Stochastic modelling of process scheduling for reduced rework cost and scrap

Uncertainties in manufacturing can have a significant effect on the outcomes of a process and pose difficulties for the management of the processes. Although many models that consider uncertainties in the manufacturing process focus on differences in the processing time and availability of resources, this article reflects on a new aspect of the Stochastic Job Shop Scheduling Problem, evaluating the probability of success (or failure) of a manufacturing job and the effect of a job failure on the other jobs in the process, in particular the rework costs. The article presents a Markovian approach to model a set of manufacturing jobs based on the cost and the probabilistic distribution for success. A failure causes either rework of the failed job, or repetition of some or all previous jobs. The article presents a brief analysis for optimal tolerance assignment using the proposed model and includes a discussion on how this approach can be augmented with machine-learning tools. The article also presents an artificial intelligence-assisted methodology through online scheduling of production processes coupled with online and adaptive tolerance redesign for better management of machining assets.