First, this paper presents the results of experiments with algorithmic techniques for efficiently solving medium and largescale linear and mixed integer programming problems. The techniques presented here are either ...
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First, this paper presents the results of experiments with algorithmic techniques for efficiently solving medium and largescale linear and mixed integer programming problems. The techniques presented here are either original or recent. The solution of a great number of problems has shown that efficient problem solving requires automatic adaptation of algorithmic techniques upon problem characteristics. We show when a given technique should be used for a particular problem. The last part of this paper describes an attempt to provide a powerful mathematical programming language, allowing an easy programming of specific studies on medium-size models such as the recursive use of LP or the build-up of algorithms based on the simplex method. All these features have been implemented in the IBM Mathematical programming System, MPSX/370, and its feature MIP/370. Extensive numerical results and comparisons on real-life problems are provided and commented upon.
This paper provides an overview and a list of references on the use of Evolutionary Algorithms (EA) in Power Systems and related fields. As didactic examples, the paper presents two applications of EA for two differen...
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This paper provides an overview and a list of references on the use of Evolutionary Algorithms (EA) in Power Systems and related fields. As didactic examples, the paper presents two applications of EA for two different problems in Power Systems. (C) 1997 Elsevier Science Ltd.
In this paper, largescale generalized assignment problems are considered. We propose an approximation technique, where the original (full size) programming problem is aggregated into a problem of more moderate size. ...
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In this paper, largescale generalized assignment problems are considered. We propose an approximation technique, where the original (full size) programming problem is aggregated into a problem of more moderate size. Feasible solutions are generated through a disaggregation approach coupled with some complementing heuristics. An upper bound on the loss of accuracy is also calculated. A small test problem is solved in order to illustrate the idea and computational results from two larger problems is presented.
A new algorithm is presented using a logarithmic barrier function decomposition for the solution of the large-scale multicommodity network flow problem. Placing the com- plicating joint capacity constraints of the mul...
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A new algorithm is presented using a logarithmic barrier function decomposition for the solution of the large-scale multicommodity network flow problem. Placing the com- plicating joint capacity constraints of the multicommodity network flow problem into a logarithmic barrier term of the objective function creates a nonlinear mathematical program with linear network flow constraints. Using the technique of restricted simplicial decomposition, we generate a sequence of extreme points by solving independent pure network problems for each commodity in a linear subproblem and optimize a nonlinear master problem over the convex hull of a fixed number of retained extreme points and the previous master problem solution. Computational results on a network with 3,300 nodes and 10,400 arcs are reported for four, ten and 100 commodities.
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