Almost all dynamic production systems are subject to lagged productive effects, which are an often-ignored latent source of interference in the efficiency measuring process. Existing data envelopment analysis (DEA) ap...
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Almost all dynamic production systems are subject to lagged productive effects, which are an often-ignored latent source of interference in the efficiency measuring process. Existing data envelopment analysis (DEA) approaches rely on a static production environment. They can easily lead to biased evaluation results due to the erroneous assumption. To tackle this issue, this paper develops a dynamic DEA model that allows intertemporal effects in efficiency measuring. Specifically, the dynamic DEA model incorporates dynamic factors via a linear parametric formulation. Our model can be applied in place of static DEA models to a wide range of applications, such as analyzing longitudinal firm performance and productivity changes. As for the empirical efficiencies, we demonstrate how the lag parameters in the dynamic model can be estimated by the panel vector autoregressive model (PVAR). We use our methodology to evaluate advertising efficiencies of several major automobile and pharmaceutical firms in North America. The result shows that using static DEA in dynamic production can lead to both rank reversals and changes in efficiency scores. (C) 2009 Elsevier B.V. All rights reserved.
Data envelopment analysis ( DEA) is designed to maximize the efficiency of a given decision-making unit ( DMU) relative to all other DMUs by the choice of a set of input and output weights. One strength of the origina...
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Data envelopment analysis ( DEA) is designed to maximize the efficiency of a given decision-making unit ( DMU) relative to all other DMUs by the choice of a set of input and output weights. One strength of the original models is the absence of any need of a priori information about the process of transforming inputs into outputs. However, in the practical application of DEA models, this strength has also become a weakness. Incorporation of process knowledge is more a norm than an exception in practice, and typically involves placing constraints on the input and/or output weights. New DEA formulations have evolved to address this issue. However, existing formulations for weight restrictions may underestimate relative efficiency or even render a problem infeasible. A new model formulation is introduced to address this issue. This formulation represents a significant improvement over existing DEA models by providing a generalized, comprehensive treatment for weight restrictions.
This work presents a compartmental model for delivery of drugs under anesthesia and an advanced model based control algorithm for insulin delivery for Type I diabetes. The model for anesthesia involves choice of three...
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This work presents a compartmental model for delivery of drugs under anesthesia and an advanced model based control algorithm for insulin delivery for Type I diabetes. The model for anesthesia involves choice of three drugs isoflurane, dopamine and sodium nitroprusside, which allows simultaneous regulation of mean arterial pressure and unconsciousness of the patients. A number of dynamic simulations are carried out to validate the model. For Type I diabetes, a parametric programming approach is used to obtain the optimal insulin infusion rate as an explicit function of the state of the patient and the regions in the space of the state of patient where these functions are valid. These explicit functions allow the implementation of blood glucose control on a simple computational software and hardware platform. (c) 2005 Elsevier Ltd. All rights reserved.
In the paper a definition of the optimal solution of the transportation problem with fuzzy cost coefficients as well as an algorithm determining this solution are proposed.
In the paper a definition of the optimal solution of the transportation problem with fuzzy cost coefficients as well as an algorithm determining this solution are proposed.
This paper presents a new algorithm for identifying all supported non-dominated vectors (or outcomes) in the objective space, as well as the corresponding efficient solutions in the decision space, for multiobjective ...
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This paper presents a new algorithm for identifying all supported non-dominated vectors (or outcomes) in the objective space, as well as the corresponding efficient solutions in the decision space, for multiobjective integer network flow problems. Identifying the set of supported non-dominated vectors is of the utmost importance for obtaining a first approximation of the whole set of non-dominated vectors. This approximation is crucial, for example, in two-phase methods that first compute the supported non-dominated vectors and then the unsupported non-dominated ones. Our approach is based on a negative-cycle algorithm used in single objective minimum cost flow problems, applied to a sequence of parametric problems. The proposed approach uses the connectedness property of the set of supported non-dominated vectors/efficient solutions to find all integer solutions in maximal non-dominated/efficient facets. (C) 2008 Elsevier B.V. All rights reserved.
An efficient optimization method is proposed for linear-quadratic optimal control problems with state and control constraints. We describe an active set solver that uses Riccati recursions to solve a sequence of equal...
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An efficient optimization method is proposed for linear-quadratic optimal control problems with state and control constraints. We describe an active set solver that uses Riccati recursions to solve a sequence of equality-constrained subproblems. The main contribution is a homotopy method based on relaxing inequality constraints. This overcomes known shortcomings of Riccati active set solvers relating to their initialization and their application to problems with time-varying model data. It can be used exclusively or in combination with established Riccati active set solvers. The efficiency is demonstrated in numerical examples against state-of-the-art quadratic programming solvers.
Let P(lambda, mu) = min {f1(x) + lambda-f2(x) + mu-f3(x)\x is-an-element-of D}. We present a method that constructs P(lambda, mu) for all lambda, mu in a given interval in O(f.T(n) + f2) time, where f denotes the numb...
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Let P(lambda, mu) = min {f1(x) + lambda-f2(x) + mu-f3(x)\x is-an-element-of D}. We present a method that constructs P(lambda, mu) for all lambda, mu in a given interval in O(f.T(n) + f2) time, where f denotes the number of faces of P(lambda, mu) in the interval and T(n) denotes the time needed to solve the associated nonparametric problem.
This paper describes developments in parametric mixed integer optimization for the solution of process synthesis and material design problems in the presence of uncertainty. For mixed integer linear models (MILP), suc...
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This paper describes developments in parametric mixed integer optimization for the solution of process synthesis and material design problems in the presence of uncertainty. For mixed integer linear models (MILP), such as the ones that arise in material design problems, a multiparametric MILP approach is described and illustrated with an example, whereas for mixed integer nonlinear convex models (MINLP), a parametric MINLP algorithm is presented and applied to a process synthesis example problem.
The purpose of this paper is to give a geometrical answer to the question to the strong second order sufficiency conditions hold at any local minimum point for almost all nonlinear programs? Our idea is to reduce the ...
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The purpose of this paper is to give a geometrical answer to the question to the strong second order sufficiency conditions hold at any local minimum point for almost all nonlinear programs? Our idea is to reduce the nonlinear programming problem to a finite family of “well-behaved” nonlinear programs by perturbing the objective function in a linear fashion and perturbing the right-hand side of the constraints by adding a constant. Each of the “well-behaved” nonlinear programs will consist of minimizing a Morse function on a manifold with boundary, where the Morse function has no critical points on the boundary.
The concept of opposition-based learning (OBL) was first introduced as a scheme for machine intelligence. In a very short period of time, some other variants of opposite numbers were proposed and opposition was applie...
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The concept of opposition-based learning (OBL) was first introduced as a scheme for machine intelligence. In a very short period of time, some other variants of opposite numbers were proposed and opposition was applied to various research areas. In metaheuristic optimization algorithms, the main idea behind applying opposite numbers is the simultaneous consideration of a candidate solution and its corresponding opposite candidate in order to achieve a better approximation for the current solution. This paper proposes an opposition-based metaheuristic optimization algorithm (OBA) and a new and efficient opposition named comprehensive opposition (CO) as its main operator. In this paper it is mathematically proven that CO not only increases the chance of achieving better approximations for the solution but also guarantees the global convergence of OBA. The efficiency of the proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various function optimizations. (C) 2014 Elsevier Ltd. All rights reserved.
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