A differential evolution algorithm for lot-streaming flow shop scheduling problem

Abstract

A differential evolution (DE) algorithm is proposed to minimize the total weighted tardiness and earliness penalties for lot-streaming flow shop scheduling problems. In the proposed DE algorithm, the largest position value (LPV) rule is used to convert a real-number DE vector to a job permutation. The DE evolution is used to perform global exploitation, and a local search procedure is used to enhance the exploration capability. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DE algorithm. © 2011 Springer-Verlag.