Application of Linear Programming Techniques to Practical Decision Making

Kanu, Success Ikechi, Ozurumba Benedict. A, Emerole Ikechukwu.C.

Abstract


In spite of the overwhelming number of real life applications, linear programming techniques are yet to receive the desired level of recognition and acceptance. The general public does not seem to understand nor appreciate the contributions of operations research and its component studies to work place decision making process. It has become necessary to review some of these applications and to reconcile the seeming apathy towards linear programming techniques and the benefits derived therein. That is the main thrust of this study! As a way out of the woods, there is need to evolve a national policy that will make the teaching and learning of operations research and its applications a national priority. This should in the long run, inculcate a culture of bias towards quantitative thinking and its applications. The phobia associated with the study of mathematics and other related subjects is still far from being over. It is high time our tertiary institutions shy away from soft sell degree programs for more practically oriented courses. Presently, not many of our Nigerian universities run a degree program in operations research and decision sciences. The general public and more especially the private sector should invest in the study of operations research and its’ component studies in programming techniques. This will evolve a culture of record keeping and statistics, thereby reducing the drudgeries associated with ‘’ guess works ‘’ and going  for a more informed decision making process. Readers are enjoined not only to appreciate and study the concept of  linear programming techniques but also to apply this all important and time tasted problem solving approach that could enhance decision making of  managers, especially where there are  restrictions or constraints  in the decision making process.

Keywords: Objective function, Decision Variable, Parameters, Minimization, Maximization, Structured   Constraints, Optimal Solutions.


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ISSN (Paper)2224-5804 ISSN (Online)2225-0522

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