Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33678
Title: A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem
Authors: Chang, PC
Huang, WH
Wu, JL
Cheng, TCE 
Keywords: Artificial chromosomes
Building blocks
Flowshop scheduling
Genetic algorithm
Linkage learning
Re-combination
Issue Date: 2013
Publisher: Elsevier
Source: International journal of production economics, 2013, v. 141, no. 1, p. 45-55 How to cite?
Journal: International journal of production economics 
Abstract: The goal of block mining is to obtain a set of genes that contain dependency among gene relationships. Such blocks without overlapping of genes can be further merged to form a new chromosome and the quality of the new chromosome can be greatly improved. Based on this concept, we propose a novel block mining method that is able to locate common structures or to establish new blocks (like a small piece of puzzle) from a set of high fit chromosomes. The identified blocks (puzzles) will also be updated generation by generation through the newly updated high fit chromosomes. We develop a heuristic re-combination procedure to form a new chromosome by re-combining the blocks. We call the new chromosomes generated as artificial chromosomes (ACs) and inject them into the evolutionary process when the convergence slope of the evolutionary process is less than a predefined threshold. This new algorithm retains the regular simple genetic algorithm (SGA) operations of crossover and mutation, and utilizes the ACs generated from elites to speed up the convergence process. Experimental results indicate that the puzzle-based method of chromosome generation is very efficient and effective in solving the traditional permutation flowshop scheduling problem. The new method can be applied to tackle other NP-complete problems such as scheduling and vehicle routing problems.
URI: http://hdl.handle.net/10397/33678
ISSN: 0925-5273
DOI: 10.1016/j.ijpe.2012.06.007
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