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ABSTRACT: We view Multicriteria Decision Making (MCDM) as the conjunction of three components: search, preference tradeoffs, and interactive visualization. The first MCDM component is the search process over the space of possible solutions to identify the non-dominated solutions that compose the Pareto set. The second component is the preference tradeoff process to select a single solution (or a small subset of solutions) from the Pareto set. The third component is the interactive visualization process to embed the decisionmaker in the solution refinement and selection loop. We focus on the intersection of these three components and we highlight some research challenges, representing gaps in the intersection. We introduce a requirement framework to compare most MCDM problems, their solutions, and analyze their performances. We focus on two research challenges and illustrate them with three case studies in electric power management, financial portfolio rebalancing, and air traffic planning.
IEEE Computational Intelligence Magazine 09/2009; · 3.37 Impact Factor
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ABSTRACT: Industrial machinery/assets are usually operated under different operating conditions or modes. Local empirical models can be built within specified operating condition boundaries to represent system dynamics more accurately than a global model, which is generally applicable over the entire operating regime. However, when the change of system operating regime occurs, none of local models can capture the characteristics of the system outside of the operating condition boundaries they were built upon. This paper presents a novel approach to model selection decision-making based on a fuzzy supervisory approach. The supervisory method selects local models and fuses these models to represent system dynamics as system transits from one operating regime to another. Through this fuzzy supervisory approach, the modeling errors caused by an operating regime switch can be significantly reduced. We present experimental results from the application of this approach to high bypass commercial aircraft engine.
Computational intelligence in miulti-criteria decision-making, 2009. mcdm '09. ieee symposium on; 05/2009
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ABSTRACT: A visual interactive multi-criteria decision-making method for partitioning a portfolio of assets into mutually exclusive categories is presented. The two principal decision categories are hold and sell - portfolio assets in the sell category are considered as potential sale prospects, and the other assets in the portfolio are considered as potential retention prospects. The problem may be mathematically formulated as a multi-criteria 0/1 knapsack problem with multiple constraints. The decision-making method centers on the utilization of several coupled 2D projections of the portfolio in the multi-dimensional criterion space. The decision-maker interacts with these projections in a variety of ways to express and record multi-category (hold, hold-bias, sell-bias, and sell) set partitioning preferences. The decision-maker may also set an aggregated preference threshold that is utilized for partitioning the portfolio into the two principal hold and sell categories. The decision-maker may further fine-tune their preferences and threshold settings so as to achieve a multitude of financial targets.
Computational Intelligence in Multicriteria Decision Making, IEEE Symposium on; 05/2007
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ABSTRACT: Two industrial deployments of multi-criteria decision-making systems at General Electric are reviewed from the perspective of their multi-criteria decision-making component similarities and differences. The motivation is to present a framework for multi-criteria decision-making system development and deployment. The first deployment is a financial portfolio management system that integrates hybrid multi-objective optimization and interactive Pareto frontier decision-making techniques to optimally allocate financial assets while considering multiple measures of return and risk, and numerous regulatory constraints. The second deployment is a power plant management system that integrates predictive modeling based on neural networks, optimization based on multi-objective evolutionary algorithms, and automated decision-making based on Pareto frontier techniques. The integrated approach, embedded in a real-time plant optimization and control software environment dynamically optimizes emissions and efficiency while simultaneously meeting load demands and other operational constraints in a complex real-world power plant
Computational Intelligence in Multicriteria Decision Making, IEEE Symposium on; 05/2007
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ABSTRACT: The U.S. national Air Traffic Management (ATM) system is today operating at the edge of its capabilities, handling the real-time planning and coordination of over 50,000 flights per day. This situation will only worsen in the years to come, as it is expected that U.S. air traffic will nearly double by the year 2025. There is a pressing need therefore for increasing capacity to meet future demand, improving safety, enhancing efficiency, providing additional flexibility to airline operators, and equitable consideration of multiple stakeholder needs in this complex dynamic system. In this paper, we present a scalable enterprise framework for multi-stakeholder, multi-objective model-based planning and optimization of air traffic in the national airspace system (NAS). The approach is based on an intelligent evaluation and optimization at the strategic and flight route levels. At the strategic level, we focus on separations between flights to improve airspace system performance. At the flight route level, we focus on identifying an optimal portfolio of flight paths within a planning horizon that trades-off a reduction in miles flown and a reduction in congestion. This framework not only considers system-level objectives, but also regards the impact of decisions on the principal stakeholders within the NAS. It is expected that this system will serve as a key decision-support tool to address future NAS scalability and reliability needs.
