Stochastic customer order scheduling with setup times to minimize expected cycle time

Short cycle time of customer orders is crucial for companies to achieve mass customization and quick response. However, the complicated and stochastic environment, especially the exist of setup times, makes it extremely challenging to optimize the efficiency of a system. In this study, stochastic customer orders are scheduled to minimize their expect cycle time with the consideration of setup times. Customer orders arrive dynamically, and each order requires multiple product types with random workloads. These workloads will be assigned to a set of unrelated parallel machines to be processed. Particularly, for each machine, setup times are required whenever there is a product type changeover, and the lengthes are both machine- and product type-dependent. This paper intends to minimize the long-run expected order cycle time by proper policies including workload allocation and type sequencing. The impacts of product type sequence and workload variance are evaluated through theoretical study and several analytical properties are developed. With the help of these properties, three scheduling algorithms are proposed, and a lower bound is derived to evaluate the proposed algorithms. Computational experiment is conducted to demonstrate the effectiveness of the lower bound and the algorithms under various circumstances, and several important managerial insights are also provided.