In this paper, we use a robust policy concept that is an extension to nonlinear systems of the mean-variance approach for linear models. In the linear case, the approach reduces to the minimization of a convex combina...
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In this paper, we use a robust policy concept that is an extension to nonlinear systems of the mean-variance approach for linear models. In the linear case, the approach reduces to the minimization of a convex combination of the mean and variance of the stochastic problem. We also discuss a method for computing expectations of nonlinear systems based on evaluating the bias from certainty equivalence using Monte Carlo simulations. The method computes robust strategies. If desired, these can be in the form of simple decision rules, as aggregate parametrisations of full feedback strategies. The parameters are determined to minimize a convex combination of the original objective function and a sensitivity (variance) expression. The latter can either be chosen as the sensitivity (variance) of the original objective function or the sensitivity (variance) of the individual endogenous variables.
Recently, O(n(2)) active set methods have been presented for minimizing the parametric quadratic functions (1/2)x' Dx - a'x + lambda/gamma'x - c\ and (1/2)x' Dx - a'x + (lambda/2)(gamma'x - c)(...
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Recently, O(n(2)) active set methods have been presented for minimizing the parametric quadratic functions (1/2)x' Dx - a'x + lambda/gamma'x - c\ and (1/2)x' Dx - a'x + (lambda/2)(gamma'x - c)(2), respectively, subject to l less than or equal to x less than or equal to b, for all nonnegative values of the parameter lambda. Here, D is a positive diagonal n x n matrix, gamma and a are arbitrary n-vectors, c is an arbitrary scalar;l and b are arbitrary n-vectors such that l less than or equal to b. In this paper, we show that each one of these algorithms may be used to simultaneously solve both parametric programs with no additional computational cost.
This paper presents a fault-tolerant control approach for actuators failures in multirotor Unmanned Aerial Vehicles UAVs. The strategy is applied on an hexarotor UAV. First, the hexarotor model is derived by means of ...
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In this paper, we propose a polynomial-time algorithm for fractional assignment problems. The fractional assignment problem is interpreted as follows. Let G=(I,J,E) be a bipartite graph where I and J are vertex sets a...
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In this paper, we propose a polynomial-time algorithm for fractional assignment problems. The fractional assignment problem is interpreted as follows. Let G=(I,J,E) be a bipartite graph where I and J are vertex sets and E subset of or equal to I x J is an edge set. We call an edge subset X( subset of or equal to E) an assignment if every vertex is incident to exactly one edge from X. Given an integer weight c(ij) and a positive integer weight d(ij) for every edge (i,j) is an element of E, the fractional assignment problem finds an assignment X( subset of or equal to E) such that the ratio (Sigma(i,j) is an element of X) C-ij)/(Sigma((i,j) is an element of X d(ij)) is minimized. Some algorithms were developed for the fractional assignment problem. Recently, Radzik (1992) showed that an algorithm which is based on the parametric approach and Newton's method is the fastest one among them. Actually, it solves the fractional assignment problem in O(root nm log(2)(nCD)/(1 + log log(nCD)- log log(nD))) time where \I\ = \J\ = n, \E\ = m, C = max {1, max {\c(ij)\ :(i,j) is an element of E}} and D = max {d(ij):(i,j) is an element of E} + 1. Our algorithm developed in this paper is also based on the parametric approach, but it is combined with the approximate binary search method. The time complexity of our algorithm is O(root nm log D log(nCD)) time, and provides with a better time bound than the above algorithm.
Safety stock levels of reparable spare parts in a hierarchical inventory structure are determined relative to the marginal improvement to end item availability provided by each stock level. This research is focused on...
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Safety stock levels of reparable spare parts in a hierarchical inventory structure are determined relative to the marginal improvement to end item availability provided by each stock level. This research is focused on the United States Air Force (USAF) hierarchical inventory and repair system as modeled by the Multi-Echelon-Technique-for-Recoverable-Item-Control (METRIC) methodology. A piecewise linear modeling framework is introduced based on output from the Aircraft Availability Model (AAM), a model that is part of the METRIC family of models. The underlying nature of the piecewise linear formulation provides integer stock levels with continuous decision variables. This model provides the highest attainable end-item availability relative to an inventory of reparable spare parts. Additionally, the model provides a robust platform for the conduct of extensive post-optimality analysis. While this research focuses on the METRIC family of models, the methodology developed may be applied to any hierarchical inventory of reparable spares that conforms to the model's underlying mathematical structure.
