A robust dynamic scheduling approach based on release time series forecasting for the steelmaking-continuous casting production

Abstract

In this work, the dynamic scheduling problem is investigated considering the uncertainty of the job release time in steelmaking-continuous casting production processes. In contrast to existing dynamic scheduling strategies, a novel robust dynamic scheduling approach based on release time series forecasting (RDSA_RTSF) is proposed. The proposed RDSA_RTSF consists of two stages, i.e., offline preparation and online robust dynamic scheduling. In the offline preparation stage, a release time series forecasting model is established using historical data, and the forecasting accuracy of the model is calculated. In the online robust dynamic scheduling stage, a chance constrained programming (CCP) model is built first for rescheduling based on the predicted release time series and the statistical information of the forecasting accuracy. A robust schedule is subsequently generated by using a Monte Carlo simulation and an evolutionary algorithm to solve the CCP model. The evolutionary algorithm is formulated by combining a genetic algorithm with a local search strategy based on the problem characteristics. Computational experiments based on real data from a steel plant in China show that the proposed RDSA_RTSF strategy performs better than classical approaches in obtaining robust schedule results under a dynamic environment with uncertain release times.