A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem

Abstract

A hybrid artificial bee colony (HABC) algorithm is proposed in this paper for solving the fuzzy flexible job-shop scheduling problem (FFJSP). First, the HABC utilises multiple strategies in a combined way to generate the initial solutions with certain quality and diversity as the food sources, and applies the left-shift decoding scheme to convert solutions to active schedules. Second, the exploitation search procedures based on the crossover operators for machine assignment and operation sequence in the employed bee phase are designed to generate the new neighbouring food sources. Third, the exploitation search procedures are also used to update the old food source in the onlooker bee phase with the new source based on the best-so-far source instead of the neighbouring sources. Fourth, to prevent premature convergence in the scout bee phase, the population is updated by the new source with an adjustable search radius. Meanwhile, a local search based on the variable neighbourhood search (VNS) is performed on the best-so-far solution to enhance the local intensification. Based on the Taguchi method of design of experiment, the influence of parameter setting is investigated and suitable parameter values are suggested. Numerical testing results and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed HABC. Besides, the comparison between the HABC with and without VNS local search demonstrates the effectiveness of hybridising ABC-based exploration and VNS-based exploitation.