Meta-Lamarckian-based iterated greedy for optimizing distributed two-stage assembly flowshops with mixed setups

Abstract

Integrated scheduling of distributed manufacturing operations has implications for supply chain optimization and requires further investigations to facilitate its application area for various industry settings. This study extends the limited literature of the distributed two-stage production-assembly scheduling problems offering a twofold contribution. First, an original mathematical extension, the Distributed Two-Stage Assembly Flowshop Scheduling Problem with Mixed Setups (DTSAFSP-MS) is investigated to integrate setup time constraints while addressing an overlooked scheduling assumption. Second, a novel extension to the Iterated Greedy algorithm is developed to solve this understudied scheduling problem. An extensive set of test instances is considered to evaluate the effectiveness of the developed solution algorithm comparing it with the current-best-performing algorithm in the literature. Results are supportive of the Meta-Lamarckian-based Iterated Greedy (MIG) as a strong benchmark algorithm for solving DTSAFSP-MS with the statistical tests confirming its meaningfully better performance compared to the state-of-the-art.