Explanation
Decision Mathematics: Models for Prediction and Optimization Not so long ago, managers faced with difficult problems made decisions based on experience, intuition, and a little luck. But now there is a better way. In recent decades, mathematics and computer science have perfected previously secret techniques for predicting the best possible outcomes when faced with conflicting choices. This field goes by different names – analytics, operations research, direct and indirect programming, management science – but its purpose is simple: to use quantitative methods to help business managers, public servants, investors, researchers -scientific researchers, and problem solvers of all kinds for good decisions.
These same techniques can be applied to retirement planning, stock portfolio analysis, budget forecasting, health care allocations, public relations, marketing and advertising, and many other activities for large and small companies. The applications are truly endless! Mathematical decision-making received its first serious tests during World War II, when the Allies used secret research in secret operations to protect convoys, improve anti-aircraft fire targeting, and to find the weak points of Allied bombing. After the war, the private industry enthusiastically took up operations research, but these new methods were expensive, the speed of calculation was slow, and only specialists could benefit from algorithms. That situation has changed dramatically, and today anyone with a home computer and a math program can take advantage of the power of these methods to solve practical problems. The trick is knowing what to do and how to do it.
What will you learn?
- Cost: Costco has risen to become one of the top retailers in the world by combining membership fees and wholesale financing. A mathematical technique—known as genetic algorithms—shows the benefits of this strategy as well as the best price to charge.
- Scheduling: Using non-linear programming, many companies employ scheduling software that can find the perfect solution to unexpected disruptions – from weather to technical problems to employee availability – saving millions of dollars in operating costs.
- Contracts: Simulation models can take many dimensions out of the bidding process of a project. By running repeated examples against competitors, a competitor can come up with a proposal that has a better chance of winning the job, while still making a profit.
- Queuing: Any system that reflects the behavior of a waiting line is called a queue. Markov analysis shows how small increases in system input can have a large impact on waiting times. The process also reveals surprising solutions for making long waits disappear.
Who is this course for?
- managers committed to making better decisions – whether in business, technical, or non-profit endeavors;
- professionals who want to improve their skills by mastering a proven method of problem solving;
- those who work with or review spreadsheets and graphical presentations, and need to be able to distinguish between good and bad data;
- students in business, mathematics, finance, marketing, health care, engineering, urban planning, and a host of other subjects;
- Mathematical enthusiasts are interested in seeing a field that is often the opposite of calculus: simple functions and complex boundary conditions, rather than complex functions with simple boundary conditions; and
- Lifelong learners who want to improve their critical thinking skills with critical analytical techniques, made more accessible and intellectually engaging than ever before.
Specialties in Mathematical Decision Making: Models of Prediction and Optimization
- Publisher: TTC
- Teacher: Scott P Stevens
- Language : English
- Level : All Levels
- Number of courses: 24
- Duration: 12 hours and 29 minutes
Concepts of Mathematical Decision: Models of Prediction and Optimization
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