On the variable-depth-search heuristic for the linear-cost generalized assignment problem

The classical assignment problem seeks to determine a mapping between a set of assignees and a set of assignments. The linear cost assignment problem (LCGAP), as a generalized model, incorporates the relative workloads between assignees and assignments. Although LCGAP is computationally intractable,...

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Bibliographic Details
Published in:International journal of computer mathematics Vol. 77; no. 4; pp. 535 - 544
Main Authors: Lin, Bertrand M.T., Huang, Y.S., Yu, H.K.
Format: Journal Article
Language:English
Published: Abingdon Gordon and Breach Science Publishers 01.01.2001
Taylor and Francis
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Approximate solutions
Assignment problem
Computer science; control theory; systems
Exact sciences and technology
F 2.1
Flows in networks. Combinatorial problems
Heuristic algorithm
Operational research and scientific management
Operational research. Management science
Personnel management
Theoretical computing
Variable depth search heuristic method
Heuristic algorithm
Resource allocation
Heuristic method
Linear cost assignement problem
Information retrieval
Cost analysis
Personnel
Approximate solution
ISSN:0020-7160, 1029-0265
Online Access:Get full text
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Summary:The classical assignment problem seeks to determine a mapping between a set of assignees and a set of assignments. The linear cost assignment problem (LCGAP), as a generalized model, incorporates the relative workloads between assignees and assignments. Although LCGAP is computationally intractable, it has been extensively studied because of its practical implications in many real world situations. Variable-depth-search heuristic (VDSH) method is one of the solution methods that have been developed to produce quality near-optimal solutions to LCGAP. The main structure of VDSH consists of two basic operations: reassign and swap. In this paper, we make further observations on this effective heuristic method through a series of computational experiments. The numerical results statistically evince that different combina-tions of these two operations will result in solutions of different quality levels. These findings are expected to have similar implications to search heuristics for other optimization problems.
ISSN:0020-7160
1029-0265
DOI:10.1080/00207160108805083