A concurrent processing framework for the set partitioning problem

Authors:
Anito Joseph
Affiliations:
Department of Management Science, School of Business Administration, University of Miami, 417 Jenkins Builing, Coral Gables, FL
Venue:
Computers and Operations Research
Year:
2002

Citing 6
Cited 4

Optimal solution of set covering/partitioning problems using dual heuristics

Management Science
A multiplier adjustment approach for the set partitioning problem

Operations Research - Supplement
Solving airline crew scheduling problems by branch-and-cut

Management Science
The column subtraction algorithm: an exact method for solving weighted set covering, packing and partitioning problems

Computers and Operations Research
Tighter representations for set partitioning problems

Discrete Applied Mathematics
Constraint Handling in Genetic Algorithms: The Set Partitioning Problem

Journal of Heuristics

Optimal refinement of rule bases

AI Communications
Optimal refinement of rule bases

AI Communications
Optimal case-based refinement of adaptation rule bases for engineering design

ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
A dual ascent procedure for the set partitioning problem

Discrete Optimization

Quantified Score

Hi-index	0.01

Visualization

Abstract

A scheme for domain decomposition of the set partitioning problem is presented. Similar to the exploitation of special structure to improve algorithm performance, special structure can be exploited to divide the set partitioning problem into smaller subproblems. Real-world set partitioning problems from the airline industry are used to study the potential advantages of solving multiple subproblems to identify optimal solutions. The results of the study show that the decomposition is especially successful when applied to large problems that are difficult when solved using a single processor. For these cases, decomposition was able to produce smaller problems that, in the majority of cases, were far easier to solve than the original problem. Also, optimal solutions were identified in significantly less time than the time taken to solve the original problem. The results suggest that concurrent processing of subproblems should be investigated as an alternative method for solving large set partitioning problems typically encountered in real-world applications.