Point and interval estimation of decomposition error in discrete-time open tandem queues

We analyze the approximation quality of the discrete-time decomposition approach, compared to simulation, and with respect to the expected value and the 95th-percentile of waiting time. For both performance measures, we use OLS regression models to compute point estimates, and quantile regression models to compute interval estimates of decomposition error. The ANOVA reveal major influencing factors on decomposition error while the regression models are demonstrated to provide accurate forecasts and precise confidence intervals for decomposition error

An Empirical Study About the Effectiveness of Lean Empowerment in Warehouses

Lean Management is well established in production environments. Some empirical evidences are available which suggest that in production systems lean management achieves positive results. For warehousing, some works have already been done, which deal with the application and adaption of lean tools for usage in warehousing. In order to answer the question, whether the application of lean tools leads to a better performance however, no study is available today. Therefore, an empirical study has been conducted, where the effectiveness of lean empowerment has been tested and compared to the performance of warehouse, who continued to work as before

Regression analyses of the data sets for the analysis of decomposition error in discrete-time open tandem queues

The data sets and regression models presented here are related to the article "Point and interval estimation of decomposition error in discrete-time open tandem queues". The data sets are the first to analyze the approximation quality of the discrete-time decomposition approach and contain independent and dependent (explanatory) variables for the analysis of decomposition error, which were obtained using discrete-time queueing models and discrete-event simulation. Independent variables are the utilization parameters of the queues, and variability parameters of the service and arrival processes. Dependent variables are decomposition error with respect to the expected value and 95-percentile of the waiting time distribution at the downstream queue. This article presents multiple linear regression and quantile regression to explain the variance of the dependent variables for tandem queues with equal traffic intensity at both queues and for tandem queues with downstream bottlenecks, respectively

Panel Session #1: Production Logistics in the Industrie 4.0 Era

Panel Session 1 Panelists: Kai Furmans, Fabio Sgarbosse, Tone Lehrer Moderator: David Porte

Using Logical Time to Ensure Liveness in Material Handling Systems With Decentralized Control

We describe a method for decentralized control of route-based material handling systems in which devices have no central controller (by definition), no common source of information, and no synchronized or common clocks with which to plan and execute their activities. The control scheme is based on the concept of \emph{logical time}, which is a means of partially ordering events in computer operating systems. We modify the concept to the domain of material handling systems and prove system liveness. We conclude by describing GridSorter, a conveyance-based sorter that uses decentralized control and logical time to sort packages. A prototype has been successfully built and tested at the Institute for Material Handling and Logistics at Karlsruhe Institute of Technology