Products are usually made by accomplishing a series of manufacturing processes in a sequential flow line that is also known as a manufacturing system. Today, lean methods are widely adopted by many manufacturing plants as a popular model in designing, implementing, operating or managing a manufacturing system. It has been proved as a cost-effective approach to boost system efficiency and productivity by consistently seeking and removing any non-value added activities (i.e., wastes) during a production with a small or without any additional investment. Nevertheless, identification of these wastes using the traditional lean methods does not include such wastes as amounts of energy consumption and CO2 emissions. For human centered assembly lines, for instance, it is reported that applying highly skilled, flexible and dynamic workers into production lines is also a good practice for implementing a lean manufacturing system in which each worker performs multiple tasks amongst stations. On the other hand, most studies on manufacturing systems using the modelling simulation methods failed to consider parameters of energy consumption, CO2 emissions and human factors that may also impact the overall system performance. The simultaneous prediction, which relates to amounts of energy consumption and CO2 emissions and effects of human factors (or human performance) for a manufacturing system evaluation, is often overlooked by researchers or system designers partially due to a lack of existing DES (discrete event simulation) tools that enable incorporating these parameters into an established DES model. This paper presents a study by addressing these issues aiming to incorporate these missing parameters of energy consumption, CO2 emissions and human factors (age and experience) into a DES model.
|Name||IOP Conference Series: Materials Science and Engineering|
|Publisher||IOP Publishing Ltd.|
|Conference||3rd International Conference on Advanced Materials Research and Manufacturing Technologies|
|Abbreviated title||AMRMT 2018|
|Period||11/08/18 → 13/08/18|