Fully informed ABC algorithm for large scale job shop scheduling problem

Abstract

The large-scale job-shop scheduling problem (LSJSSP) is a complex scheduling problem. Previously, although the nature-inspired algorithm, specially the swarm intelligence (SIA) based algorithms have been efficiently applied to solve the LSJSSP, finding the best solution for LSJSSP instances remains a challenging task. Therefore, in this paper, a novel SIA is applied to solve the 105 LSJSSP instances. The selected SIA is a fully informed artificial bee colony (FABC) algorithm. The FABC algorithm is a variant of the ABC algorithm in which the position update process is inspired from the GABC. In the FABC, the onlooker bee process of the ABC strategy is modified and designed such that the new position of the solution search agent is obtained while learning from all the nearby agents. The results obtained by the FABC are compared with the strategies available in the literature. The results analysis shows that the proposed approach to solving LSJSSP is competitive in the field of SIA.