Development of a regression-based method with case-based tuning to solve the due date assignment problem

D. Y. Sha, R. L. Storch and C.-H. Liu

International Journal of Production Research, 2007, vol. 45, issue 1, 65-82

Abstract: Many regression-based methods to date have been proposed for solving the due date assignment (DDA) problem. The advantages of regression-based DDA methods are that they are easy to both put into practice and comprehend. However, relatively little scheduling research has focused on improving the performances of regression-based DDA methods. The performance of a regression-based DDA method could be improved if its values of regression coefficients could provide a more accurate and precise flowtime estimation for each individual job. The difficulty in doing this stems from the dynamic and stochastic nature of production environment that precludes accurate estimation. Therefore, the aim of this study is to suggest a particular methodology for setting the regression coefficients to improve the performance of regression-based DDA method. In particular, the regression-based DDA method achieved by our suggested methodology is able to adjust the values of coefficients dynamically to best predict the job due date based on the condition of the shop at the instant of job entry. To evaluate the robustness of the methodology, an experimental design was used with four regression coefficient determining procedures, two shop models, and three dispatching rules. The results of this investigation clearly indicate that significant improvements in the performance of regression-based DDA method can occur when the suggested methodology is used.

Date: 2007
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207540500507435 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:45:y:2007:i:1:p:65-82

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207540500507435

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().