Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing.
Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing.
复制标题
在劳动力密集型制造中，使用工人位置数据进行人为驱动的决策支持。
DOI：
10.3390/s23104928
复制
发表时间：
2023-05-20
期刊：
影响因子：
--
通讯作者：
Sherlock A
中科院分区：
文献类型：
--
作者：
Aslan A;El-Raoui H;Hanson J;Vasantha G;Quigley J;Corney J;Sherlock A
本文为劳动密集型制造系统中的产能分配提供了一种新颖的人为决策支持方法。在这类系统中（产出完全依赖于人类劳动），任何旨在提高生产率的变革都必须以工人的实际工作实践为依据，而不是试图根据理论生产流程的理想化表示来实施战略。本文介绍了如何将工人位置数据（通过定位传感器获得）作为流程挖掘算法的输入，生成数据驱动的流程模型，以了解生产任务的实际执行情况，以及如何利用该模型建立离散事件模拟，以研究对数据中观察到的原始工作实践进行产能分配调整的性能。所提出的方法使用了一个由人工装配线生成的真实数据集，该数据集涉及六名工人执行六项生产任务。结果发现，通过小规模的产能调整，可以将完成时间缩短 7%（即不需要增加任何工人），而通过增加瓶颈任务的产能，则可以将完成时间缩短 16%，因为瓶颈任务比其他任务耗时相对较长。
This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others.
登录
查看更多内容
DOI：
10.1109/tim.2021.3074403
发表时间：
2021-01-01
影响因子：
5.6
作者：
Barbieri, Luca;Brambilla, Mattia;Nicoli, Monica
通讯作者：
Nicoli, Monica
影响因子：
3.9
作者：
Goel, Pankaj;Mehta, Sandhya;Castano, Fernando
通讯作者：
Castano, Fernando
影响因子：
7.9
作者：
Song, B. L.;Wong, W. K.;Chan, S. F.
通讯作者：
Chan, S. F.
DOI：
10.3390/s22082927
发表时间：
2022-04-11
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Crețu-Sîrcu AL;Schiøler H;Cederholm JP;Sîrcu I;Schjørring A;Larrad IR;Berardinelli G;Madsen O
通讯作者：
Madsen O
影响因子：
9.2
作者：
Chang, Ping-Chen;Lin, Yi-Kuei
通讯作者：
Lin, Yi-Kuei