Multi-temperature simulated annealing for optimizing mixed-blocking permutation flowshop scheduling problems

Scheduling problems play an increasingly significant role in the design and optimization of highly computerized and automated production systems. Given the importance of just-in-time production in advanced manufacturing, scheduling methods should enable the users to consider various blocking situations in a zero work-in-process scheme. In this situation, Permutation Flowshop Scheduling Problem with Mixed-Blocking Constraints (MBPFSP) is a much-needed scheduling extension that allows for heterogeneous blocking criteria between successive machines. Considering the scale of integrated production systems, and the inherent complexities involved in this type of scheduling problems, efficient and robust solution algorithms are necessary to facilitate industry applications of this emerging scheduling problem. This study extends to develop an improved metaheuristic, the Multiple Temperature Simulated Annealing (MTSA) algorithm, to provide high-quality solutions to MBPFSPs, considering makespan. Using extensive benchmark experiments, it is shown that the developed algorithm outperforms the state-of-the-art existing approaches applied to solve the MBPFSP. Overall, this research sets the stage for MBPFSP's industry scale applications, narrowing the gap between the scheduling theory and practice.