Simulation optimization: a new job release approach for Industry 4.0

Abstract

The rise of Industry 4.0 has highlighted simulation optimisation as a decision-making tool for scheduling complex-manufacturing systems, specifically when resources are expensive and multiple jobs compete for the same resources. In this context, simulation optimisation provides an important mean to predict, evaluate and improve the short-term performance of the manufacturing system. An important scheduling function is controlled job release; jobs (or orders) are not released immediately to the shop floor, as they arrive to the production system, but release is controlled to stabilize work-in-process, reduce manufacturing lead times and meet customer delivery requirements. While there exists a broad literature on job release, reported release procedures typically use simple rules and greedy heuristics to determine which job to select for release. While this is justified by its simplicity, the advent of Industry 4.0 and its advanced scheduling techniques question its adequateness. In this study, an integer linear programming model is used to select jobs to be released to the shop floor. While there are some recent studies that use a similar procedure, these studies assume the release decision for a given set of jobs is optimized in discrete time intervals. In contrast, in this study, we analyse the impact of different triggering intervals. Experimental results for a pure flow shop support our contention that simulation optimisation as a decision-making tool for job release is likely to be too important to be overlooked