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Order release is a key component of Workload Control - a production planning and control system that aims at balancing workloads across workstations, while ensuring the timely release of jobs (or orders) to the shop floor in order to meet due dates. Several release methods have been proposed and evaluated in the WLC literature. A major criterion to distinguish between release methods is whether they take the release decision at periodic time intervals or continuously. This paper aims at improving WLC order release by incorporating a starvation avoidance trigger into continuous release. Using simulation, we demonstrate that significant performance improvements in terms of mean tardiness and standard deviation of lateness can be obtained. These results are expected to have important implications for industrial practice and for future research on WLC.

Constant Work-in-Process (ConWIP) is a card-based control system that was developed for simple flow shops – a lack of load-balancing capabilities hinders its application to more complex shops. In contrast, load balancing is an integral part of Workload Control, a production planning and control concept developed for high-variety environments. One means of load balancing evident in the Workload Control literature is through the use of a capacity slack-based backlog-sequencing rule. This study therefore investigates the potential of the backlog-sequencing decision to improve load balancing in the context of ConWIP, thereby making it suitable for more complex, high-variety environments. Using simulation, we demonstrate that: (i) the choice of backlog-sequencing rule significantly impacts throughput times and tardiness-related performance measures; and (ii) capacity slack-based sequencing rules achieve significant performance improvements over ‘classical’ ConWIP backlog-sequencing rules. These results significantly extend the applicability of ConWIP. Results from the Workload Control literature however do not directly translate across to ConWIP. The simplified release procedure of ConWIP makes backlog-sequencing based on planned release dates dysfunctional. This negatively impacts the performance of modified capacity slack-based sequencing rules that were recently shown to be the best choice for Workload Control.

POLCA (i.e. Paired-cell Overlapping Loops of Cards with Authorization) is a card-based production control approach developed to support the adoption of Quick Response Manufacturing. The approach has received significant research attention but has remained largely unchanged since its introduction in the late 1990s. The main improvements have occurred in the context of an electronic POLCA system, but such developments undermine the simplicity of the original card-based concept. We ask: is there any refinement possible to enhance the performance of POLCA without jeopardizing its simplicity? By analyzing POLCA, two possible refinements are identified: (i) the choice of rule to support both the card allocation and dispatching decisions; and (ii) the use of a starvation avoidance mechanism to overcome premature station idleness, as reported in the context of load limiting order release. Using simulation, we demonstrate that performance gains can be obtained by using different rules for card allocation and dispatching other than the earliest release date rule typically applied in POLCA for both decisions. Further, results demonstrate performance improvements for all combinations of card allocation and dispatching rules considered via the addition of a simple starvation avoidance mechanism. Both refinements significantly enhance POLCA performance, potentially furthering its application in practice.

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

POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is a decision support system for material flow control under Quick Response Manufacturing. It operates in the context of low-volume, high-mix, and cellular manufacturing. While there is an increasing literature on POLCA performance, current studies usually assume full availability of components (or parts) at assembly stations, neglecting parts manufacturing and feeding. Therefore, this study uses simulation to assess POLCA performance in a two-stage production system, where at the first stage parts are manufactured and at the second, they are assembled into end-products. The study demonstrates that using POLCA to control both production stages, manufacturing and assembly, significantly outperforms the use of POLCA at the assembly stage only, leading to important reductions of the total throughput time of orders and on the percentage of tardy orders. Statistical analysis of our results was conducted using ANOVA.

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.