Rough sets and maintenance in a production line

: The great volume of information used/obtained in industry nowadays produces the need for systems that are helpful in decision-making. An area of special interest, because of its influence on productive capacity, is maintenance, where the great quantity of influential variations and available facts (including real time) make the analysis almost impossible. Using the concept of rough sets, we present a comprehensive (but real) application where the information system is reduced so as to get a minimum subset of attributes without loss of quality; a set of nine decision rules is obtained.

[1]  Z. Pawlak.  Rough set approach to knowledge-based decision support , 1997 .

[2]  A. Dempster.  UPPER AND LOWER PROBABILITY INFERENCES FOR FAMILIES OF HYPOTHESES WITH MONOTONE DENSITY RATIOS , 1969 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[4]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[5]  John Crocker,et al.  Effectiveness of maintenance , 1999 .

[6]  Roman Slowinski,et al.  'Roughdas' and 'Roughclass' Software Implementations of the Rough Sets Approach , 1992, Intelligent Decision Support.

[7]  David Sherwin,et al.  A review of overall models for maintenance management , 2000 .

[8]  Ashraf Labib,et al.  An effective maintenance system using the analytic hierarchy process , 1998 .

[9]  Zahir Irani,et al.  A framework for managing maintenance using performance measurement systems , 2001 .

[10]  Fredrik Backlund,et al.  Can we make maintenance decisions on risk analysis results , 2002 .

[11]  Ramin Yasdi,et al.  Learning Classification Rules from Database in the Context of Knowledge Acquisition and Representation , 1991, IEEE Trans. Knowl. Data Eng..

[12]  Salvatore Greco,et al.  Rough sets methodology for sorting problems in presence of multiple attributes and criteria , 2002, Eur. J. Oper. Res..

[13]  Wojciech Ziarko.  Acquisition of Control Algorithms from Operation Data , 1992, Intelligent Decision Support.

[14]  Xiaohua Hu.  Knowledge discovery in databases: an attribute-oriented rough set approach , 1996 .

[15]  Jerzy W. Grzymala-Busse,et al.  LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.

[16]  Bernard Roy,et al.  Decision science or decision-aid science? , 1993 .

[17]  Jean-Charles Pomerol,et al.  Artificial intelligence and human decision making , 1997 .

[18]  C. Madu.  Competing through maintenance strategies , 2000 .

[19]  Albert H. C. Tsang,et al.  Condition-based maintenance: tools and decision making , 1995 .

[20]  Z. Pawlak,et al.  Decision analysis using rough sets , 1994 .

[21]  Chen-Hua Chung,et al.  Integrating maintenance and production decisions in a hierarchical production planning environment , 1999, Comput. Oper. Res..

[22]  Salih O. Duffuaa,et al.  Mathematical programming approaches for the management of maintenance planning and scheduling , 1997 .

[23]  Francis Eng Hock Tay,et al.  Economic and financial prediction using rough sets model , 2002, Eur. J. Oper. Res..

[24]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[25]  Albert H. C. Tsang,et al.  A strategic approach to managing maintenance performance , 1998 .

[26]  Tzung-Pei Hong,et al.  Learning rules from incomplete training examples by rough sets , 2002, Expert Syst. Appl..