Today in the increasingly competitive market, consumers prefer to have a great variety of products to choose from; this preference is often coupled with demands for a relatively smaller lot size, shorter lead time, higher quality and lower cost. Consequently, manufacturing companies are being forced to consistently increase flexibility and responsiveness of their production systems in order to accommodate changes of the fluctuating market. Among various forms of production systems, human-centred manufacturing systems can offer such a capability in dealing with product variations and production volumes as human workers can always adapt themselves to perform multiple tasks after a learning process. However, human performance can also be unpredictable and it may alter due to varying psychological and physiological states, which are often overlooked by researchers when designing, implementing or evaluating a manufacturing system. This paper presents a study aiming to address these issues by exploring human factors and their interactions that may affect human performance on human-centred assembly systems. The study was carried out based on a literature review and an industrial survey. Critical system performance indicators, which are affected by human factors, were evaluated and the most significant human factors were identified using the fuzzy extent analysis method. The research findings show that experience is the most significant human factor that affects individual human performance, compared to age and general cognitive abilities in humancentred assembly. By contrast, both human reaction time and job satisfaction have the least effect on human performance. The significance of ageing on human performance was also studied and it was concluded that average assembly time of human workers rises by average 1% per year after the age of 38 years old.
In manufacturing plants, part of assembly is often performed by human workers and therefore its performance largely depends on humans (or human performances) rather than machines. However, most studies on human centred assembly systems using the modelling simulation methods do not consider or incorporate the effects of human performance that may also impact the overall system performance of such as an assembly line. The prediction of human performance or behaviour in a manufacturing system evaluation is often overlooked by researchers or system designers partially due to a lack of proper versions of existing simulation tools that can incorporate human attributes into an established simulation model. This paper presents a study by incorporating human attributes of learning and ageing into a discrete event simulation (DES) model based on a human centred assembly system. The effects of worker performance due to aging and learning were investigated and identified through a literature review [1]. The simulation result demonstrates that the worker performance may approach his/her full capacity at the age of 38 years old and this may decrease by an average of 1% from 38 to 40 years old, and 6% at the age of 45 years old. After the age of 45 years old, the decline rate of worker performance may slow down as it can be offset by accumulating worker experience through learning.
Increasing demand for health care coupled with resource constraints may lead to overcrowding in health care center. Generally, patient flow into health care center is characterized by variability due to its stochastic behavior. In recent years, there has been increasing interest in Discrete Event Simulation (DES) technique in healthcare systems due to its sensitivity to systems’ variability. Discrete Event Simulation (DES) technique is used in this study to examine patient flow in an urban health care center, the General Hospital Hadejia (GHH), with a view to analyzing resource utilization, minimizing waiting time, and improving efficiency and effectiveness. Data relating to waiting time, service time, and movements of patients between departments within the health center were collected and analyzed. The influence of operational policies on the efficient movement of patients during peak and off-peak demand periods is studied. Results of the discrete event simulation experiment show an average increase of 41% of patients’ visits to the hospital when the existing layout of the hospital is modified.
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