A Self-braking Symbiotic Organisms Search Algorithm for Bi-objective Reentrant Hybrid Flow Shop Scheduling Problem

Abstract

The bi-objective reentrant hybrid flow shop problem (BRHFSP) is a typical NP-hard scheduling case in a semiconductor wafer fabrication. In this paper, a self-braking Symbiotic Organisms Search algorithm (SSOS) is proposed to minimize the total tardiness and makespan of this problem. A discrete multi-objective Symbiotic Organisms Search is selected to reduce the wasted time of adjusting excessive parameters in most evolutionary algorithms. This algorithm has a brief structure with no control parameters. Moreover, the entropy-based termination criterion is added to multi-objective Symbiotic Organisms Search to decrease the computation burden. In this way, an entropy-based dissimilarity measure criterion is generated to help our algorithm stop automatically with the increase of iterations. Numerical test results in many cases demonstrate that SSOS is effective for BRHFSP.