• Urmila M. Diwekar
Part of the Applied Optimization book series (APOP, volume 80)


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.


Objective Function Decision Variable Feasible Region Numerical Optimization Optimization Software 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Urmila M. Diwekar
    • 1
  1. 1.Center for Uncertain Systems: Tools for Optimization & Management, Department of Chemical Engineering, and Institute for Environmental Science & PolicyUniversity of Illinois at ChicagoChicagoUSA

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