OptQuest White Papers

Downloadable case studies and best practices from the complex systems optimization experts at OptTek

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. 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.

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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. Two of the leading and most widely used examples are scenario optimization and robust optimization, both of which are limited in scope and capability as compared with the more powerful and flexible technique of simulation optimization.

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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. Metaheuristics have been demonstrated by the scientific community to be a viable, and often superior, alternative to more traditional (exact) methods of mixed-integer optimization such as branch and bound and dynamic programming.

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OptQuest – Optimization of Complex Systems

Optimization of complex systems was for many years limited to problems that could be formulated as linear, nonlinear and integer mathematical programs. Problem-specific heuristics that do not require a mathematical formulation of the problem have been also used for optimizing complex systems. Advances in the area of metaheuristic optimization, coupled with improved computing environments, have placed the ambitious goal of building general-purpose, “black box” optimizers within reach. 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.

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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.

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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. In this paper, we demonstrate the advantages of using a Simulation Optimization approach to manage high-risk, critical decisions, by showcasing the methodology in two popular applications from the areas of finance and business process design.

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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. We illustrate this proposed approach via a market research application embodying agent-based simulation.

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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.

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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. The beneficiaries of this cutting-edge technology include executives responsible for capital investments, finance department analysts charged with capital budgeting, and technology managers responsible for project planning and implementation.

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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.

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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.

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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. Simulation is promoted to enable examination and testing of decisions prior to actually making them in the “real” environment. Since simulation approximates reality, it also permits the inclusion of uncertainty and variability into the forecasts of process performance. This paper explores how new approaches are significantly expanding the power of simulation for business process management.

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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. Most are not able to answer the key questions posed by senior management. Additionally, most senior managers are unfamiliar with the power and advantages provided by a true comprehensive portfolio tool.

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OptFolio® Project Portfolio Optimization Software

On January 1, 2003 OptTek Systems, Inc. won a Phase I Small Business Innovative Research (SBIR) grant* from the National Science Foundation (NSF). Considered by many to be one of the most prestigious United States Federal Government awards to a small business, SBIR’s have had a profound impact on the growth and prosperity of a vital sector of the US economy since their inception over twenty years ago.

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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 and common investment sense, 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 the investment. This is not only true in portfolios made up of securities and financial assets, but also in project portfolios. Therefore, for a given amount of risk, there is an “optimal” portfolio of projects that produces the highest possible return.

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Advanced Project Portfolio Selection Methods

In all areas of business, determining how to allocate investment capital in order 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 such as linear and quadratic programming. Unfortunately, such formulations omit key aspects of real world settings resulting in flawed solutions. In this column we focus on a flexible modeling and solution approach that overcomes these limitations.

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Combining Simulation & Optimization for Improved Business Decisions

Simulation is a powerful computer-based tool used by many decision-makers in business and industry to improve operating and organizational efficiency. The basic idea of simulation is to model a physical process on the computer, incorporating the uncertainties that are inherent in all real systems. The model is then “run” to simulate the effects of the physical process and to determine their consequences.

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OptQuest Vs. Microsoft Excel Solver (Brief Summary)

Distinguishing features of OptQuest include the ability to avoid being trapped in local optima, the capacity to handle real world (as opposed to “classical”) nonlinearities, and the power to handle relationships embodied in a simulation.

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Optimization in Production Process Flow

Optimization provides a powerful method to quickly search and identify simulation scenarios that yield the desired outcomes. The latest advancements in integrating optimization technology with evaluation techniques that model the complex production process flow environment have contributed to enabling improved and more focused decisions by the diverse set of managers involved in extracting the most value from the factory.

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Optimization in Supply Chain Planning and Management

Optimization has recently become a “hot” technology 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.

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