This paper presents an efficient algorithm for finding all characteristic curves of nonlinear resistive circuits. This algorithm is based on interval analysis and a new computational test for nonexistence of a charact...
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This paper presents an efficient algorithm for finding all characteristic curves of nonlinear resistive circuits. This algorithm is based on interval analysis and a new computational test for nonexistence of a characteristic curve in a given region X. In this test, the simplex method is applied to a linear programming (LP) problem whose feasible region contains all characteristic curves in X. Such an LP problem can be formulated by replacing the component nonlinear functions with auxiliary variables and linear inequalities that are obtained by using interval extensions. If the feasible region of the LP problem is empty (that can be easily checked by the simplex method), then there is no characteristic curve in X. This test is much more powerful than the conventional nonexistence test, which makes the number of analyzed regions much smaller. By numerical examples, it is shown that the proposed algorithm can find all characteristic curves of nonlinear resistive circuits much more efficiently than the conventional algorithm. (C) 2001 Scripta Technica.
The aim of this paper is to transform a multi-choice linear programming problem to a standard mathematical programming problem where the right hand side goals of some constraints are 'multi-choice' in nature. ...
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The aim of this paper is to transform a multi-choice linear programming problem to a standard mathematical programming problem where the right hand side goals of some constraints are 'multi-choice' in nature. For each of the constraint there may exist multiple number of goals, out of which exactly one is to be chosen. The selection of goals should be in such a manner that the combination of choices for each constraint should provide an optimal solution to the objective function. There may be more than one combination which will provide an optimal solution. However the problem cannot be solved by standard linear programming techniques. In order to solve the present multi-choice linear programming problem, this paper proposes a new transformation technique. Binary variables are introduced in the transformation technique to formulate a non-linear mixed integer programming model. Using standard non-linear programming software optimal solution of the proposed model can be obtained. Finally, a numerical example is presented to illustrate the transformation technique and the solution procedure. (C) 2009 Elsevier Inc. All rights reserved.
Considering the ever changing market conditions, it is essential to design responsive and flexible manufacturing systems. This study addresses the multi-period Dynamic Cellular Manufacturing System (DCMS) design probl...
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Considering the ever changing market conditions, it is essential to design responsive and flexible manufacturing systems. This study addresses the multi-period Dynamic Cellular Manufacturing System (DCMS) design problem and introduces a new mathematical model. The objective function of the mathematical model considers inter-cell and intra-cell material handling, machine purchasing, layout reconfiguration, variable and constant machine costs. Machine duplication, machine capacities, operation sequences, alternative processing routes of the products, varying demands of products and lot splitting are among the most important issues addressed by the mathematical model. It makes decisions on many system related issues, including cell formation, inter- and intra-cell layout, product routing and product flow between machines. Due to the complexity of the problem, we suggest two heuristic solution approaches that combine Simulated Annealing (SA) with linear programming and Genetic Algorithm (GA) with linear programming. The developed approaches were tested using a data set from the literature. In addition, randomly generated test problems were also used to investigate the performance of the hybrid heuristic approaches. A problem specific lower bound mathematical model was also proposed to observe the solution quality of the developed approaches. The suggested approaches outperformed the previous study in terms of both computational time and the solution quality by reducing the overall system cost. (C) 2015 Elsevier Ltd. All rights reserved.
There is a need to augment the irrigation operation policy, change of cropping pattern to improve the efficiency of canal system and to increase the overall productivity in the command area. This paper reports an opti...
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This paper introduces an approach to modeling the uncertainties concerning future characteristics of energy technologies within the framework of long-term dynamic linear programming models. The approach chosen explici...
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This paper introduces an approach to modeling the uncertainties concerning future characteristics of energy technologies within the framework of long-term dynamic linear programming models. The approach chosen explicitly incorporates the uncertainties in the model, endogenizing interactions between decision structure and uncertainties involved. The use of this approach for future investment costs of electricity generation technologies in the framework of very long-term energy scenarios shows improvements in model behavior and more robust solutions with respect to technology choices made. Copyright (C) 1996 Elsevier Science Ltd.
The optimization of synthesis, design and operation in trigeneration systems for building applications is a quite complex task, due to the high number of decision variables, the presence of irregular heat, cooling and...
