White Papers
These white papers provide an array of information and case examples regarding OptTek’s optimization engine, OptQuest. The papers contained herein were written by experts in the field of simulation optimization and range from academic to very practical in nature. Simply select those you are interested in reading and contact OptTek should you wish to gain further insights and expertise in the area of simulation optimization.
Pandemic: Decision Support for a Novel Program and Its Challenges
Policy makers face hard decisions with competing objectives. We show how OptQuest can be used in conjunction with your models to be a valuable tool to support decision making in a transparent and scientifically defensible way.
Optimizing AI/ML Hyperparameters with SimWrapper and OptQuest
Machine learning (ML) is an application of artificial intelligence (AI) that enables models to “learn” to perform tasks using data. Some applications of ML include regression, classification, image/speech recognition, forecasting, and decision making. Using ML techniques, models are developed, or “trained”, algorithmically through exposure to data.
Tabu Search
Tabu Search, also called Adaptive Memory Programming, is a method for solving challenging optimization problems in the fields of business, engineering, economics and science. 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.
Optimization withing Project Portfolio Selection: The Efficient Frontier Approach
Efficient Frontier Analysis traces its origins to Nobel Prize winner Harry Markowitz and his work related to modern portfolio theory. According to this theory, there is a trade-off between portfolio risk and portfolio return: the more risk an investor is willing to accept, the higher the expected return of investment.
Scenario-Based Risk Management and Simulation Optimization
Traditional optimization approaches for handling uncertainty and risk typically require severe assumptions that are often not satisfied in complex practical settings. In an effort to overcome such limitations, several methods have been developed to handle uncertainty when the data and associated real world parameters do not behave according to classical assumptions.
Metaheuristics
A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, and ant colony optimization, although many more exist.
OptQuest - Optimization of Complex Systems
Leveraging research from some of the top experts in the field, including Dr. Fred Glover and Professor Rafael Marti, Dr. Manuel Laguna discusses the technologies embedded in OptQuest, a commercial software for optimization of complex systems — such as those modeled as computer simulations. Dr. Laguna illustrates the system’s features with applications in stochastic, nonlinear and combinatorial optimization.
Simulation-Based Performance Analytics
Imagine a rapid modeling tool that not only lets you quickly test countless variations on a plan but also produces data on each alternative, allowing you to get a glimpse into the future of your project. Imagine software that answers the question: What can happen? More importantly: What will happen? With rapid modeling simulation tools, companies have the power to maximize business processes and decision effectiveness using simulation-based performance analytics.
Simulation Optimization: Applications in Risk Management
Simulation Optimization is providing solutions to important practical problems previously considered beyond reach. Recent advances in Simulation Optimization technology are leading to new opportunities to solve problems more effectively. Specifically, in applications involving risk and uncertainty, Simulation Optimization surpasses the capabilities of other optimization methods, not only in the quality of solutions, but also in their interpretability and practicality.
Advances in Analytics: Integrating Dynamic Data Mining with Simulation Optimization
This paper introduces a simulation optimization approach that is effective in guiding the search for optimal values of input parameters to a simulation model. Our recommended approach combines enhanced data mining methodology with state-of-the art optimization technology to produce the best possible results, particularly for business problems involving a large amount of data to be analyzed.
The Exploding Domain of Simulation Optimization
The merging of optimization and simulation techniques has seen remarkable growth in recent years. A Google search on “Simulation Optimization” returns many thousands of pages where this phrase occurs. The content of these pages ranges from articles, conference presentations and books to software, sponsored work and consultancy. This is an area that has sparked much interest in both the academic world and practical settings.
Simulation Optimization: A Review, New Developments and Applications
Advances in computing power and memory in recent years have increased the possibilities for optimizing simulation models. This phenomenon, and corresponding algorithmic and theoretical developments, offers one of the most exciting opportunities in simulation, paving the way for new and more powerful approaches for complex problem solving.
Selecting Project Portfolios by Optimizing Simulations
Recent advances in the area of simulation optimization allow managers to go beyond traditional methodologies in selecting optimal sets of projects to fund. These advances make use of portfolio performance measures and goals that can be defined to directly relate to corporate strategy and are easy to understand and communicate. This paper presents a real-world example to illustrate this state-of-the-art methodology.
Enhancing Business Process Management with Simulation Optimization
A growing number of business process management software vendors are offering simulation capabilities to extend their modeling functions and enhance their analytical proficiencies. Simulation is positioned as a means to evaluate the impact of process changes and new processes in a model environment through the creation of “what-if” scenarios.
Practical Optimization: Working with the Realities of Decision Management
Portfolio management tools are designed to aid senior management in the development and analysis of portfolio strategies; however, most of the commercial portfolio optimization packages are relatively inflexible and unable to answer the key questions posed.
Advanced Project Portfolio Selection Methods
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. Simulation optimization offers a flexible modeling and solution approach that overcomes these limitations.
Optimization in Supply Chain Planning and Management
The latest advancements in integrating optimization technology with evaluation techniques that model the complex supply chain environment have contributed to enabling improved and more focused decisions by the diverse set of managers involved in extracting the most value from the supply chain.
OptFolio - A Simulation Optimization System for Project Portfolio Planning
OptFolio is a best-in-class portfolio optimization software system that simultaneously addresses financial return goals, catastrophic loss avoidance, and performance probability. The innovations embedded in the system enable users to confidently design effective plans for achieving financial goals, employing accurate analysis based on real data.
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Machine Learning Hyperparameter Tuning with SimWrapper
Optimize your Python Model with SimWrapper
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