Dynamic shop-floor scheduling using real-time information: A case study from the thermoplastic industry

Practical production planning and scheduling systems must promptly respond to major real-time events and adjust their plans and schedules accordingly. To highlight the importance of concurrency in such systems, this paper addresses the problem of dynamic shop-floor scheduling using real-time information in a case study from the thermoplastic industry. The considered production line is organized as unrelated parallel production cells with a set of identical parallel machines in each cell. Parts are produced in batches using different molds on specific machines. Due to the size and complicated design of the molds, they require extended recovery periods (i.e., maintenance) in case of major failures. Therefore, previously developed plans and schedules need to be revised using real-time information every time a mold's major failure occurs. The production process is subject to the following constraints: batch processing, safety stocks, dedicated machines, machine-dependent setup times, precedence constraints, mold failures, and real-time updates. The problem is formulated as a mixed-integer programming model to minimize a weighted cost function that includes tardiness and operating costs. To solve the problem, a predictive-reactive scheduling approach is introduced based on a modified simulated annealing (SA) algorithm. The developed approach utilizes an event-driven rescheduling policy. It also embeds a problem-specific neighborhood structure and solution evaluation into the modified SA algorithm. The experi-mental study indicates that the proposed approach generates better real-life planning and scheduling results than the methods based on dispatching rules. The findings demonstrate that the proposed SA-based predictive-reactive scheduling approach generates the solutions with about a 26.1% less tardiness cost and a 6.99% less total weighted cost (on average). In addition, the results also show the competitiveness of the proposed SA-based predictive-reactive scheduling approach compared to two other approaches based on an iterated greedy (IG) algorithm and a Tabu Search (TS) algorithm from the literature.