Robust Shipping Date Predictions in Manufacturing using High-level Petri Nets. (4th May 2022)

• Record Type: Journal Article
• Authors: Holznigenkemper, Jana; Hannig, Jennifer; Keiner, Jan-Niklas; Seeger, Bernhard; Guckert, Michael
• Is Part Of: International journal of modelling & simulation. Volume 42:Number 3(2022)
• Page Start: 464
• Page End: 484
• Publication Date: 2022-05-04
• Subjects: Flexible job shop manufacturing -- buffer time calculation -- stochastic modelling -- shipping reliability -- monte carlo simulation -- timed coloured petri net; Mathematical models -- Periodicals; Simulation methods -- Periodicals; Mathematical models; Simulation methods; Periodicals; 003.3
• DOI: 10.1080/02286203.2021.1931770
• Languages: English
• ISSNs: 0228-6203

Abstract

ABSTRACT: Machine failures, unavailability of the workforce, and missing raw materials are typical, frequent faults in complex manufacturing systems and cause unstable processes. Plans and schedules become volatile, predictability of shipping dates suffers and delayed delivery appears. However, for a customer reliable shipping can often be of higher benefit than promised short-term shipping with significant probability of delay and error. Inserting buffer times stabilises schedules by reducing the potential impact of disruptions. Therefore, computing appropriately sized buffer times for individual jobs in a flexible job shop manufacturing with setup times is a promising yet difficult mission for production research. In this paper, we discuss methods for buffer time calculation that are based on data collected directly from the underlying production processes. We show how a high-level Petri net model can be used to simulate stochastic variations of the manufacturing process. Using this simulation model, we estimate the effect of buffer times and produce predictions for the expected shipping dates together with probabilities for how likely the predicted dates will actually be met.