A genetic algorithm for flexible job shop scheduling with fuzzy processing time

Abstract

This paper presents a flexible job shop scheduling problem with fuzzy processing time. An efficient decomposition-integration genetic algorithm (DIGA) is developed for the problem to minimise the maximum fuzzy completion time. DIGA uses a two-string representation, an effective decoding method and a main population. In each generation, DIGA decomposes the chromosomes of the main population into a job sequencing part and a machine assigning part and independently evolves the populations of these parts. Some instances are designed and DIGA is tested and compared with other algorithms. Computational results show the effectiveness of DIGA.

Keywords:

• artificial intelligence
• scheduling
• evolutionary algorithms

Acknowledgements

This research is supported by China Hubei Provincial Science and Technology Department under grant science foundation project (2007ABA332).