This paper deals with two bi-objective models arising from competitive location problems. The first model simultaneously intends to maximize market share and to minimize cost. The second one aims to maximize both prof...
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This paper deals with two bi-objective models arising from competitive location problems. The first model simultaneously intends to maximize market share and to minimize cost. The second one aims to maximize both profit and the profit margin. We study some of the related properties of the models, examine relations between the models and a single objective parametric integer programming problem, and then show how both bi-objective location problems can be solved through the use of a single objective parametric integer program. Based on this, we propose two methods of obtaining a set of efficient solutions to the problems of fundamental approach. Finally, a numerical example is presented to illustrate the solution techniques.
[Auto Generated] Chapter 1: Concepts and Examples . 1 Distillate (heating oil) Blending. 26 Fuel Blending . 27 1.1: Example of Production Capacity 3.3: The Objective Function Allocation . 2 and Constraints 27 1. 2: Ex...
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[Auto Generated] Chapter 1: Concepts and Examples . 1 Distillate (heating oil) Blending. 26 Fuel Blending . 27 1.1: Example of Production Capacity 3.3: The Objective Function Allocation . 2 and Constraints 27 1. 2: Example of Feed Blending 7 Objective Function . 27 1. 3: Example of Investment Policy 9 Material-Balance Constraints 27 1. 4: Characteristics of a Linear Pipe-Still Constraints. 27 programming Application. 13 Cat-Cracker Constraint 27 1. 5: Typical Linear programming Fuel Oil-Blending
This paper proposes a novel approach for time-cost trade-off analysis of a project network in fuzzy environments. Different from the results of previous studies, in this paper the membership function of the fuzzy mini...
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This paper proposes a novel approach for time-cost trade-off analysis of a project network in fuzzy environments. Different from the results of previous studies, in this paper the membership function of the fuzzy minimum total crash cost is constructed based on Zadeh's extension principle and fuzzy solutions are provided. A pair of two-level mathematical programs parameterized by possibility level alpha is formulated to calculate the lower and upper bounds of the fuzzy minimum total crash cost at alpha. By enumerating different values of alpha, the membership function of the fuzzy minimum total crash cost is constructed, and the corresponding optimal activity time for each activity is also obtained at the same time. An example of time-cost trade-off problem with several fuzzy parameters is solved successfully to demonstrate the validity of the proposed approach. Since the minimum total crash cost is expressed by a membership function rather than by a crisp value, the fuzziness of parameters is conserved completely, and more information is provided for time-cost trade-off analysis in project management. The proposed approach also can be applied to time-cost trade-off problems with other characteristics. (C) 2011 Elsevier B.V. All rights reserved.
Although parametric optimization with uncertainties on the objective function (OF) or on the so-called "right-hand-side" (RHS) of the constraints has been addressed successfully in recent papers, very little...
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Although parametric optimization with uncertainties on the objective function (OF) or on the so-called "right-hand-side" (RHS) of the constraints has been addressed successfully in recent papers, very little work exists on the same with uncertainties on the left-hand-side (LHS) of the constraints or in the coefficients of the constraint matrix. The goal of this work has been to develop a systematic method to solve such parametric optimization problems. This is a very complex problem and we have begun with the simplest of optimization problems, namely the linear programming problem with a single parameter on the LHS. This study reviews the available work on parametric optimization, describes the challenges and issues specific to LHS parametric linear programming (LHS-pLP), and presents a solution algorithm using some classic results from matrix algebra. (C) 2013 Elsevier Ltd. All rights reserved.
In this paper we study local behavior of optimal solutions of parametric programs in Banach spaces. A general approach to approximation of the original problem by a simpler one is outlined. Under various regularity co...
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In this paper we study local behavior of optimal solutions of parametric programs in Banach spaces. A general approach to approximation of the original problem by a simpler one is outlined. Under various regularity conditions, Lipschitz continuity and directional differentiability properties of the optimal solutions are derived.
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