure, involving the number of hidden layer and node and their connection, is determined endogenously ...
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This paper applies artificial neural network ( ANN) to model the observed effluent quality data. The ANN's structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable ( mu) that represents the level of aspiration achieved in one objective and the conjugate of mu, ( 1-mu), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm ( GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant ( WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate ( Q), CN-, SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying mu ( aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN- and SS in equalization basin;aeration tank hydraulic retention time ( HRT) and percentage of returned sludge ( R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.
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