Discrete event simulation is generally recognized as a valuable aid to the strategic and tactical decision making that is required in the evaluation stage of the manufacturing systems design and redesign processes. It is common practice to represent workers within these simulation models as simple resources, often using deterministic performance values derived from time studies. This form of representing the factory worker ignores the potentially large effect that human performance variation can have on system performance, and it particularly affects the predictive capability of simulation models with a high proportion of manual tasks. The intentions of the chapter are twofold: firstly, to raise awareness of the importance of considering human performance variation in such simulation models; and secondly, to present some conceptual ideas for developing a worker agent for representing worker performance in manufacturing systems simulation models.
Key Terms in this Chapter
Indirect Performance Indicators: Indicators that measure how the system affects the workers, which in return might have an effect on the performance of the individual worker. Typical indicators are absenteeism (absence from the workplace for any reason other than official leave or those covered by collective agreements), accident rate (an indication of how safely the worker conducts his or her work), and staff turnover (the number of employees starting or finishing employment at a particular place of work over a given period).
Time and Motion Study: An analysis applied to a job or number of jobs to check the efficiency of the work method, the equipment used, and the worker. Each operation is studied minutely and analyzed in order to eliminate unnecessary motions and thus reduce production time and raise output, which increases productivity.
Discrete Event Simulation (DES): Modeling of a real system as it evolves over time by representing the changes as separate events, for the purpose of better understanding and/or improving that system.
Direct Performance Indicators: Indicators that measure how the individual worker affects the system. Typical indicators are activity time (the actual time it takes a worker to complete a task that is usually a repetitive cycle), error rate (an indication of how well a worker conducts a task), and dependability (given that all conditions for a task to commence are met, when does the operator start the activity in response to a request?).
Worker Performance Modeling (WPM): Modeling of the processes and effects of human behavior within a working environment.
Artificial White Room: Simulation of a laboratory as it is used by social scientists for data gathering under controlled conditions.
KISS (Keep It Simple, Stupid) Principle: A popular maxim often invoked when discussing a design process as a reminder to avoid the unnecessary complexity that can arise during the design process.
Direct Workers: Factory workers dedicated to predominately manual routines.
Human Performance Variation (HPV): The variation in the time taken to complete a task by a direct worker under normal working conditions.
Agent-Based Modeling: In the context of this chapter, this is a bottom-up approach that allows the behavior of human beings to be captured in a more realistic fashion. The artificial agents acting as representatives for real factory workers have to be designed to mimic the attributes and behaviors of their real-world counterparts as similarly as possible. The system’s macro-observable properties emerge as a consequence of these attributes and behaviors, and the interactions between them.
Complete Chapter List
P. Collet, J. Rennard
I. Naveh, R. Sun
J. Barr, F. Saraceno
H. Kwasnicka, W. Kwasnicki
A. Berro, I. leroux
N. J. Saam, W. Kerber
A. Brabazon, A. Silva, T. F.S. Sousa, R. Matthews, M. O’Neill
G. D.M. Serugendo
K. Taveter, G. Wagner
L. Shan, R. Shen, J. Wang
M. Klein, P. Faratin, H. Sayama
A. Mochon, Y. Saez
R. Marks, D. Midgley, L. Cooper
T. Erez, S. Moldovan, Soloman
M. Ciprian, M. Kaucic
S. Lavigne, S. Sanchez