Volume 7, Issue 2, April 2019, Page: 15-20
On Technique for Generating Pareto Optimal Solutions of Multi-objective Linear Programming Problems
Effanga Effanga Okon, Department of Statistics, University of Calabar, Calabar, Cross River State, Nigeria
Edwin Frank Nsien, Department of Mathematics/Statistics, University of Uyo, Uyo, Akwa Ibom State, Nigeria
Received: Apr. 1, 2019;       Accepted: May 23, 2019;       Published: Jun. 10, 2019
DOI: 10.11648/j.sjams.20190702.12      View  402      Downloads  69
Subjective selection of weights in method of combining objective functions in a multi – objective programming problem may favour some objective functions and thus suppressing the impact of others in the overall analysis of the system. It may not be possible to generate all possible Pareto optimal solution as required in some cases. In this paper we develop a technique for selecting weights for converting a multi-objective linear programming problem into a single objective linear programming problem. The weights selected by our technique do not require interaction with the decision makers as is commonly the case. Also, we develop a technique to generate all possible Pareto optimal solutions in a multi-objective linear programming problem. Our technique is illustrated with two and three objective function problems.
Multi-objective, Single Objective, Linear Programming, Pareto Optimal Solution, Weight, Non-inferior Solution
To cite this article
Effanga Effanga Okon, Edwin Frank Nsien, On Technique for Generating Pareto Optimal Solutions of Multi-objective Linear Programming Problems, Science Journal of Applied Mathematics and Statistics. Vol. 7, No. 2, 2019, pp. 15-20. doi: 10.11648/j.sjams.20190702.12
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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