This paper studies a synchronised scheduling problem of production simultaneity and shipment punctuality in a two-stage assembly flowshop system. Production simultaneity seeks to ensure all products belonging to a same customer order are simultaneously completed (at least as close as possible). Shipment punctuality attempts to satisfy orders’ individual shipment due dates. We provide two criteria, i.e. mean longest waiting duration and mean earliness and tardiness, for measuring production simultaneity and shipment punctuality, respectively. A synchronised scheduling model is developed by balancing the two criteria using linear weighted sum method. A modified genetic algorithm (GA) is then proposed for solving this model. Numerical studies demonstrate the effectiveness of the proposed approach. The results indicate that considering production simultaneity can remarkably reduce finished products inventory. A prioritised weight combination interval for production simultaneity and shipment punctuality has been suggested. Production simultaneity is affected by the production system configuration, especially in peak seasons.

Traditionally, production scheduling is optimised using objective functions based on punctuality criteria such as earliness and tardiness. However, synchronisation in complicated manufacturing process is always neglected. Due to the lack of synchronisation, severe problems happen frequently such as long holding time and unreasonable setups. Motivated by the practical challenges, the authors are among the first to present the IoT-enabled real-time synchronisation approaches for an automobile standard part factory with the following objectives. First, the synchronisation paradigm is proposed in terms of two dimensions, namely the Horizontal Synchronisation (HSync) for the consistency of batch production progresses within one customer order, and the Vertical Synchronisation (VSync) for the coordination between adjacent processing stages. Second, the IoT-enabled synchronisation mechanisms using rule-based heuristics and real-time production data is presented. Third, an advanced planning and scheduling (APS) system framework is developed to automatically implement the synchronisation mechanisms. Finally, the effectiveness of the synchronisation mechanisms is verified through a case study.