Paper Info
Title
MetalP - a new approach to combinatorial optimization: case studies
Abstract
We propose a new approach to solve combinatorial optimization problems. Our approach is simple to implement but powerful in terms of performance and speed. We combine the strengths of a meta-heuristic approach with the integer programming method by partitioning the problem into two interrelated subproblems, where the higher level problem is solved by the metahueristic and the lower level problem is solved by integer programming. We discuss the selection of key variables to facilitate an effective partitioning, and test our approach on two real world crossdocking problems, which is very popular in this part of the world. Our experimental results indicate that our new approach is very promising.
Year
DOI
Venue
2004
10.1109/ICTAI.2004.84
ICTAI
Keywords
Field
DocType
combinatorial optimization,meta-heuristic approach,case studies,integer programming,combinatorial optimization problem,search problems,heuristic programming,crossdocking problem,computational complexity,lower level problem,problem solving,genetic algorithm,effective partitioning,genetic algorithms,higher level problem,integer programming method,real world,new approach,np-hard problems
Mathematical optimization,Vehicle routing problem,Computer science,Quadratic assignment problem,Branch and price,Combinatorial optimization,Integer programming,Artificial intelligence,Cutting stock problem,Optimization problem,Stochastic programming,Machine learning
Conference
ISSN
ISBN
Citations 
1082-3409
0-7695-2236-X
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Yanzhi Li16711.56
Andrew Lim2203.19