Predictive performance monitoring of material handling systems using the performance spectrum

Vadim Denisov, Dirk Fahland, Wil M.P. van der Aalst

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

12 Citaten (Scopus)
1 Downloads (Pure)

Samenvatting

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.

Originele taal-2Engels
TitelProceedings - 2019 International Conference on Process Mining, ICPM 2019
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's137-144
Aantal pagina's8
ISBN van elektronische versie978-1-7281-0919-0
DOI's
StatusGepubliceerd - 1 jun. 2019
Evenement1st International Conference on Process Mining, ICPM 2019 - Aachen, Duitsland
Duur: 24 jun. 201926 jun. 2019

Congres

Congres1st International Conference on Process Mining, ICPM 2019
Land/RegioDuitsland
StadAachen
Periode24/06/1926/06/19

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