An Iterated Greedy Algorithm for the Distributed Permutation Flow Shop Scheduling Problem with Worker Flexibility

The Distributed Permutation Flow Shop scheduling Problem with Worker Flexibility is a newly proposed topic in the shop scheduling field. It represents a complex scheduling challenge that arises in modern manufacturing environments where jobs must be assigned to multiple production lines distributed across different locations. This complexity increases with the consideration of worker flexibility, where workers can be reassigned across various tasks and machines, impacting overall efficiency. In this study, we propose the application of the Iterated Greedy algorithm to solve the Distributed Permutation Flow Shop scheduling Problem with Worker Flexibility with the objective of minimizing the makespan among all factories. The experimental results demonstrate that the Iterated Greedy algorithm can generate high-quality solutions within reasonable computational times, proving its effectiveness in addressing the complexities of Distributed Permutation Flow Shop scheduling Problem with Worker Flexibility. Moreover, the study highlights the critical role of worker flexibility in influencing scheduling efficiency.