A memetic discrete differential evolution algorithm for the distributed permutation flow shop scheduling problem

Abstract The distributed manufacturing has become a prevail production mode under the economic globalization. In this article, memetic discrete differential evolution (MDDE) algorithm is proposed to address permutation flow shop scheduling problem (DPFSP) with minimization of makespan. An enhanced NEH (Nawaz–Enscore–Ham) method presented produce potential candidate solutions and Taillard’s acceleration adopted ameliorate operational efficiency MDDE. A new mutation strategy introduced promote search Four neighborhood structures, which are based on job sequence factory assignment adjustment mechanisms, prevent candidates from falling local optimum during process. mechanism selected adaptively through knowledge-based focuses adaptive evaluation for selection. optimal combinations parameters in MDDE testified by design experiment. computational results comparisons demonstrated effectiveness solving DPFSP.