Predictive performance monitoring of material handling systems using the performance spectrum

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

Predictive performance analysis is crucial for supporting operational processes. Prediction is challenging when cases are not isolated but influence each other by competing for resources (spaces, machines, operators). The so-called performance spectrum maps a variety of performance-related measures within and across cases over time. We propose a novel prediction approach that uses the performance spectrum for feature selection and extraction to pose machine learning problems used for performance prediction in non-isolated cases. Although the approach is general, we focus on material handling systems as a primary example. We report on a feasibility study conducted for the material handling systems of a major European airport. The results show that the use of the performance spectrum enables much better predictions than baseline approaches.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Process Mining, ICPM 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages137-144
Number of pages8
ISBN (Electronic)978-1-7281-0919-0
DOIs
Publication statusPublished - 1 Jun 2019
Event1st International Conference on Process Mining, ICPM 2019 - Aachen, Germany
Duration: 24 Jun 201926 Jun 2019

Conference

Conference1st International Conference on Process Mining, ICPM 2019
CountryGermany
CityAachen
Period24/06/1926/06/19

Keywords

  • Material handling systems
  • Performance spectrum
  • Predictive process monitoring

Fingerprint Dive into the research topics of 'Predictive performance monitoring of material handling systems using the performance spectrum'. Together they form a unique fingerprint.

Cite this