On-the-fly performance-aware human resource allocation in the business process management systems environment using Naïve Bayes

Arif Wibisono, Amna Shifia Nisafani*, Hyerim Bae, You Jin Park

*Corresponding author for this work

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

20 Citations (Scopus)

Abstract

Traditionally, resource allocation problem has been considered as one of the important issues in business process management to maintain the acceptable level of each activity completion time which can reduce the total completion time. Especially, the complexity of managing resources increases when the resource type is human because performance of each human resource might fluctuate over time due to various unpredicted factors. Hence, upfront planning of the resource allocation might be unsuitable in this matter. Therefore, this study proposes an on-the-fly resource allocation using Naïve Bayes to manage human resources more efficiently. The term on-the-fly here indicates that the resource allocation planning will be frequently updated and executed during the execution time by considering recent human resource performances. In this paper, we will show the proposed approach exceeds other resource allocation approaches in terms of total completion time.

Original languageEnglish
Title of host publicationAsia Pacific Business Process Management - 3rd Asia Pacific Conference, AP-BPM 2015, Proceedings
EditorsLijie Wen, Suriadi Suriadi, Joonsoo Bae
PublisherSpringer Verlag
Pages70-80
Number of pages11
ISBN (Print)9783319195087
DOIs
Publication statusPublished - 2015
Event3rd Asia Pacific Conference on Business Process Management, AP-BPM 2015 - Busan, Korea, Republic of
Duration: 24 Jun 201526 Jun 2015

Publication series

NameLecture Notes in Business Information Processing
Volume219
ISSN (Print)1865-1348

Conference

Conference3rd Asia Pacific Conference on Business Process Management, AP-BPM 2015
Country/TerritoryKorea, Republic of
CityBusan
Period24/06/1526/06/15

Keywords

  • Dispatching rules
  • Machine learning
  • On-the-fly resource allocation
  • Resource-based priority rules

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