White Papers

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

Strategic Workforce Optimization: Ensuring Workforce Readiness with OptForce

Strategic workforce planning (SWP) has been identified as a top business challenge and a high priority in order to produce organizations that are capable of performing more effectively. Industry leaders recognize that success depends on attracting, retaining and developing talent, and having the right talent in the right place at the right time – to achieve the condition often characterized as workforce readiness. High readiness levels require anticipating and responding to changing workforce needs and market demands, and allocating resources effectively.

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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. We discuss the technologies embedded in OptQuest, a commercial software for optimization of complex systems — such as those modeled as computer simulations. We illustrate 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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Workforce Analytics Case Study

Many engineering firms are facing a market with a shrinking supply of talent. Coupled with an aging workforce, the problem of having a sufficient inventory of capable engineers and project managers available and/or working on projects is of significant concern. The following case study identifies the insights and actionable decision recommendations that emerged from the role analytics played in a large engineering firm experiencing critical talent turnover.

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The Exploding Domain of Simulation Optimization

In this paper, we first summarize some of the most relevant approaches that have been developed for the purpose of optimizing simulated systems. We then concentrate on the metaheuristic black-box approach that leads the field of practical applications and provide some relevant details of how this approach has been implemented and used in commercial software. Finally, we present an example of simulation optimization in the context of a simulation model developed to predict performance and measure risk in a real world project selection problem.

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Practical Introduction to Simulation Optimization

The merging of optimization and simulation technologies has seen a rapid growth in recent years. A Google search on “Simulation Optimization” returns more than six thousand pages where this phrase appears. 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 as much interest in the academic world as in practical settings. In this paper, we first summarize some of the most relevant approaches that have been developed for the purpose of optimizing simulated systems. We then concentrate on the metaheuristic black-box approach that leads the field of practical applications and provide some relevant details of how this approach has been implemented and used in commercial software. Finally, we present an example of simulation optimization in the context of a simulation model developed to predict performance and measure risk in a real world project selection problem.

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Optfolio – A Simulation Optimization System for Project Portfolio Planning

OptFolio is a new portfolio optimization software system 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. Traditional analysis and prediction methods are based on mean variance analysis — an approach known to be faulty. The new software system takes a much more sophisticated and strategic direction. State-of-the-art technology integrates simulation and metaheuristic optimization techniques and a new surface methodology based on linear programming into a global system that guides a series of evaluations to reveal truly optimal investment scenarios.

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Simulation Optimization: A Review, New Developments and Applications

We provide a descriptive review of the main approaches for carrying out simulation optimization, and sample some recent algorithmic and theoretical developments in simulation optimization research. Then we survey some of the software available for simulation languages and spreadsheets, and present several illustrative applications.

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Selecting Project Portfolios By Optimizing Simulations

New advances in the area of simulation optimization allow managers to go beyond traditional ranking rules, CAPM and real options analysis in order to select optimal sets of projects to fund. Furthermore, these advances make use of portfolio performance measures and goals that can be defined to directly relate to corporate strategy and are easy to communicate and understand. We present a real-world example to illustrate this 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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SIMOPT – Reliability for Improved Workforce Planning

Strategic Workforce Planning has a dramatic impact on both economic and social welfare. CEOs of major companies and institutions consistently identify “attracting, retaining and developing talent” as a top priority. To meet such goals requires accounting for a multitude of factors that affect the financial health and effective functioning of the organization and its employees alike.

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Classification by Vertical and Cutting Multi-Hyperplane Decision Tree Induction

Two-group classification is a key task in decision making and data mining applications. We introduce two new mixed integer programming formulations that make use of multiple separating hyperplanes. They represent a generalization of previous piecewise-linear models that embed rules having the form of hyperplanes, which are used to successively separate the two groups. In fact, the classifiers obtained are particular types of decision trees which are allowed to grow in depth and not in width. Computational results show that our models achieve better classification accuracy in less time than previous approaches.

