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Paper Abstract and Keywords
Presentation 2023-01-19 14:40
Fairness-aware human resource allocation for runtime business process
Nayuta Egashira, Hiroki Horita (Ibaraki Univ.) KBSE2022-46
Abstract (in Japanese) (See Japanese page) 
(in English) Predictive business process monitoring improves the work efficiency of a running business process based on the analysis of event log data of previously executed business processes. This technique has been applied to methods that improve work efficiency by allocating optimal human resources to tasks. However, such studies focus on the completion of the most recent task in the minimum time, and do not focus on the long-term improvement of business processes. In this study, we investigate a method to link the results of predictive business process monitoring to the improvement of the execution environment of business processes. Using predictions about future tasks in running business processes, we optimize online the allocation of human resources required to execute the tasks. Validation using simulation-generated hospital event logs shows that the proposed method is able to balance the workload of human resources at the expense of current work efficiency. The generated human resource assignments are expected to minimize the risk of overload and improve proficiency efficiency and motivation.
Keyword (in Japanese) (See Japanese page) 
(in English) process mining / predictive business process monitoring / resource allocation / job-shop scheduling / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 345, KBSE2022-46, pp. 19-24, Jan. 2023.
Paper # KBSE2022-46 
Date of Issue 2023-01-12 (KBSE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF KBSE2022-46

Conference Information
Committee KBSE  
Conference Date 2023-01-19 - 2023-01-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To KBSE 
Conference Code 2023-01-KBSE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fairness-aware human resource allocation for runtime business process 
Sub Title (in English)  
Keyword(1) process mining  
Keyword(2) predictive business process monitoring  
Keyword(3) resource allocation  
Keyword(4) job-shop scheduling  
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1st Author's Name Nayuta Egashira  
1st Author's Affiliation Ibaraki University (Ibaraki Univ.)
2nd Author's Name Hiroki Horita  
2nd Author's Affiliation Ibaraki University (Ibaraki Univ.)
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Speaker Author-1 
Date Time 2023-01-19 14:40:00 
Presentation Time 30 minutes 
Registration for KBSE 
Paper # KBSE2022-46 
Volume (vol) vol.122 
Number (no) no.345 
Page pp.19-24 
#Pages 6 
Date of Issue 2023-01-12 (KBSE) 

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