Advanced Project Portfolio Selection Methods
Download our report to learn the state of the art technique of simulation optimization for effective capital budget decision making.
An Enhanced Approach for Optimal Capital Budgeting
In all areas of business, determining how to allocate investment capital to maximize returns is a ubiquitous challenge, where approaches to solutions cover a very wide spectrum. Portfolio optimization for capital investment is often too complex to allow for tractable mathematical formulations. Nonetheless, many analysts force these problems into standard forms that can utilize traditional optimization technologies including linear and quadratic programming. Unfortunately, such formulations omit key aspects of real world settings, thus resulting in flawed solutions. Simulation optimization offers a flexible modeling and solution approach that overcomes these limitations.
This paper begins with a discussion of capital investment decision making using traditional tools and methodologies such as net present value (NPV) analysis. Next follows an overview of the much-improved technique of optimization technology to select the best possible project mix for a given capital budget. The paper concludes with an illustrative project portfolio optimization example in the Petroleum and Energy industry.
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More Optimization White Papers
Tabu Search, also called Adaptive Memory Programming, is a method for solving challenging optimization problems in the fields of business, engineering, economics and science. Everyday examples include practical applications in resource management, financial and investment planning, healthcare systems, energy and environmental policy, pattern classification, biotechnology and a host of other areas. Tabu search has emerged as one of the leading technologies for handling real-world problems that have proved difficult or impossible to solve with classical procedures.
Optimizing AI/ML Hyperparameters with SimWrapper and OptQuest
Download our report to see how SimWrapper and OptQuest can be used to optimally tune hyperparameters for your AI/ML models.