TY - CHAP

T1 - Switching Dispatching Rules with Gaussian Processes

AU - Heger, Jens

AU - Hildebrandt, Torsten

AU - Scholz-Reiter, Bernd

PY - 2013

Y1 - 2013

N2 - Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems, e.g. semiconductor manufacturing. Nevertheless, no dispatching rule outperforms other rules across various objectives, scenarios and system conditions. In this paper we present an approach to dynamically select the most suitable rule for the current system conditions in real time. We calculate Gaussian process (GP) regression models to estimate each rule’s performance and select the most promising one. The data needed to create these models is gained by a few preliminary simulation runs of the selected job shop scenario from the literature. The approach to use global information to create the Gaussian process models leads to better local decision at the machine level. Using a dynamic job shop scenario we demonstrate, that our approach is capable of significantly reducing the mean tardiness of jobs.

AB - Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems, e.g. semiconductor manufacturing. Nevertheless, no dispatching rule outperforms other rules across various objectives, scenarios and system conditions. In this paper we present an approach to dynamically select the most suitable rule for the current system conditions in real time. We calculate Gaussian process (GP) regression models to estimate each rule’s performance and select the most promising one. The data needed to create these models is gained by a few preliminary simulation runs of the selected job shop scenario from the literature. The approach to use global information to create the Gaussian process models leads to better local decision at the machine level. Using a dynamic job shop scenario we demonstrate, that our approach is capable of significantly reducing the mean tardiness of jobs.

KW - Engineering

KW - Simulation

KW - Gaussian process regression

KW - Scheduling

KW - Dispatching rules

U2 - 10.1007/978-3-642-30749-2_7

DO - 10.1007/978-3-642-30749-2_7

M3 - Article in conference proceedings

SN - 978-3-642-30748-5

SP - 91

EP - 103

BT - Robust Manufacturing Control

A2 - Windt, Katja

PB - Springer

T2 - Conference on Robust Manufacturing Control - RoMaC 2012

Y2 - 18 June 2012 through 20 June 2012

ER -