A hybrid algorithm based on fuzzy linear regression analysis by quadratic programming for time estimation: An experimental study in manufacturing industry

Atalay K. D. , Eraslan E. , Cinar M. O.

JOURNAL OF MANUFACTURING SYSTEMS, vol.36, pp.182-188, 2015 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36
  • Publication Date: 2015
  • Doi Number: 10.1016/j.jmsy.2014.06.005
  • Page Numbers: pp.182-188


In time studies, estimation of the standard times with direct or indirect measurement methods is particularly difficult in companies having complex production schedules or ones employing an inexperienced workforce. Such companies require new and specific time measurement procedures. In this study, a new time estimation algorithm based on fuzzy linear regression analysis (FLRA) by quadratic programming (QP) is proposed for specific manufacturing systems. In our study, data is provided by one of the biggest casting and machining companies in Europe. The database includes items that have similar production processes. A fuzzy linear regression model is built by using the previously measured standard times of a product family. The model developed is used for estimating the standard times of the remaining products. FLRA based on QP approach facilitates integration of the central tendency of least squares and possible properties of fuzzy regression. The main factors that directly impact standard times are determined and used for the estimation of the fuzzy standard times. Through utilization of sum of squares error (SSE) and index of confidence (IC), the important factors in the model are identified. The use of QP makes it possible to reconcile the minimization of the deviation of central tendency and the spreads of membership functions in a simultaneous manner. In this study, the efficiency of the proposed algorithms in casting companies is authenticated. Besides, it is seen that this estimation procedure could be implemented easily for various sectors using the relevant algorithms. (C) 2014 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.