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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

An important scheduling function of manufacturing systems is controlled order release. While there exists a broad literature on order release, reported release procedures typically use simple sequencing rules and greedy heuristics to determine which jobs to select for release. While this is appealing due to its simplicity, its adequateness has recently been questioned. In response, this study uses an integer linear programming model to select orders for release to the shop floor. Using simulation, we show that optimisation has the potential to improve performance compared to ‘classical’ release based on pool sequencing rules. However, in order to also outperform more powerful pool sequencing rules, load balancing and timing must be considered at release. Existing optimisation-based release methods emphasise load balancing in periods when jobs are on time. In line with recent advances in Workload Control theory, we show that a better percentage tardy performance can be achieved by only emphasising load balancing when many jobs are urgent. However, counterintuitively, emphasising urgency in underload periods leads to higher mean tardiness. Compared to previous literature we further highlight that continuous optimisation-based release outperforms periodic optimisation-based release. This has important implications on how optimised-based release should be designed.