Volume : 12, Issue : 6, JUN 2026

FUZZY ASSIGNMENT PROBLEM IN REAL LIFE

SANTOSH GUPTA, DR. VINOD KUMAR SHARMA

Abstract

The Fuzzy Assignment Problem (FAP) has become an important optimisation technique for solving real-world decision-making problems characterised by uncertainty, vagueness, and incomplete information. Unlike the classical assignment problem, which assumes precise, deterministic cost values, the fuzzy assignment problem incorporates fuzzy set theory to represent uncertain costs, processing times, resource availability, and human judgments using fuzzy numbers. This capability makes FAP a more realistic and effective model for complex environments where exact numerical information is difficult to obtain.

This article presents a comprehensive study of the applications of the Fuzzy Assignment Problem across various real-life domains. The study discusses how fuzzy modelling improves the quality of decision-making by handling ambiguity and uncertainty more effectively than traditional optimization methods.

Article : Download PDF

Cite This Article

IESRJ

International Educational Scientific Research Journal

E-ISSN: 2455-295X

International Indexed Journal | Multi-Disciplinary Refereed Research Journal

ISSN: 2455-295X

Peer-Reviewed Journal - Equivalent to UGC Approved Journal

Peer-Reviewed Journal

Article No : 13

Number of Downloads : 21

References

1. Chanas, S. and Kobylanski, P. (1996). 'A new approach to the fuzzy linear programming problem.' Fuzzy Sets and Systems, 82(2), pp. 193–198.

2. Dubois, D. and Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. New York: Plenum Press.

3. Golde, C. M. (2005). 'The role of the department and discipline in doctoral student attrition: Lessons from four departments.' Journal of Higher Education, 76(6), pp. 669–700.

4. Ishibuchi, H. and Tanaka, H. (1990). 'Multiobjective programming in optimization of the interval objective function.' European Journal of Operational Research, 48(2), pp. 219–225.

5. Jasemi, M. and Ahmadi, E. (2018). 'A new fuzzy ELECTRE-based multiple criteria method for personnel selection.' Scientia Iranica, 25(2), pp. 943–953.

6. Kuchta, D. (2000). 'Fuzzy capital budgeting.' Fuzzy Sets and Systems, 111(3), pp. 367–385.

7. Mukherjee, S. and Basu, K. (2010). 'Application of fuzzy ranking method for solving assignment problems with fuzzy costs.' International Journal of Computational and Applied Mathematics, 5(3), pp. 359–368.

8. Teodorovic, D. and Pavkovic, G. (1992). 'A simulated annealing technique approach to the vehicle routing problem in the case of stochastic demand.' Transportation Planning and Technology, 16(4), pp. 261–273.

9. Yager, R. R. (1981). 'A procedure for ordering fuzzy subsets of the unit interval.' Information Sciences, 24(2), pp. 143–161.

10. Zadeh, L. A. (1965). 'Fuzzy sets.' Information and Control, 8(3), pp. 338–353.

11. Zheng, Y. and Liu, B. (2006). 'Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm.' Applied Mathematics and Computation, 176(2), pp. 673–683.