An Adaptive Shuffled Frog-Leaping Algorithm for Hybrid-Flow Shop Scheduling with No Precedence Between Some Stages

Abstract

Hybrid flow shop scheduling problem (HFSP) has been extensively considered, however, some real-life conditions are seldom investigated. In this study, HFSP with no precedence between some stages is solved and an adaptive shuffled frog-leaping algorithm (ASFLA) is developed to optimize makespan. A new solution representation and a decoding procedure are presented, an adaptive memeplex search and dynamical population shuffling are implemented together. Many computational experiments are implemented. Computational results prove that the new strategies of ASFLA are effective and ASFLA is very competitive in solving HFSP with no precedence between some stages.