Controlling and Optimization of On-time Delivery for Assemble-to-order Production System

Meeting the delivery commitments is important for the success of any business. On the other hand, balancing other resources is also essential for business sustainability. This work provides a conceptual framework to make an optimization model to optimize the on-time delivery (OTD) for an Assemble-to-order (ATO) Production System. OTD optimization model is given by considering the delivery penalty, overtime working, manpower cost, and production item set-up time. The proposed model is helpful while making the daily production plan and work schedule of a multi-model assembly line. Furthermore, the optimization model is a integration of prediction and assembly line optimization models. Learning-based techniques are adopted to make the prediction model. Assembly line worker assignment and workload balancing algorithms are adopted to optimize the workforce or manpower cost. A use case illustration is given to understand the research scope better.