EconPapers    
Economics at your fingertips  
 

Solution approach to multi-objective linear fractional programming problem using parametric functions

Suvasis Nayak () and Akshay Kumar Ojha
Additional contact information
Suvasis Nayak: Indian Institute of Technology Bhubaneswar
Akshay Kumar Ojha: Indian Institute of Technology Bhubaneswar

OPSEARCH, 2019, vol. 56, issue 1, No 8, 174-190

Abstract: Abstract In this paper, an iterative technique based on the use of parametric functions is proposed to obtain the best preferred optimal solution of a multi-objective linear fractional programming problem. The decision maker ascertains own desired tolerance values for the objectives as termination constants and imposes them on each iteratively computed objective functions in terms of termination conditions. Each fractional objective is transformed into non-fractional parametric function using certain initial values of parameters. The parametric values are iteratively computed and $$\epsilon $$ ϵ -constraint method is used to obtain the pareto (weakly) optimal solutions in each step. The computations get terminated when all the termination conditions are satisfied at a pareto optimal solution of an iterative step. A numerical example is discussed at the end to illustrate the proposed method and fuzzy max–min operator method is applied to validate the obtained results.

Keywords: Multi-objective LFPP; Pareto optimal solution; $$\epsilon $$ ϵ -Constraint method; Parametric function; Fuzzy programming (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s12597-018-00351-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:opsear:v:56:y:2019:i:1:d:10.1007_s12597-018-00351-2

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/12597

DOI: 10.1007/s12597-018-00351-2

Access Statistics for this article

OPSEARCH is currently edited by Birendra Mandal

More articles in OPSEARCH from Springer, Operational Research Society of India
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:opsear:v:56:y:2019:i:1:d:10.1007_s12597-018-00351-2