Scheduling fuzzy job shop using random key genetic algorithm

Abstract

Genetic algorithm has been successfully applied to fuzzy job shop scheduling problem, however, the coding and decoding strategies of the problem aren't fully investigated. This paper presents an efficient random key genetic algorithm (RKGA) for the problem to minimize the maximum fuzzy completion time. RKGA uses a novel random key representation, a new decoding strategy and discrete crossover. RKGA is applied to some fuzzy scheduling instances and compared with a genetic algorithm and particle swarm optimization with genetic operators. Computational results demonstrate that RKGA has the promising advantage on fuzzy scheduling.