@inproceedings{379b46693c574d209f517eee7240fbca,
title = "On-the-fly performance-aware human resource allocation in the business process management systems environment using Na{\"i}ve Bayes",
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 Na{\"i}ve 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.",
keywords = "Dispatching rules, Machine learning, On-the-fly resource allocation, Resource-based priority rules",
author = "Arif Wibisono and Nisafani, {Amna Shifia} and Hyerim Bae and Park, {You Jin}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 3rd Asia Pacific Conference on Business Process Management, AP-BPM 2015 ; Conference date: 24-06-2015 Through 26-06-2015",
year = "2015",
doi = "10.1007/978-3-319-19509-4_6",
language = "English",
isbn = "9783319195087",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer Verlag",
pages = "70--80",
editor = "Lijie Wen and Suriadi Suriadi and Joonsoo Bae",
booktitle = "Asia Pacific Business Process Management - 3rd Asia Pacific Conference, AP-BPM 2015, Proceedings",
address = "Germany",
}