This paper focuses on the hybrid flow shop scheduling problem with sequence-dependent setup time (HFSP-SDST) with minimizing the makespan. To address this problem, this paper designs a hybrid migrating birds optimization (HMBO) algorithm that integrates migrating birds optimization (MBO) algorithm, variable neighborhood descent search (VND) algorithm, problem-based local search (LS) algorithm, and constraint programming (CP) model. Specifically, HMBO consists of three primary stages. The first stage employs a hybrid algorithm (MBOVND) that integrates MBO and VND with permutation encoding and decoding. Because the solution space of permutation encoding and decoding cannot cover the full solutions of HFSP-SDST, LS and CP are used to enlarge the solution space of MBOVND in the second and third stages respectively. Specifically, LS algorithm is used in the second stage to explore the solutions that are not in the solution space of MBOVND, and CP model is used in the third phase to further enlarge the solution space of MBOVND and LS algorithm. The efficacy of the proposed VND, LS, CP, and HMBO are verified. Experimental results demonstrate that VND, LS, and CP are effective to improve the solving ability of MBO, and HMBO improves 89 out of the 120 best-known solutions for the benchmark instances.

In real production, machines are operated by workers, and the constraints of worker flexibility should be considered. The flexible job shop scheduling problem with both machine and worker resources (DRCFJSP) has become a research hotspot in recent years. In this paper, DRCFJSP with the objective of minimizing the makespan is studied, and it should solve three sub-problems: machine allocation, worker allocation, and operations sequencing. To solve DRCFJSP, a novel hybrid algorithm (CEAM-CP) of cooperative evolutionary algorithm with multiple populations (CEAM) and constraint programming (CP) is proposed. Specifically, the CEAM-CP algorithm is comprised of two main stages. In the first stage, CEAM is used based on three-layer encoding and full active decoding. Moreover, CEAM has three populations, each of which corresponds to one layer encoding and determines one sub-problem. Moreover, each population evolves cooperatively by multiple cross operations. To further improve the solution quality obtained by CEAM, CP is adopted in the second stage. Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations, CP, and CEAM-CP. Most importantly, the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances.