Bayesian network for order picker burden factor estimation considering individual differences
Recently, order picking, which is the most labour-intensive task in warehouse, has promoted the introduction of automated guided vehicles (AGVs). Most studies on human factors in order picking using AGVs have focused on the physical workload (PWL) of the worker, called the picker, but it is also imp...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2025.2491170 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850032221557096448 |
|---|---|
| author | Yurika Ono Aya Ishigaki Seiko Taki Aya Saito |
| author_facet | Yurika Ono Aya Ishigaki Seiko Taki Aya Saito |
| author_sort | Yurika Ono |
| collection | DOAJ |
| description | Recently, order picking, which is the most labour-intensive task in warehouse, has promoted the introduction of automated guided vehicles (AGVs). Most studies on human factors in order picking using AGVs have focused on the physical workload (PWL) of the worker, called the picker, but it is also important to consider mental workload (MWL) and individual differences. In this paper, we propose a Bayesian network (BN) considering individual differences for estimating the picker’s burden factor by conducting a replicated experiment of order picking with AGVs installed. BN is constructed from task data, heart rate variability indices, questionnaire data for PWL and MWL (the NASA task load index, Borg Scale CR10). To evaluate the performance of BN, the classification accuracy of Physical demand and Rate of Perceived Exertion are calculated and compared with three machine learning (random forest. neural network, support vector machine). The results indicate that the classification accuracy of BN is comparable to or higher than that of the comparison methods. Additionally, the BN demonstrates a highly interpretable and flexible model for uncertain data, such as HRV indicators and questionnaire data with individual differences. |
| format | Article |
| id | doaj-art-572ec13d0f8c4d3eb5779d4d71fe6ae9 |
| institution | DOAJ |
| issn | 1884-9970 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj-art-572ec13d0f8c4d3eb5779d4d71fe6ae92025-08-20T02:58:43ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702025-12-0118110.1080/18824889.2025.24911702491170Bayesian network for order picker burden factor estimation considering individual differencesYurika Ono0Aya Ishigaki1Seiko Taki2Aya Saito3Tokyo University of ScienceTokyo University of ScienceChiba Institute of TechnologyTokyo City UniversityRecently, order picking, which is the most labour-intensive task in warehouse, has promoted the introduction of automated guided vehicles (AGVs). Most studies on human factors in order picking using AGVs have focused on the physical workload (PWL) of the worker, called the picker, but it is also important to consider mental workload (MWL) and individual differences. In this paper, we propose a Bayesian network (BN) considering individual differences for estimating the picker’s burden factor by conducting a replicated experiment of order picking with AGVs installed. BN is constructed from task data, heart rate variability indices, questionnaire data for PWL and MWL (the NASA task load index, Borg Scale CR10). To evaluate the performance of BN, the classification accuracy of Physical demand and Rate of Perceived Exertion are calculated and compared with three machine learning (random forest. neural network, support vector machine). The results indicate that the classification accuracy of BN is comparable to or higher than that of the comparison methods. Additionally, the BN demonstrates a highly interpretable and flexible model for uncertain data, such as HRV indicators and questionnaire data with individual differences.http://dx.doi.org/10.1080/18824889.2025.2491170order pickingautomated guided vehiclenasa task load indexheart rate variability analysisindividual difference |
| spellingShingle | Yurika Ono Aya Ishigaki Seiko Taki Aya Saito Bayesian network for order picker burden factor estimation considering individual differences SICE Journal of Control, Measurement, and System Integration order picking automated guided vehicle nasa task load index heart rate variability analysis individual difference |
| title | Bayesian network for order picker burden factor estimation considering individual differences |
| title_full | Bayesian network for order picker burden factor estimation considering individual differences |
| title_fullStr | Bayesian network for order picker burden factor estimation considering individual differences |
| title_full_unstemmed | Bayesian network for order picker burden factor estimation considering individual differences |
| title_short | Bayesian network for order picker burden factor estimation considering individual differences |
| title_sort | bayesian network for order picker burden factor estimation considering individual differences |
| topic | order picking automated guided vehicle nasa task load index heart rate variability analysis individual difference |
| url | http://dx.doi.org/10.1080/18824889.2025.2491170 |
| work_keys_str_mv | AT yurikaono bayesiannetworkfororderpickerburdenfactorestimationconsideringindividualdifferences AT ayaishigaki bayesiannetworkfororderpickerburdenfactorestimationconsideringindividualdifferences AT seikotaki bayesiannetworkfororderpickerburdenfactorestimationconsideringindividualdifferences AT ayasaito bayesiannetworkfororderpickerburdenfactorestimationconsideringindividualdifferences |