Aerospace Conference, 2007 IEEE; 04/2007
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ABSTRACT: Over the past two decades, model predictive control and decision-making strategies have established themselves as powerful methods for optimally managing the behavior of complex dynamic industrial systems and processes. This paper presents a novel model-based multi-objective optimization and decision-making approach to model-predictive decision-making. The approach integrates predictive modeling based on neural networks, optimization based on multi-objective evolutionary algorithms, and decision-making based on Pareto frontier techniques. The predictive models are adaptive, and continually update themselves to reflect with high fidelity the gradually changing underlying system dynamics. The integrated approach, embedded in a real-time plant optimization and control software environment has been deployed to dynamically optimize emissions and efficiency while simultaneously meeting load demands and other operational constraints in a complex real-world power plant. While this approach is described in the context of power plants, the method is adaptable to a wide variety of industrial process control and management applications
Computational Intelligence for Measurement Systems and Applications, Proceedings of 2006 IEEE International Conference on; 08/2006
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ABSTRACT: We discuss implicit and explicit knowledge representation mechanisms for evolutionary algorithms (EAs). We also describe offline and online metaheuristics as examples of explicit methods to leverage this knowledge. We illustrate the benefits of this approach with four real-world applications. The first application is automated insurance underwriting-a discrete classification problem, which requires a careful tradeoff between the percentage of insurance applications handled by the classifier and its classification accuracy. The second application is flexible design and manufacturing-a combinatorial assignment problem, where we optimize design and manufacturing assignments with respect to time and cost of design and manufacturing for a given product. Both problems use metaheuristics as a way to encode domain knowledge. In the first application, the EA is used at the metalevel, while in the second application, the EA is the object-level problem solver. In both cases, the EAs use a single-valued fitness function that represents the required tradeoffs. The third application is a lamp spectrum optimization that is formulated as a multiobjective optimization problem. Using domain customized mutation operators, we obtain a well-sampled Pareto front showing all the nondominated solutions. The fourth application describes a scheduling problem for the maintenance tasks of a constellation of 25 low earth orbit satellites. The domain knowledge in this application is embedded in the design of a structured chromosome, a collection of time-value transformations to reflect static constraints, and a time-dependent penalty function to prevent schedule collisions.
IEEE Transactions on Evolutionary Computation 07/2006; · 3.34 Impact Factor
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ABSTRACT: We present methods to automatically identify and optimize controllers for large-scale complex dynamic systems; in particular, aircraft gas turbine engines. We show how the optimization of different elements within the overall controller can be addressed in an efficient fashion. These elements include local actuator gains, control modifiers, and control schedules. An evolutionary algorithm (EA) is utilized to realize multiobjective optimization on a local as well as a global level, depending on the optimization task at hand. The fitness function comprises performance metrics that incorporate stall margins, exhaust gas temperature, fan-speed tracking error, and local tracking errors. Less attention has been given in the literature to the application of optimization techniques to aircraft engine control systems design, where the controls design and optimization is performed using a full-order engine model and full control systems structures that do not oversimplify the inherent complexities in these highly complex nonlinear dynamic systems. This paper attempts to close that gap.
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 12/2005; · 2.01 Impact Factor
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ABSTRACT: A principal challenge in modern computational finance is efficient portfolio design - portfolio optimization followed by decision-making. Optimization based on even the widely used Markowitz two-objective mean-variance approach becomes computationally challenging for real-life portfolios. Practical portfolio design introduces further complexity as it requires the optimization of multiple return and risk measures subject to a variety of risk and regulatory constraints. Further, some of these measures may be nonlinear and nonconvex, presenting a daunting challenge to conventional optimization approaches. We introduce a powerful hybrid multiobjective optimization approach that combines evolutionary computation with linear programming to simultaneously maximize these return measures, minimize these risk measures, and identify the efficient frontier of portfolios that satisfy all constraints. We also present a novel interactive graphical decision-making method that allows the decision-maker to quickly down-select to a small subset of efficient portfolios. The approach has been tested on real-life portfolios with hundreds to thousands of assets, and is currently being used for investment decision-making in industry.
