A method of unit commitment in an electric utility system is discussed. The need for production costing as a part of optimum unit commitment is emphasized. A lambda separable programming technique is presented which a...
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A method of unit commitment in an electric utility system is discussed. The need for production costing as a part of optimum unit commitment is emphasized. A lambda separable programming technique is presented which allows the use of separation variables, lambda , to represent the fixed charge component as well as the nonlinear component of the objective function. The method is tested using a hypothetical system of generating units including nuclear, fossil, hydro, thermal peaking and pumped storage. The daily unit commitment schedule for these generators is found using the optimization technique presented. Also, the suitability of this type of algorithm to study the inter-fuel substitutability is discussed.
In this article, a novel approach is presented for the simulation of failure processes of structures in extreme events such as earthquakes. Models of material behavior are represented using a Generalized Standard Mate...
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In this article, a novel approach is presented for the simulation of failure processes of structures in extreme events such as earthquakes. Models of material behavior are represented using a Generalized Standard Material framework, and the governing equations are formulated using a Mixed Lagrangian Formalism. This results in the failure simulation computations being cast as different types of mathematical programs. Efficient strategies are devised to solve these mathematical programs in the context of large-scale problems of practical interest. Numerical examples are presented to illustrate the approach.
Two mathematical programming procedures for treating nonlinear problems involving mixed variables are presented. One involves a relatively simple concept. First an optimum is located treating all variables as continuo...
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Two mathematical programming procedures for treating nonlinear problems involving mixed variables are presented. One involves a relatively simple concept. First an optimum is located treating all variables as continuous. Adjacent discrete points are then evaluated in order of increasing distance from the all-continuous optimum, each evaluation requiring an optimization of the continuous variables, if any, until a satisfactory design is found. The other method utilizes an optimal discrete search to locate the optimum. These procedures are applied to the minimum weight design of stiffened, cylindrical shells where they prove to be effective.
An integer mathematical program that is useful in the context of manufacturing system design and evaluation is presented. In particular, the design of adaptable/programmable assembly systems is considered, although th...
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An integer mathematical program that is useful in the context of manufacturing system design and evaluation is presented. In particular, the design of adaptable/programmable assembly systems is considered, although the method presented is equally applicable to flexible manufacturing systems. The system design decision entails the selection of assembly stations to include in the system, plus the assignment of assembly tasks to these stations.
The place of mathematical programming methods in structural optimization is now well established after some 15 years of research and development. Numerous design applications have been reported for aircraft and ship c...
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The place of mathematical programming methods in structural optimization is now well established after some 15 years of research and development. Numerous design applications have been reported for aircraft and ship components, steel and concrete buildings and bridges and other civil and mechanical systems. This paper reviews these developments and attempts to establish a general framework for the type of design problems that can be formulated and solved. Design problems are divided into three categories for application including element design, system design and discrete decisions. The techniques described include unconstrainted minimization, linear programming based methods and dynamic programming. A discussion is presented of the interrelationships of the mathematical programming approach to optimization with both the theoretical optimality criteria and the direct design or structural index method.
Efficient research method represents the primary content of hydrogen networks optimization. In the view of methodology, as pinch analysis is not specialized in precisely matching a network and mathematical programming...
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This paper presents two solution techniques for constructing a minimal weighted tree connecting a fixed set of n terminal vertices while allowing extra vertices to be added to the tree to reduce the cost or length of ...
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ISBN:
(纸本)9780897916585
This paper presents two solution techniques for constructing a minimal weighted tree connecting a fixed set of n terminal vertices while allowing extra vertices to be added to the tree to reduce the cost or length of the connection. The problem discussed is a variation of the Steiner tree problem that reduces to the classical Steiner tree problem for many special cases. It is also a generalization of the planar Steiner tree problem. The algorithms seek to determine the location of points (Steiner points) such that each terminal vertex is connected to a Steiner point. The Steiner points are then connected to each other and the algorithm minimizes total cost. We present a solution to this problem for a modest number of terminal vertices using a mathematical programming approach and combinatorics. This approach is guaranteed to find the optimal points (Steiner points) that minimize the total cost. We also construct an algorithm that iteratively combines the early use of mathematical programming along with a hybrid genetic algorithm. The solution techniques were tested extensively on small planar problems but were also applied to large problems in two-, three- and five-dimensional space. The genetic algorithm is used to determine the appropriate structure of a solution and the mathematical programming algorithm determines the optimal location of Steiner points for each structure. The structure determines the assignment of terminal vertices to the Steiner points. The mathematical programming algorithm is the optimization part of a very powerful evaluation function for the genetic algorithm approach to the problem.
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