MetaIP - A new approach to Combinatorial Optimization: Case studies

Yanzhi Li; Andrew Lim

doi:10.1109/ICTAI.2004.84

MetaIP - A new approach to Combinatorial Optimization : Case studies

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal › peer-review

Overview

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Author(s)

Yanzhi Li
Andrew Lim

Detail(s)

Original language	English
Pages (from-to)	56-62
Journal / Publication	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Publication status	Published - 2004
Externally published	Yes

Conference

Title	Proceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004
Place	United States
City	Boca Raton, FL
Period	15 - 17 November 2004

Link(s)

DOI	DOI https://doi.org/10.1109/ICTAI.2004.84 Final Published version
	Check@CityULib
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-16244422963&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(f16d9891-34de-447a-9bcb-e4ae6ad011ba).html

Abstract

In this paper, 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 cross-docking problems which is very popular in this part of the world. Our experimental results indicate that our new approach is very promising.