Ranch linear programming : Users Manual by United States. Bureau of Land Management; published by [washington, D.C.?] : U.S. Dept. of the Interior, Bureau of Land Management
Ranch linear programming : Users Manual by United States. Bureau of Land Management; published by [washington, D.C.?] : U.S. Dept. of the Interior, Bureau of Land Management
Conditions are established under which the optimal control of processes having both absolutely continuous and singular (with respect to time) controls are equivalent to linear programs over a space of measures on the ...
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This paper is devoted to a study of infinite horizon optimal control problems with time discounting and time averaging criteria in discrete time. We establish that these problems are related to certain infinite-dimens...
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A Model for linear programming Optimization of I/O-Bound Programs by Gold, David E; University of Illinois at Urbana-Champaign. Dept. of Computer Science; published by Urbana, Ill. : Dept. of Computer Science, Univers...
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A Model for linear programming Optimization of I/O-Bound Programs by Gold, David E; University of Illinois at Urbana-Champaign. Dept. of Computer Science; published by Urbana, Ill. : Dept. of Computer Science, University of Illinois at Urbana-Champaign
This paper addresses an improved approach to dynamic output-feedback control design of positive systems. By using a simple matrix transformation technique that a matrix can be described via the sum of all its column v...
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ISBN:
(纸本)9781509045839
This paper addresses an improved approach to dynamic output-feedback control design of positive systems. By using a simple matrix transformation technique that a matrix can be described via the sum of all its column vectors, a linear programming based controller design approach for nominal positive systems is proposed. Subsequently, the proposed approach is extended to interval positive systems. The present approach contains three merits that are different from existing ones: (i) it employs the linear programming technique while existing ones adopt the linear matrix inequalities technique;(ii) it provides a more general controller without negative restriction while existing ones may require the negativity of the controller gain matrix;(iii) it is easy to be extended to other issues of positive systems while this may not always be possible for existing approaches. Finally, a numerical example is provided to verify the effectiveness of the proposed design.
Multi-parametric programming has proven to be an invaluable tool for optimisation under uncertainty. Despite the theoretical developments in this area, the ability to handle uncertain parameters on the left-hand side ...
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Multi-parametric programming has proven to be an invaluable tool for optimisation under uncertainty. Despite the theoretical developments in this area, the ability to handle uncertain parameters on the left-hand side remains limited and as a result, hybrid, or approximate solution strategies have been proposed in the literature. In this work, a new algorithm is introduced for the exact solution of multi-parametric linear programming problems with simultaneous variations in the objective function's coefficients, the right-hand side and the left-hand side of the constraints. The proposed methodology is based on the analytical solution of the system of equations derived from the first order Karush-Kuhn-Tucker conditions for general linear programming problems using symbolic manipulation. Emphasis is given on the ability of the proposed methodology to handle efficiently the LHS uncertainty by computing exactly the corresponding nonconvex critical regions while numerical studies underline further the advantages of the proposed methodology, when compared to existing algorithms. (C) 2017 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.
This study compares two models of the production-inventory system -optimal control and linear programming. We derived the optimality conditions of optimal control model and formulated the linear programming model. A n...
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This study compares two models of the production-inventory system -optimal control and linear programming. We derived the optimality conditions of optimal control model and formulated the linear programming model. A new method to determine the theoretical solution of the boundary value problem has been suggested. Our numerical results suggest that control on the inventory level was realized at the end of the planning period, depending on the optimal control model, while in the linear programming model, it was realized from the beginning of the planning period. Also, the method to determine the theoretical solution of the boundary value problem has proven to be efficient.
This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis ...
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This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer programming has been carried out. The general formulation of these models has been presented and applied to a real case study micro-grid installed in Somalia. The case study is an islanded micro-grid supplying the city of Garowe by means of a hybrid power plant, consisting of diesel generators, photovoltaic systems and batteries. In both models the optimization is based on load demand and renewable energy production forecast. The optimized control of the battery state of charge, of the spinning reserve and diesel generators allows harvesting as much renewable power as possible or to minimize the use of fossil fuels in energy production.
Supplier selection is a critical process in sustainable supply chain management. Increased pressure from stakeholders has forced companies to search for methodologies that help in arriving at intelligent supplier sele...
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Supplier selection is a critical process in sustainable supply chain management. Increased pressure from stakeholders has forced companies to search for methodologies that help in arriving at intelligent supplier selection decisions. This is a unique study as it illustrates how to optimise orders among various suppliers while taking into consideration all three dimensions of sustainability -economic, social, and environmental. Previous studies have mostly relied on simple ranking of suppliers on the basis of past performance for selection. Those studies that did emphasise on optimisation of orders among suppliers, did not consider all three dimensions of sustainability. To establish an improved sustainable supply chain, this study uses integrated fuzzy AHP and fuzzy multi-objective linear programming approach for order allocation among suppliers. fuzzy AHP has been used for weighing various factors such as quality, lead time, cost, energy use, waste minimisation, emission, and social contribution, and weights of the factors have been considered for developing linear programming. Demand has been taken as a fuzzy variable in this model. The case of an Indian automobile company has been taken as illustration.
This paper presents a new technique for online set membership parameter estimation of linear regression models affected by unknown-but-bounded noise. An orthotopic approximation of the set of feasible parameters is up...
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This paper presents a new technique for online set membership parameter estimation of linear regression models affected by unknown-but-bounded noise. An orthotopic approximation of the set of feasible parameters is updated at each time step. The proposed technique relies on the solution of a suitable linear program, whenever a new measurement leads to a reduction of the approximating orthotope. The key idea for preventing the size of the linear programs from steadily increasing is to propagate only the binding constraints of these optimization problems. Numerical studies show that the new approach outperforms existing recursive set approximation techniques, while keeping the required computational burden within the same order of magnitude. Copyright (c) 2016 John Wiley & Sons, Ltd.
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