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Why Optimization?
Industrial AI Optimization Lab
HOME > Introduction > Why Optimization?
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 Definition of optimization in Wikipedia | | Operations Research (OR) is an interdisciplinary branch of mathematics which uses methods like mathematical modeling, statistics, and algorithms to arrive at optimal or good decisions in complex problems which are concerned with optimizing the maxima (profit, faster assembly line, greater crop yield, higher bandwidth, etc) or minima (cost loss, lowering of risk, etc) of some objective function. | | | | | Where is it from? | | During World War II, scientists in the United Kingdom and the United States looked for ways to make better decisions in such areas as logistics and training schedules. After the war it began to be applied to similar problems in industry and was established as a discipline in academic area. | | | | | Why do we need optimization? | | These days it is necessary to efficiently resolve decision making problems occurred in various areas of production, transportation, and telecommunication, etc., where economic situation is changed drastically. However, there are many constraints technically, meaning relationship among tasks under consideration, and physically, meaning finite capacities of resources available. Furthermore, there might be many possible alternatives to be selected for attacking the decision problems. Therefore, it becomes very important to decide how to model, analyze, and solve it, eventually generating the best one to achieve our final goal. Optimization can provide the systematic approach for this purpose as the key for generating new value by realizing efficient and effective use of our limited resources. | | | | | Example applications of Optimization |  Designing the layout of a factory for efficient flow of materials
Constructing a telecommunications network at low cost
Determining the routes of school buses (or city buses) so that few buses are needed as possible
Managing the flow of raw materials and products in a supply chain based on uncertain demand for the finished products
Managing freight transportation and delivery systems
Scheduling:  manufacturing steps
project tasks
network data traffic: these are known as queuing models or queuing systems. | Blending of raw materials in oil refineries, and so on | | | |
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