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MINIMAX RESOURCE-ALLOCATION PROBLEMS - OPTIMIZATION AND PARAMETRIC ANALYSIS
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DOI：10.1016/0377-2217(92)90335-7.png)
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Abstract
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We consider a linear minimax resource allocation problem with single-variable terms in the objective function and multiple knapsack-type resource constraints. All variables are continuous and nonnegative. Efficient algorithms for such large-scale problems have been developed by Luss and Smith and by Tang. This paper describes an enhanced algorithm that provides a more efficient search for the optimal solution. Further, we develop post-optimization schemes and parametric analysis that are employed once an optimal solution for the original minimax problem is obtained. Post-optimization provides a perturbed optimal solution under a specified change in the data, whereas parametric analysis provides a continuum of optimal solutions when some data elements are changed over a given interval.
Keywords:
LINEAR PROGRAMMING
LARGE-SCALE OPTIMIZATION
RESOURCE ALLOCATION
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