Strong uncertainties is a key challenge for the application of scheduling algorithms in real-world production environments, since the optimized schedule at a time often turns to be deteriorated or even infeasible during its execution due to a large majority of unexpected events. This paper studies the uncertain scheduling problem arising from the steelmaking-continuous casting (SCC) process and develops a soft-decision based two-layered approach (SDA) to cope with the challenge. In our approach, traditional scheduling decisions, i.e. the beginning time and assigned machine for each job at each stage, are replaced with soft scheduling decisions in order to provide more flexibility towards unexpected events. Furthermore, all unexpected events are classified into two categories in terms of the impact degree on scheduling: critical events and non-critical events. In the two-layered solution framework, the upper layer is the offline optimization layer for handling critical events, in which a particle swarm optimization algorithm is proposed for generating soft scheduling decisions; while the lower layer is the online dispatching layer for handling non-critical events, where a dispatching heuristic is designed to decide in real time which charge and when to process after a machine becomes available, with the guidance of the soft schedule given by the upper layer. Computational experiments on randomly generated SCC scheduling instances and practical production data demonstrate that the proposed soft-decision based approach can obtain significantly better solutions compared to other methods under strongly uncertain SCC production environments. 
A soft-decision based two-layered scheduling approach for uncertain steelmaking-continuous casting process