The time required for a certain task to be performed normally reduces on its frequent completion, as more units are produced over time, it is expected to have an increase in the total worker’s output performance. Learning curve (LC) is a mathematical representation to estimate the time of tasks which occurs repeatedly. The parameter prediction is considered a major disadvantage from which LC suffers. The purpose of this paper is to investigate grey systems theory as a method for the standard time.
The proposed method starts with data which are obtained by traditional time study and then, models LC for an assembling activity of Electrogen Company. The paper studies the grey evaluation method based on triangular whitenization weight functions which includes two classes: endpoint triangular whitenization functions and center-point triangular whitenization functions. The grey system results are compared with those of the LC.
The results show that the standard time given by grey systems theory is closer than the standard time given by LC to standard time with 100 per cent performance level.
Scheduling problems are complex and uncertain, and it is very rare for such systems to be exactly determined in all their complexity. According to grey systems theory, the job processing time can be considered as the object that extension is definite but intension is uncertain. Consequently, grey systems theory with its focus on the uncertainty problems of small samples and incomplete information is proposed in the paper.
The authors wish to express the gratitude to Dr Sajjad Zahir for providing the Grey Systems: Theory and Applications book for this research. The authors are also grateful to Mehrdad Hoseinpoor for English proofreading, the guest editors and the anonymous reviewers of Journal of Manufacturing Technology Management for their constructive comments that helped to improve the paper.
Maleki, H. and Taghavi Fard, M.T. (2015), "Evaluation of scheduling using triangular whitenization functions", Journal of Manufacturing Technology Management, Vol. 26 No. 2, pp. 297-310. https://doi.org/10.1108/JMTM-09-2013-0124
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