Seru order acceptance and scheduling in the distributed production network considering preventive maintenance

Abstract

In today’s globalized manufacturing environment, producers encounter unprecedented challenges stemming from intense competition, volatile customer demands, and the imperative to deliver high-quality, customized products. Collaborative production within multi-factory networks has emerged as a vital strategy for optimizing workflows and increasing flexibility. Complementing this approach, seru systems—which segment production lines into smaller, autonomous units—enhance responsiveness to fluctuating demand patterns. However, coordinating and scheduling operations across such networks introduces significant complexity, posing a substantial challenge for production-optimization research. Moreover, preventive maintenance is essential for sustaining system productivity by minimizing unplanned downtime and extending equipment lifespan. This study examines scheduling and order acceptance in a distributed production network that integrates seru systems, requiring customer orders to be allocated among factories—some of which operate under seru configurations. A mixed‑integer nonlinear programming (MINLP) model is proposed and implemented in GAMS. For larger-scale instances, the study introduces two memetic algorithms, each integrating a distinct local-search strategy: one utilizing simulated annealing and the other applying hill-climbing. Comparative analysis against a genetic algorithm demonstrates the superior efficiency of both memetic approaches in solving complex instances. The findings offer significant practical implications, such as reduced operational costs, increased production flexibility, and accelerated order fulfillment. Results further demonstrate that integrating seru systems with preventive maintenance strategies in multi-factory networks enhances system stability and efficiency, supports customized manufacturing, and mitigates downtime.