Capital rationing is a real decision problem in government, yet it has never been seriously addressed in the literature on public budgeting. Conventional methods such as NPV or IRR that frequently appear in the discus...
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Capital rationing is a real decision problem in government, yet it has never been seriously addressed in the literature on public budgeting. Conventional methods such as NPV or IRR that frequently appear in the discussion on the subject are limited in their scope. Alternative methods such as mathematical programming, which can substantially overcome some of the limitations of the conventional methods, have been extensively used in the private sector, but their applications have been few and far between in government. This article illustrates the potential these models hold for the capital rationing problem in government.
Comments on a study that described the association between goal programming (GP), compromise programming (CP) and reference point method (RPM) for operational research. Background on the GP, CP and RPM models; Discuss...
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Comments on a study that described the association between goal programming (GP), compromise programming (CP) and reference point method (RPM) for operational research. Background on the GP, CP and RPM models; Discussion on the balanced solutions in GP, CP and RPM; Details on a numerical example that illustrated the balanced equations of the GP, CP and RPM models; Reply to the comments.
The aim of this paper is to make comparative analysis of the main paradigms of programming and to justify their integration, in creating highly intellectual program compositions. Also, the experience accumulated in de...
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The aim of this paper is to make comparative analysis of the main paradigms of programming and to justify their integration, in creating highly intellectual program compositions. Also, the experience accumulated in developing a system of algebraic programming has been described. Besides, on the basis of the system mentioned above, applied intellectual solvers of problems were developed, using integration of paradigms of various types.
Dynamic portfolio choice is an important problem in finance, but the optimal strategy analysis is difficult when considering multiple stochastic volatility variables such as the stock price, interest rate, and income....
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Dynamic portfolio choice is an important problem in finance, but the optimal strategy analysis is difficult when considering multiple stochastic volatility variables such as the stock price, interest rate, and income. Besides, recent research in experimental economics indicates that the agent shows limited attention, considering only the variables with high fluctuations but ignoring those with small ones. By extending the sparse max method, we propose an approach to solve dynamic programming problem with small stochastic volatility and the agent's bounded rationality. This approach considers the agent's behavioral factors and avoids effectively the "Curse of Dimensionality" in a dynamic programming problem with more than a few state variables. We then apply it to Merton dynamic portfolio choice model with stochastic volatility and get a tractable solution. Finally, the numerical analysis shows that the bounded rational agent may pay no attention to the varying equity premium and interest rate with small variance.
An interactive approach for solving multi-objective programming problems (MOPP) is described, when the decision maker (DM) preferences have fuzzy membership values. The suggested approach avoids the imprecise preferen...
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An interactive approach for solving multi-objective programming problems (MOPP) is described, when the decision maker (DM) preferences have fuzzy membership values. The suggested approach avoids the imprecise preferences in the decision making Judgements. The main idea of this approach depends on invoking both analytic hierarchy process (AHP) and fuzzy analysis within the solution of MOPP.
An integer programming algorithm is described for assigning tasks on an assembly line to work stations in such a way that the number of work stations is minimal for the rate of production desired. The procedure insure...
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An integer programming algorithm is described for assigning tasks on an assembly line to work stations in such a way that the number of work stations is minimal for the rate of production desired. The procedure insures that no task is assigned to a work station before all tasks which technologically must be performed before it have been assigned (precedence restrictions are not violated), and that the total time required at each work station performing the tasks assigned to it does not exceed the time available (cycle time restrictions are not violated). The procedure is based on a systematic evaluation (enumeration) of all possible task assignments to work stations. Significant portions of the enumeration process are performed implicitly, however, by utilizing the tests described which are based on the specific structure of the line balancing problem.
A duality theory for convex functionals is developed. As an illustration, an entropy maximizing model associated with information science is considered.
A duality theory for convex functionals is developed. As an illustration, an entropy maximizing model associated with information science is considered.
A large number of real-world problems may be characterized via a multiobjective integer mathematical programming model. However, the solution to truly large scale problems of such a type has been a difficult task. The...
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A large number of real-world problems may be characterized via a multiobjective integer mathematical programming model. However, the solution to truly large scale problems of such a type has been a difficult task. The paper presents a hybrid approach, combining generalized goal programming and generalized networks, for the modeling of such problems. Once such a model has been developed, it may then be possible to employ the solution procedures of generalized networks to efficiently obtain a solution - particularly if the resultant hybrid model is, fundamentally, a multiobjective generalized network.
The integer programming literature contains many algorithms for solving all-integer programming problems but, in general, existing algorithms are less than satisfactory even in solving problems of modest size. This pa...
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The integer programming literature contains many algorithms for solving all-integer programming problems but, in general, existing algorithms are less than satisfactory even in solving problems of modest size. This paper presents a new technique for solving the all-integer, integer programming problem. This algorithm is a hybrid (i. e. , primal-dual) cutting-plane method which alternates between a primal-feasible stage related to R. D. Young's simplified primal algorithm, and a dual-infeasible stage related to R. E. Gomory's dual all-integer algorithm. The results of computational testing are presented.
Due to the opening of the energy market and agreements for the reduction of pollution emissions, the use of microgrids attracts more attention in the scientific community, but electricity administration has new challe...
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ISBN:
(纸本)9781538659359
Due to the opening of the energy market and agreements for the reduction of pollution emissions, the use of microgrids attracts more attention in the scientific community, but electricity administration has new challenges. This paper considers distributed generation as a main part to design a microgrid and the resources management is defined in a period through proposed dynamic economic dispatch approach. The inputs are obtained by the model predictive control algorithm considering variations of both pattern of consumption and generation systems capacity, including conventional and renewable energy sources. Furthermore, the proposed approach considers a stimulus program to customers involving a demand restriction and the costs of regeneration of the pollutants produced by conventional generation systems. The dispatch strategy seeks to reduce to the minimum the fuel cost of conventional generators, the energy transactions, the regeneration of polluted emissions and, finally, includes the benefit in electricity demand reduction satisfying all restrictions through mathematical programming strategy. The results exhibit the proposed approach effectiveness through a study case under different considerations.
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