Distributed factories represent a type of manufacturing system in which production is spread out across multiple geographically dispersed locations. This approach offers several advantages, including reduced transportation costs, improved responsiveness to customer demand, increased flexibility and enhanced supply chain resilience. The production-assembly flow shop problem with three stages is a scheduling problem focused on optimizing the sequence of jobs to be processed on a set of machines. In this problem, the first and third stages involve dedicated parallel machines, meaning that each job is assigned to a specific machine and cannot be processed on any other. The second stage consists of identical parallel machines, where all machines are functionally equivalent and capable of processing any job. A model is presented for minimizing total tardiness times. Since the problem under investigation is NP-hard, solving it exactly is either impossible or highly time-consuming (depending on the processor's capability) for large instances. Consequently, the Hybrid Biogeography-Based Optimization Dominance Rules (HBBO) algorithm is proposed to address the problem in larger instances. This algorithm is an enhanced version of the Biogeography-Based Optimization (BBO) algorithm, incorporating dominance rules. The Taguchi method has been employed to determine appropriate parameter values. The results obtained from the model and algorithm demonstrate the algorithm’s acceptable efficiency and the inclusion of dominance rules has further improved the outcomes.
