Optimization is a part of life. The evolution process in nature reveals that it follows optimization. For example, animals that live in colder climates have smaller limbs than the animals living in hotter climates, to provide a minimum surface area to volume ratio. In our day to day lives we make decisions that we believe can maximize or minimize our set of objectives, such as taking a shortcut to minimize the time required to reach a particular destination, finding the best possible house which can satisfy maximum conditions within cost constraints, or finding a lowest priced item in the store. Most of these decisions are based on our years of knowledge of the system without resorting to any systematic mathematical theory. However, as the system becomes more complicated involving more and more decisions to be made simultaneously and becoming constrained by various factors, some of which are new to the system, it is difficult to take optimal decisions based on a heuristic and previous knowledge. Further, many times the stakes are high and there are multiple stake holders to be satisfied. Mathematical optimization theory provides a better alternative for decision making in these situations provided one can represent the decisions and the system mathematically.
KeywordsObjective Function Decision Variable Feasible Region Numerical Optimization Optimization Software
Unable to display preview. Download preview PDF.
- 1.Beale E. M. (1977), Integer Programming: The State of the Art in Numerical Analysis, Academic Press, London.Google Scholar
- 2.Biegler L., I. E. Grossmann, and A. W. Westerberg (1997), Systematic Methods of Chemical Process Design, Prentice Hall International, Upper Saddle River, NJ.Google Scholar
- 5.Carter M. W. and C. C. Price (2001), Operations Research: A Practical Introduction, CRC Press, New York, NY.Google Scholar
- 6.Diwekar U. M. (1995), Batch Distillation: Simulation, Optimal Design and Control, Taylor & Francis International Publishers, Washington D.C.Google Scholar
- 9.Reklaitis R., A. Ravindran, and K. M. Ragsdell (1983), Engineering Optimization, John Wiley & Sons, New York, NY.Google Scholar