Evolutionary Computation, 2005. The 2005 IEEE Congress on; 10/2005
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ABSTRACT: A theoretical foundation is presented for modeling and convergence analysis of a class of distributed coevolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for a class of centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a model for a class of distributed coevolutionary algorithms. Convergence and convergence rate analyses are pursued for basic classes of objective functions. Our theoretical investigation reveals that for certain unimodal and multimodal objectives, we can expect these algorithms to converge at a geometrical rate. The distributed coevolutionary algorithms are of most interest from the perspective of their performance advantage compared to centralized algorithms, when they execute in a network environment with significant local access and internode communication delays. The relative performance of these algorithms is therefore evaluated in a distributed environment with realistic parameters of network behavior.
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 05/2004; · 3.08 Impact Factor
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ABSTRACT: A novel evolutionary planning framework (coevolutionary virtual design environment) particularly suited to distributed network-enabled design and manufacturing organizations is presented. The approach utilizes distributed evolutionary agents and mobile agents as principal object-oriented software entities that support a network-efficient evolutionary exploration of planning alternatives in which successive populations systematically select planning alternatives that reduce cost and increase throughput. This paper presents the architecture of the coevolutionary virtual design environment, and examines the network-based performance of the coevolutionary algorithms that execute in this environment. Simulation analysis examines the percentage convergence error and percentage computational advantage comparing the distributed network-based implementation to a centralized network-based implementation. The algorithms and architectures are evaluated in a realistic network setting and analyzed using models of network delays and processing times.
IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans 04/2004; · 2.12 Impact Factor
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ABSTRACT: A retrospective argument supporting the adaptive control of the resources of an evolutionary algorithm is presented. In this approach, a fuzzy controller monitors the population diversity and maturity of the evolutionary process, and issues control actions to adapt the algorithm's population size and mutation rate. The fuzzy controller's rule base is simple and intuitive, and encodes expert heuristic knowledge on the management of an evolutionary algorithm's resources. Analysis based on experimentation and statistical hypothesis testing reveals that such an adaptive approach significantly reduces the variance in search performance of the evolutionary algorithm. Experiments also reveal that the approach in general improves search performance of the algorithm.
Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on; 06/2003
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ABSTRACT: Innovations in software, networks, and database systems are
enabling widely distributed organizations to integrate activities, share
information, collaborate on decisions, and execute transactions.
However, successful enterprise-wide collaboration is increasingly
dependent on the availability of generalized decision-support tools that
can efficiently access and utilize distributed information. This paper
presents a basic theory and network-based performance evaluation of a
class of coevolutionary algorithms that supports efficient planning in a
distributed environment. Performance of these coevolutionary algorithms
is evaluated in a distributed information architecture (coevolutionary
virtual design environment) that supports integrated
design-supplier-manufacturing planning. In this architecture,
distributed evolutionary agents and mobile agents are principal entities
that support a network-efficient exploration of planning alternatives in
which successive populations systematically select superior planning
alternatives
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on; 02/2002
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ABSTRACT: Describes a framework to support enterprise-level decision-making in network-based scalable systems. The fundamental principle of this approach asserts that decision tasks over a set of multiple, distributed, logically interrelated databases may be cast in the semantics of the underlying distributed database management system (DBMS). In this form, the augmented relational decision framework takes advantage of the principles of normalization and decomposition that are inherent to the relational DBMS model. The decision process is viewed as an instantiation of the relational schema, and the resulting representation of normal form hierarchies and data dependencies structures the process. Definition of a value function (or rule set) over the augmented relational decision space guides the search for desirable instances of the decision relations that constitute the suggested outcomes.
Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on; 02/2002
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ABSTRACT: Planning in distributed environments requires efficient allocation
of resources and access to information. This paper presents a basic
theory and analysis of a class of distributed coevolutionary algorithms
that supports planning in a networked environment. These algorithms are
evaluated in a distributed information architecture that facilitates
coevolutionary optimization of a design-supplier-manufacturing planning
problem. Simulation analysis examines the percentage convergence error
and the percentage computational advantage comparing the distributed
algorithm to a centralized approach. The algorithms are evaluated in a
realistic network setting and analyzed using models of network delays
and processing times
Assembly and Task Planning, 2001, Proceedings of the IEEE International Symposium on; 02/2001
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ABSTRACT: In an increasingly networked global marketplace, products and services are seldom created in isolation and are instead being realized through strategic and dynamic partnerships between suppliers, contract manufacturers, and customers. Superior design-supplier-manufacturing decisions are critical to the survival of enterprises that seek to compete in this environment. A novel evolutionary decision support framework (coevolutionary virtual design environment) particularly suited to distributed network-enabled organizations is introduced. In this framework an electronic interchange of design, supplier, and manufacturing information facilitates concurrent, network distributed decision-making based on evolutionary computation. The approach utilizes distributed evolutionary agents and mobile agents as principal entities that support a network-efficient exploration of planning alternatives in which successive populations systematically select planning alternatives that reduce cost and increase throughput.
Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on; 02/2001
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[show abstract]
[hide abstract]
ABSTRACT: A theoretical foundation is presented for modeling and convergence
analysis of distributed co-evolutionary algorithms applied to
optimization problems in which the variables are partitioned among p
nodes. An evolutionary algorithm at each of the p nodes performs a local
evolutionary search based on its own set of primary variables, and the
secondary variable set at each node is clamped during this phase. An
infrequent intercommunication between the nodes updates the secondary
variables at each node. The local search and intercommunication phases
alternate, resulting in a cooperative search by the p nodes. First, we
specify a theoretical basis for centralized evolutionary algorithms in
terms of construction and evolution of sampling distributions over the
feasible space. Next, this foundation is extended to develop a general
model of distributed co-evolutionary algorithms. Convergence and
convergence rate analyses are pursued for certain basic classes of
objective functions. Also considered are relative computational delays
of the centralized and distributed algorithms when they are implemented
in a network environment
Evolutionary Computation, 2000. Proceedings of the 2000 Congress on; 02/2000
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ABSTRACT: This paper presents a comparison of the performance of a fuzzy
logic controlled genetic algorithm (FLC-GA) and a parameter tuned
genetic algorithm (TGA) for an agile manufacturing application. In the
FLC-GA, fuzzy logic controllers dynamically schedule parameters of the
object-level GA. A fuzzy knowledge-base is automatically identified and
tuned using a high-level GA. In the TGA, a high-level GA is used to
determine an optimal static parameter set for the object-level GA. The
object-level GA supports a global evolutionary optimization of design,
manufacturing, and supplier planning decisions for manufacturing of
printed circuit assemblies in an agile environment. We demonstrate that
high-level system identification or tuning performed with small
object-level search spaces, can be extended to more elaborate
object-level search spaces. The TGA performs superior searches, but
incurs large search times. The FLC-GA performs faster searches than a
TGA, and is slower than the GA that utilizes a canonical static
parameter set. However, search quality of the FLC-GA is comparable to
that of the GA which utilizes a canonical static parameter set
Intelligent Control (ISIC), 1998. Held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Intelligent Systems and Semiotics (ISAS), Proceedings; 10/1998
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ABSTRACT: The virtual design environment is an information architecture to
support design-manufacturing-supplier planning decisions in a
distributed, heterogeneous environment. The approach utilizes
evolutionary intelligent agents as program entities which generate and
execute queries among distributed computing applications and databases.
The evolutionary agents support a global evolutionary optimization
process in which successive populations systematically select planning
alternatives which reduce cost and increase throughput. A prototype of
the virtual design environment has been implemented using CORBA as a
principal distributed systems programming tool. The prototype has been
used to examine design-manufacturing-supplier decisions for a real
commercial electronic circuit board product (Pitney Bowes Inc.) and to
explore plans in controlled experiments with alternative manufacturing
facilities
Robotics and Automation, 1998. Proceedings. 1998 IEEE International Conference on; 06/1998
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ABSTRACT: Fusing the outputs from an ensemble of models in an effective way can often boost overall model accuracy. This paper presents a novel method, called locally weighted fusion, which aggregates the results of multiple predictive models based on local accuracy measures of these models in the neighborhood of the probe point for which we want to make a prediction. While we demonstrate the method in the context of multiple neural network models, the concepts may be applied to other predictive techniques as well. This fusion method is applied to develop highly accurate models for emissions, efficiency, and load prediction in a complex real-world power plant. The locally weighted fusion method boosts the predictive performance by 20-40% over the baseline single model approach for the various prediction targets. Relative to this approach, fusion strategies which apply averaging or globally weighting only produce a 2-6% performance boost over the baseline.
Neural Networks, 2006. IJCNN '06. International Joint Conference on;
