Frontline Systems Analytic Solver Platform 2016 x32-x64, (Mar 2016)
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Frontline Systems Analytic Solver Platform 2016 x32-x64, (Mar 2016)
Archive : Frontline Systems Analytic Solver Platform 2016 x32-x64, (Mar 2016)
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Optimization Tutorial
Solvers, or optimizers, are software tools that help users determine the best way to allocate scarce resources. Examples include allocating money to investments, or locating new warehouse facilities, or scheduling hospital operating rooms. In each case, multiple decisions need to be made in the best possible way while simultaneously satisfying a number of requirements (or constraints). The “best” or optimal solution might mean maximizing profits, minimizing costs, or achieving the best possible quality. Here are some representative examples of optimization problems:
- Finance/Investment: Cash management, capital budgeting, portfolio optimization.
- Manufacturing: Job shop scheduling, blending, cutting stock problems.
- Distribution and Networks: Routing, truck loading, fleet scheduling.
Simulation/Risk Analysis Tutorial
Quantitative risk analysis is the practice of creating a mathematical model of a project or process that explicitly includes uncertain parameters that we cannot control, and also decision variables that we can control. Monte Carlo simulation explores thousands of possible scenarios, and calculates the impact of the uncertain parameters and the decisions we make on outcomes that we care about — such as profit and loss, investment returns, environmental results and more. Industries where simulation and risk analysis are heavily used include:
- Pharmaceuticals: Modeling R&D and clinical trials
- Oil & Gas: Modeling drilling projects
- Insurance: Modeling frequency and types of claims
Data Mining Tutorial
Data mining software tools help users find patterns and hidden relationships in data, that can be used to predict behavior and make better business decisions. A machine learning algorithm “trained” on past observations can be used to predict the likelihood of future outcomes such as customer “churn'” or classify new transactions into categories such as “legitimate” or “suspicious”. Other methods can be used uncover “clusters” of similar observations, or find associations among different items. Common applications include:
- Financial Services: Fraud detection, good vs. bad credit risks
- Direct Marketing: Segmentation to improve response rates
- Electoral Politics: Identifying “most persuadable” voters
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