Dynamic scheduling of manufacturing systems: a product-driven approach using hyper-heuristics

Abstract

ABSTRACT Dynamic scheduling of manufacturing systems is encountered in many real-world industries such as the food and pharmaceutical industries. The scheduling of these systems must not only be efficient but also reactive to cope with dynamic job arrivals and machine breakdowns. Over the last decade, products within Product-Driven Control Systems (PDCS) have become smart entities capable of actively handling the manufacturing process. In this paper, a PDCS based on the design of Smart Products is proposed. A generic model of the decisional strategy allows Smart Products to characterize different decisional contexts and thus switch efficiently from one scheduling rule to another using a novel Hyper-Heuristics (HH) based approach. The implementation and testing of the proposed PDCS on hybrid flexible flow-shops with multiple constraints inspired by the pharmaceutical industry are presented. The comparative study with 168 combinations of scheduling rules from the literature highlighted the superiority of the HH to minimize the Mean Completion Time. Furthermore, the proposed approach enhanced both the global performance and the reactivity of the manufacturing control system.