What is optimization?
Optimization problems are made up of three basic ingredients: • An objective function which we want to minimize or maximize. For instance, in a manufacturing process, we might want to maximize the profit or minimize the cost. In fitting experimental data to a user-defined model, we might minimize the total deviation of observed data from predictions based on the model. In designing an automobile panel, we might want to maximize the strength. • A set of unknowns or variables which affect the value of the objective function. In the manufacturing problem, the variables might include the amounts of different resources used or the time spent on each activity. In fitting-the-data problem, the unknowns are the parameters that define the model. In the panel design problem, the variables used define the shape and dimensions of the panel. • A set of constraints that allow the unknowns to take on certain values but exclude others. For the manufacturing problem, it does not make sense to spend a n
Optimization is a mathematical discipline that concerns the finding of minima and maxima of functions, subject to so-called constraints. Optimization originated in the 1940s, when George Dantzig used mathematical techniques for generating “programs” (training timetables and schedules) for military application. Since then, his “linear programming” techniques and their descendents were applied to a wide variety of problems, from the scheduling of production facilities, to yield management in airlines. Today, optimization comprises a wide variety of techniques from Operations Research, artificial intelligence and computer science, and is used to improve business processes in practically all industries. Discrete optimization problems arise, when the variables occurring in the optimization function can take only a finite number of discrete values. For example, the staff scheduler of a hospital unit has a finite set of staff members available, and thus staff scheduling consists of taking dis
Optimization means “the action of finding the best solution”. Mathematical Programming, or optimization modelling, is a branch of mathematical modelling which is concerned with finding the best solution to a problem. The process of building a model and optimizing it is summarised in the diagram below: Building a mathematical model of a problem involves abstracting its essence and representing it as a series of mathematical relationships. The best solution to the mathematical model can then be found using appropriate optimization software. If the model has been built well, this solution will translate back into the real world as a good solution to the real-world problem. If it does not, analysis of why it is no good leads to greater understanding of the real-world problem. Optimization can help solve problems where there are: • many ways of doing something; • limited resources available. Typical industrial examples include planning and scheduling, blending problems and supply-chain opti
Search Engine Optimization means that your site will be analyzed and then improved to make it possible for Search Engines to index and rank it. If your web site does not rank well, optimization helps that your web site will be found in the first upcoming pages of the search results. This can vary from ranking very high in Google to good in Yahoo and excellent in MSN. We cannot predict in which Search Engine the best ranking will take place because they work in different ways.
