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

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