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The optimization of synthesis, design and operation in trigeneration systems for building applications is a quite complex task, due to the high number of decision variables, the presence of irregular heat, cooling and electric load profiles and the variable electricity price. Consequently, computer-aided techniques are usually adopted to achieve the optimal solution, based either on iterative techniques, linear or non-linear programming or evolutionary search. Large efforts have been made in improving algorithm efficiency, which have resulted in an increasingly rapid convergence to the optimal solution and in reduced calculation time;robust algorithm have also been formulated, assuming stochastic behaviour for energy loads and prices. This paper is based on the assumption that margins for improvements in the optimization of trigeneration systems still exist, which require an in-depth understanding of plant's energetic behaviour. Robustness in the optimization of trigeneration systems has more to do with a "correct and comprehensive" than with an "efficient" modelling, being larger efforts required to energy specialists rather than to experts in efficient algorithms. With reference to a mixed integer linear programming model implemented in MatLab for a trigeneration system including a pressurized (medium temperature) heat storage, the relevant contribute of thermoeconomics and energo-environmental analysis in the phase of mathematical modelling and code testing are shown. (C) 2008 Elsevier Ltd. All rights reserved.
A mathematical linear programming (LP) model was developed to optimize the decision-making for managing a cogeneration facility as a potential clean-development mechanism project in a hospital in Palestine. The model ...
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A mathematical linear programming (LP) model was developed to optimize the decision-making for managing a cogeneration facility as a potential clean-development mechanism project in a hospital in Palestine. The model was developed to optimize the cost of energy and the cost of installation of a small cogeneration plant under constraints on electricity-and-heat supply and demand balances. In the model, the sources of electricity are either from cogeneration or public utilities and it was calculated the least cost to supply electricity and heat to the hospital. The hospital is using heat for their operation and that made the application for the cogeneration to be attractive and feasible. In this study, we will develop the LP model and will show the results and the time schedule for the cogeneration. This developed LP model can be used and run to any cogeneration application with little modification.
A new method is proposed for the optimization of biochemical systems. The method, based on the separation of the stoichiometric and kinetic aspects of the system, follows the general approach used in the previously pr...
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A new method is proposed for the optimization of biochemical systems. The method, based on the separation of the stoichiometric and kinetic aspects of the system, follows the general approach used in the previously presented indirect optimization method (IOM) developed within biochemical systems theory. It is called GMA-IOM because it makes use of the generalized mass action (GMA) as the model system representation form. The GMA representation avoids flux aggregation and thus prevents possible stoichiometric errors. The optimization of a system is used to illustrate and compare the features, advantages and shortcomings of both versions of the IOM method as a general strategy for designing improved microbial strains of biotechnological interest. Special attention has been paid to practical problems for the actual implementation of the new proposed strategy, such as the total protein content of the engineered strain or the deviation from the original steady state and its influence on cell viability. (C) 2003 Elsevier Science Inc. All rights reserved.
Traditional sensitivity analysis in linear programming usually focuses on variations of one coefficient or term at a time. The tolerance approach was proposed to provide a decision maker with an effective and easy-to-...
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Traditional sensitivity analysis in linear programming usually focuses on variations of one coefficient or term at a time. The tolerance approach was proposed to provide a decision maker with an effective and easy-to-use method to summarize the effects of simultaneous and independent changes in selected parameters. In particular, for variations of the objective function coefficients, the approach gives a maximum-tolerance percentage within which selected coefficients may vary from their estimated values (within a priori limits) while still retaining the same optimal basic feasible solution. Although an optimal solution may cease being optimal for variations beyond the maximum-tolerance percentage, it may still be close to optimal. Herein we characterize the potential loss of optimality for variations beyond the maximum-tolerance percentage as a maximum-regret function. We consider theoretical properties of this function and propose a method to compute a relevant portion of it.
A sequential solution procedure to stochastic linear programming problems with 0–1 variables is described. The procedure is based on multiple stages of experimental optimization, when the results from a given stage m...
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A sequential solution procedure to stochastic linear programming problems with 0–1 variables is described. The procedure is based on multiple stages of experimental optimization, when the results from a given stage may determine part of the final solution set. By establishing criteria for reducing the problem size, while proceeding from one stage to another, one can have some control on the total budget for computer time needed.
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