Pareto optimal solution for multiobjective stochastic linear programming problems with partial uncertainty

Abdulqader Othman Hamadameen, Nasruddin Hassan

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A study on multiobjective stochastic linear programming (MSLP) problems with partial information on probability distribution is conducted. A method is proposed to utilise the concept of dominated solution for the multiobjective linear programming (MLP) problems, and find a pareto optimal solution (POS) without converting the MLP problem into its unique linear programming (LP) problem. An algorithm is proposed along with a numerical example which illustrated the practicability of the proposed algorithm. Comparison of results with existing methods shows the efficiency of the proposed method based on the analysis of results performed.

Original languageEnglish
Pages (from-to)139-166
Number of pages28
JournalInternational Journal of Mathematics in Operational Research
Volume12
Issue number2
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Pareto Optimal Solution
Linear programming
Multiobjective Linear Programming
Uncertainty
Partial
Partial Information
Probability distributions
Probability Distribution
Numerical Examples
Optimal solution

Keywords

  • Dominated solution
  • Fuzzy transformation
  • MSLP problems
  • Pareto optimal solution
  • POS
  • Stochastic transformation.

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modelling and Simulation

Cite this

Pareto optimal solution for multiobjective stochastic linear programming problems with partial uncertainty. / Hamadameen, Abdulqader Othman; Hassan, Nasruddin.

In: International Journal of Mathematics in Operational Research, Vol. 12, No. 2, 01.01.2018, p. 139-166.

Research output: Contribution to journalArticle

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