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Cyber Swarm Algorithms – Improving Particle Swarm Optimization Using Adaptive Memory Strategies

Particle swarm optimization (PSO) has emerged as an acclaimed approach for solving complex optimization problems. The nature metaphors of flocking birds or schooling fish that originally motivated PSO have made the algorithm easy to describe but have also occluded the view of valuable strategies based on other foundations. From a complementary perspective, scatter search (SS) and path relinking (PR) provide an optimization framework based on the assumption that useful information about the global solution is typically contained in solutions that lie on paths from good solutions to other good solutions. Shared and contrasting principles underlying the PSO and the SS/PR methods provide a fertile basis for combining them. Drawing especially on the adaptive memory and responsive strategy elements of SS and PR, we create a combination to produce a Cyber Swarm Algorithm that proves more effective than the Standard PSO 2007 recently established as a leading form of PSO. Applied to the challenge of finding global minima for continuous nonlinear functions, the Cyber Swarm Algorithm not only is able to obtain better solutions to a well known set of benchmark functions, but also proves more robust under a wide range of experimental conditions.

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Exact, Heuristic and Metaheuristic Methods for Confidentiality Protection by Controlled Tabular Adjustment

Government agencies and commercial organizations that report data face the task of representing the data meaningfully while simultaneously protecting the confidentiality of critical data components. The challenge is to organize and disseminate data in a form that prevents these components from being unmasked by corporate espionage, or falling prey to efforts to penetrate the security of the information underlying the data. Unscrupulous data investigators could use unprotected data sources to infer sensitive, personal data about individuals. Besides harming individuals, these types of disclosures can drastically affect the willingness of future respondents to provide valuable data. Controlled tabular adjustment is a recently developed approach for protecting sensitive information by imposing a special form of statistical disclosure limitation on tabular data. The underlying model gives rise to a mixed integer linear programming problem involving both continuous and discrete (zero-one) variables. In this paper we develop new hybrid heuristics and a new meta-heuristic learning approach for solving this model, and compare their performance to previous heuristics and to an exact algorithm in the ILOG-CPLEX software. Our new approaches are based on partitioning the problem into its discrete and continuous components, and first creating a hybrid that reduces the number of binary variables through a grouping procedure that combines an exact mathematical programming model with constructive heuristics. Finally, we introduce a new metaheuristic learning method that significantly improves the quality of solutions obtained.

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Integrated Exact, Hybrid and Metaheuristic Learning Methods for Confidentiality Protection

A vital task facing government agencies and commercial organizations that report data is to represent the data in a meaningful way and simultaneously to protect the confidentiality of critical components of this data. The challenge is to organize and disseminate data in a form that prevents such critical components from being inferred by groups bent on corporate espionage, to gain competitive advantages, or having a desire to penetrate the security of the information underlying the data. Controlled tabular adjustment is a recently developed approach for protecting sensitive information by imposing a special form of statistical disclosure limitation on tabular data. The underlying model gives rise to a mixed integer linear programming problem involving both continuous and discrete (zero-one) variables.

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Improved Classification and Discrimination by Successive Hyperplane and Multi-Hyperplane Separation

We propose new models for classification and discrimination analysis based on hyperplane and multi-hyperplane separation models. Our models are augmented for greater effectiveness by a tree-based successive separation approach that can be implemented in conjunction with either linear programming or mixed integer programming formulations. Additional model robustness for classifying new points is achieved by incorporating a retrospective enhancement procedure. The resulting models and methods may be viewed from the perspective of support vector machines and supervised machine learning, although the new approaches produce regions and means of exploring them that are not encompassed by the procedures customarily applied. We focus primarily on two-group classification, but also identify how our approaches can be applied to classify points that lie in multiple groups.

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Mathematical Programming Models for Balancing Data Quality and Confidentiality in Tabular Data

Statistical agencies use different methods to protect the confidentiality of tabular data. The most widely used method, complementary cell suppression, suppresses both primary (sensitive) and secondary (non-sensitive cells) to assure confidentiality. Despite its popularity, it suffers from severe limitations. Complementary cell suppression problem is an NP-hard problem, and thereby computationally difficult to solve. It generates tables with missing data and many end users find it difficult to analyze the resulting data from a statistical point of view. For example, users cannot easily estimate means or variance from this data. Finally, published data protected by cell suppression can be susceptible to possible disclosure.

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OptCenter™ Achieving Breakthrough Performance Levels in Contact Centers

One of the most important tasks that a contact center manager must accomplish is the effective scheduling of the center employees. The manager wishes to determine the optimal labor requirements throughout the day that provide the appropriate customer service at the least possible cost. The staffing solution may contain multiple skill levels that must be considered when determining the optimal schedule.

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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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