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“This is a post-peer-review, pre-copyedit version of an article published in Advances in Intelligent Systems and Computing. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-56538-5_40"
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Industrial and Production Engineering on 13/02/2019, available online: https://www.tandfonline.com/doi/full/10.1080/21681015.2019.1576784
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 16/01/2018, available online: http://www.tandfonline.com/10.1080/00207543.2018.1425018
Lot splitting is an important approach for shops that compete on short delivery times. Similarly, such shops can benefit from load-limiting order release mechanisms that balance workloads and regulate throughput times. Yet few studies have examined the combined effect of lot splitting and load-limiting order release. We use simulation to assess the combined effect of lot splitting and Paired-cell Overlapping Loops of Cards with Authorization (POLCA), an important load-limiting order release mechanism in the context of time-based competition. The experimental design includes different lot sizes, lot transfer policies, and POLCA quanta, i.e. the limit on the size of jobs represented by a single POLCA card. Lot splitting improves performance if lots can proceed independently as this ensures the quick replenishment of queues at downstream stations. However, we find that enforcing the synchronization of all lots that make up a job at every routing step leads to a deterioration in performance. This extends previous research, which appears to have overemphasized the positive effects of lot splitting. Meanwhile, although POLCA cards were originally used to represent lots, we demonstrate that using cards to represent a certain amount of workload can improve percentage tardy performance. This may also have resonance with other card-based solutions, including kanban.
“This is a pre-print of an article published in Journal of Intelligent Manufacturing. The final authenticated version is available online at Springer http://dx.doi.org/10.1007/s10845-018-1402-2
