Session: Memetic Computing (06/06, 11:15-13:15, Room 7)

Iterated Local Search for Distributed Multiple Assembly No-wait Flowshop Scheduling

Iterated Local Search for Distributed Multiple Assembly No-wait Flowshop Scheduling

Peng Li, Yixin Yang, Xiying Du, Xinghua Qu, Keyang Wang and Bo Liu

Peng Li, Yixin Yang, Xiying Du, Xinghua Qu, Keyang Wang and Bo Liu

Academy of Mathematics and Systems Science Chinese Academy of Sciences, China Academy of Mathematics and Systems Science Chinese Academy of Sciences, China School of Mathematical Science Peking University, China Astronautics School Beihang U

Academy of Mathematics and Systems Science Chinese Academy of Sciences, China Academy of Mathematics and Systems Science Chinese Academy of Sciences, China School of Mathematical Science Peking University, China Astronautics School Beihang U

Driven by the pressing needs in coordinating and synchronizing multi-plant facilities for efficient production and manufacturing, distributed assembly permutation flowshop scheduling problem (DAPFSP) has been becoming the focus of concern of evolutionary computing and operations research, which is a typical NP-hard combinatorial optimization problem. In this paper, we propose a generalized version of DAPFSP, labeled here as multi-distributed assembly permutation flowshop problem (M- DAPFSP). In the M-DAPFSP, multiple assembly factories exist rather than only one assembly factory in the conventional DAPFSP, meanwhile no-wait constraint exists in the processing stage. To solve the aforementioned M-DAPFSP, we propose hybrid iterated local search with simulated annealing (ILS-SA) for the M-DAPFSP. Finally, simulation results based on 27 large-scale benchmark problems show that our proposed ILS-SA can effectively solve M-DAPFSP problems.