A steepest descent multiplier adjustment method for the generalized assignment problem
Conference
·
OSTI ID:36186
Multiplier adjustment methods (MAMs) have been used to solve the Lagrangian dual of the generalized assignment problem. We improve the traditional MAMs so that a steepest descent improvement is guaranteed at each iteration. The steepest descent is achieved by incorporating a post-optimality analysis for the 0-1 knapsack subproblems based on a dynamic programming formulation. The new approach also eliminates all intermediate knapsack solutions required by the traditional MAMs for calculating a step length. A branch-and-bound algorithm that incorporates the new MAM as a bounding tool is described and computational results obtained with the algorithm are reported.
- OSTI ID:
- 36186
- Report Number(s):
- CONF-9408161--
- Country of Publication:
- United States
- Language:
- English
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