Real-Time Near-Optimal Scheduling With Rolling Horizon for Automatic Manufacturing Cell

Abstract

This paper presents position-based optimization methods to schedule the production of automatic cells of a wheel manufacturing factory. Real-time schedule is challenging when a cell is interrupted by various order changes. Given a sequence of orders to be scheduled, it is sorted based on an earliest due day policy, a mixed integer linear programming model is formulated, and then rolling-horizon optimization methods are used to timely find the near-optimal schedule by minimizing earliness and tardiness penalties with setup times of a manufacturing cell. In addition, an original schedule can be partial rescheduled with the preset order sequence by using the linear programming model. Experimental results show that the proposed method enables a wheel manufacturing cell to reschedule its three to five daily orders within the cycle time of a rim when there exist order changes, e.g., rush orders and customized orders. Hence, these proposed methods are promising to promptly derive the near-optimal schedule for satisfying the objective of mass customization for industry 4.0.