Identification and incorporation of human factors into a discrete event simulation model for human centred assembly performance evaluation

Abstract

One of the most commonly faced challenges to manufacturing industry today is that consumers increasingly require a great variety of products, which are coupled with the intrinsic demand of short lead-time, high quality and low costs. Consequently, manufacturing companies need consistent improvements of their production systems in terms of flexibility and responsiveness to accommodate unpredictable circumstances from the competitive market. A human centred assembly system, as an example, may have these characteristics as it offers good flexibility and responsiveness due to inherent human intelligence and problem-solving abilities. In addition, human-centred manufacturing systems can provide such a capability in dealing with product variations and production volumes by adapting themselves to perform multiple tasks after a learning process. Nevertheless, human performance can also be unpredictable, and it may alter over time due to varying psychological and physiological states, which are often overlooked by researchers when designing, implementing or evaluating a manufacturing system. This study aims to address these issues by exploring human factors and their interactions that may affect human performance on human-centred assembly systems. Through a literature study, although there were some investigations into human factors relating to human performance, there were a few studies by examining human factors and their interactions on human performance of a human-centred assembly system. This thesis presents a report by identifying the relevant human factors that may have impacts on human-centred assembly based on findings of a comprehensive literature review and an industrial survey. This includes alternative methods used for quantifying the influential levels between one of key human factors and one of key performance indicators. The research findings through the literature study conclude that experience is the most significant human factor that affects individual human performance, compared to age and general cognitive abilities in human-centred assembly. By contrast, both human reaction time and job satisfaction have the least effect on human performance, a framework of a user-friendly integrated DES (discrete event simulation) tool that allows manufacturing system designers to examine the overall performance of a human centred system with considerations of effects of selected human factors was developed, since the existing DES tools do not have functionalities that enable incorporating parameters of human factors into established DES models for manufacturing systems design and evaluation.

Date of Award	Jul 2019
Original language	English
Awarding Institution	University of Portsmouth
Supervisor	Qian Wang (Supervisor)