FUZZY METHOD FOR SOLVING MULTI-OBJECTIVE ASSIGNMENT PROBLEM WITH INTERVAL COST

Kagade K.L.1*, Bajaj V.H.2*
1Department of Statistics, Dr. B. A. M. University, Aurangabad, MS, 431004, India
2Department of Statistics, Dr. B. A. M. University, Aurangabad, MS, 431004, India
* Corresponding Author : vhbajaj@gmail.com

Received : -     Accepted : -     Published : 15-06-2010
Volume : 1     Issue : 1       Pages : 1 - 9
J Stat Math 1.1 (2010):1-9

Cite - MLA : Kagade K.L. and Bajaj V.H. "FUZZY METHOD FOR SOLVING MULTI-OBJECTIVE ASSIGNMENT PROBLEM WITH INTERVAL COST." Journal of Statistics and Mathematics 1.1 (2010):1-9.

Cite - APA : Kagade K.L., Bajaj V.H. (2010). FUZZY METHOD FOR SOLVING MULTI-OBJECTIVE ASSIGNMENT PROBLEM WITH INTERVAL COST. Journal of Statistics and Mathematics, 1 (1), 1-9.

Cite - Chicago : Kagade K.L. and Bajaj V.H. "FUZZY METHOD FOR SOLVING MULTI-OBJECTIVE ASSIGNMENT PROBLEM WITH INTERVAL COST." Journal of Statistics and Mathematics 1, no. 1 (2010):1-9.

Copyright : © 2010, Kagade K.L. and Bajaj V.H., Published by Bioinfo Publications. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

Abstract

This paper concentrates on the solution procedure of the Multi-Objective Assignment Problem (MOAP) where the cost coefficients of the objective functions have been expressed as interval values by the decision maker. This problem has been transformed into a classical MOAP where the interval objective function is minimized. The order relations that represent the decision maker’s preference between interval profits have been defined by the right limit, left limit, centre and half-width of an interval. Finally, the equivalent transformed problem has been solved by fuzzy programming techniques. Numerical example has been provided to illustrate the solution procedure.

References

[1] Bit A.K., Biswal M.P. and Alam S. S. (1992) Fuzzy sets and systems, 50, 135-141  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[2] Chanas S. and D. Kuchta (1996) European Journal of Operations Research, 94, 594-598.  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[3] Chanas S. and D. Kuchta (1996) Fuzzy Sets and Systems, 82, 299-305  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[4] Das S.K., A. Goswami and Alam S.S. (1999) European J. of Operations Research, 117, 100-112  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[5] Geetha S. and Nair K.P.K. (1993) European Journal of Operational Research, 68: 422-426.  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[6] Inuiguchi M., Ichihashi H. and Kume Y. (1990) Fuzzy Sets and Systems, 34, 15-31.  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[7] Inuiguchi M. and Kume Y. (1991) European Journal of Operations Research, 52, 345-360  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[8] Ishibuchi H., H. Tanaka. (1990) European Journal of Operations Research, 48, 219-225  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[9] Leberling H (1981) Fuzzy sets and systems.6, 105-118.  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[10] Ravindran A., Don T. Phillips and Tames J. Solberg. Operations Research: principles and practice. 2nd Edition, John Wiley and Sons,1987  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[11] Tong S (1994) Fuzzy Sets and Systems, 66, 301-306  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus  

[12] Tsai C.H., Wei C.C., and Cheng C.L. (1999) Int’l J. of the Computer, the Internet and Management, 7, 2.  
» CrossRef   » Google Scholar   » PubMed   » DOAJ   » CAS   » Scopus