Piero P. Bonissone’s Research Interests:
Multi-Criteria Decision Making (MCDM)
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Timeline
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Papers
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(2009) [11] P. Bonissone,
R. Subbu, J. Lizzi, “Multi Criteria Decision Making (MCDM): A Framework for
Research and Applications”, IEEE
Computational Intelligence Magazine, 4(3):48-61, Aug 2009– [GE GR
Technical Report, 2009GRC412, Apr 2009 (pdf)]. We
view Multi Criteria 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
development of efficient search algorithms has been the goal of
Multi-Objective Optimization (MOO), from classical mathematical programming
to evolutionary approaches. However MOO’s emphasis has been on generating densely sampled,
well-distributed Pareto sets, without worrying about the solution selection
phase. The second component is the
preference tradeoff process to select a single solution (or a small subset of
solutions) from the Pareto set. The development of methods to capture and
aggregate preferences has been the goal of Bayesian and Fuzzy decision-making
techniques. However, their emphasis
has been on the aggregation mechanisms to select a solution, rather than the
solution generation phase. The
third component is the interactive visualization process to embed the
decision-maker in the solution refinement and selection loop. We often need
to embed the decision-maker in the solution refinement and selection loop. To
this end, we need to understand and present the impacts that intermediate
tradeoffs in one sub-space could have in the other ones, while allowing
him/her to retract or modify any intermediate decision steps to strike
appropriate tradeoff balances 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. (2008) [10] P. Bonissone, “Research Issues in
Multi Criteria Decision Making (MCDM): The Impact of Uncertainty in Solution
Evaluation”, IPMU 2008, We consider Multi Criteria 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
decision-maker 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 the impact of uncertainty in each of these components and
illustrate it with a real-world application. (2007) [9]
Bollapragada, K. Chalermkraivuth, N. Eklund, N. Iyer, R. Shah, F. Xue and W.
Yan, “A review of two industrial deployments of multi-criteria
decision-making systems at General Electric”, First IEEE Symposium on
Computational Intelligence in Multi-Criteria Decision-Making (MCDM 2007),
Honolulu, Hawaii, 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 (2006) [8] N. Iyer, K. Goebel, P. Bonissone, Framework for
Post-Prognostic Decision Support, IEEE Aerospace Conference (IEEEAC) 11.0903,
March 4-11, 2006, (pdf) This paper describes a
decision support system (DSS) for use in operational decision making with PHM
specific data. Challenges arise from the large amount of different
information pieces upon which a decision maker has to act. Conflicting
information from on-board and offboard PHM modules,
seemingly contradictory and changing requirements from operations as well as
maintenance for a multitude of different systems within strict time
constraints make operational decision-making a difficult undertaking. The DSS
will enable the user to make optimal decisions based on his expression of
rigorous trade-offs between different prognostic and external information
sources. This is accomplished through guided evaluation of different optimal
decision alternatives under operational boundary conditions using
user-specific and interactive collaboration. We present some preliminary
results of the use of such a DSS for post-prognostics decision-making.
(2006) [7] R. Subbu, P. Bonissone, N. Eklund,
W. Yan, N. Iyer, F. Xue, R. Shah,
“Management of Complex Dynamic Systems based on Model-Predictive
Multi-objective Optimization” CIMSA 2006, pp. 64-69, La Coruña,
Spain, Jul 12-14, 2006 (pdf) – [GE GR Tech. Report,
2006GRC456, 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. (2005) [6] R.
Subbu, P. Bonissone, N.
Eklund, S. Bollapragada, K. Chalermkraivuth, "Multiobjective Financial
Portfolio Design: A Hybrid Evolutionary Approach", 2005 IEEE Congress
on Evolutionary Computation (CEC 2005), pp. 1722-1729, Edinburgh UK,
September 2-5, 2005 (pdf)
– [GE GR Tech. Report, 2005GRC389, 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. (2005) [5] F. Xue,
A.C. Sanderson, P. Bonissone,
R.J. Graves, “Fuzzy Logic Controlled Multi-Objective Differential Evolution”,
Proc. FUZZ-IEEE 2005, Reno NV, USA, May 22-25, 2005 (pdf)
– [GE GR Tech. Report, 2005GRC347, In
recent years, multi-objective evolutionary algorithms (MOEA) have generated a
large research interest. MOEA’s attraction stems
from their ability to find a set of Pareto solutions rather than any single, aggregated optimal solution for a multi-objective
problem. As for single-objective evolutionary algorithms (SOEA),
multi-objective evolutionary algorithms also require parameter tuning to
achieve desirable performance. In the literature we can find Fuzzy Logic
Controllers (FLC’s) applied to online parameter
control for SOEA. In this paper, we propose to use a FLC to dynamically
adjust the parameters of a particular Multi-Objective Differential Evolution
(MODE) algorithm. The fuzzy logic controlled multi-objective differential
evolution (FLC-MODE) is applied to a suite of benchmark functions. Its
results are compared to those obtained by using MODE with constant parameter
settings. We show that the FLC-MODE obtains better results in 80% of the
testing examples. Given that the benchmarks were synthetic test functions, we
designed the FLC using only our understanding of the working mechanism of the
MODE, without incorporating any additional problem-specific knowledge. When
addressing real-world applications, we expect the FLC to be an excellent way
for representing and leveraging their associated heuristic knowledge. (2002) [4] P. Bonissone, R. Subbu, and K. Aggour, "Evolutionary
Optimization of Fuzzy Decision Systems for Automated Insurance
Underwriting", Proc. FUZZ-IEEE 2002, pp. 1003-1008, Honolulu,
HI, May 2002 (pdf) (1996) [3] P. Bonissone, P. Khedkar, Y-T Chen, “Genetic Algorithms for Automated
Tuning of Fuzzy Controllers: A Transportation Application”, Proceedings
of the 1996 IEEE Conference on Fuzzy Systems (FUZZ-IEEE'96), pages
674--680, (1984) [2] R. Tong and P. Bonissone, “Linguistic Solutions to Fuzzy
Decision Problems”, in Studies in the Management Science, Vol. 20,
H.J. Zimmerman, L.A. Zadeh, B.R. Gaines (Editors), pp. 323-334, North
Holland, 1984 (1980) [1] R. Tong, P. Bonissone, “A Linguistic Approach to Decisionmaking with Fuzzy Sets”, IEEE Transaction on
Systems, Man, and Cybernetics, 10(11): 716-723, November 1980 |
Patents (Issued / Applications)
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(2010)
System and process for multivariate
adaptive regression splines classification for
insurance underwriting suitable for use by an automated system, P. Bonissone, R. Messmer, R. Subbu, W. Yan, A.
Chakraborty, US Patent 7,813,945, (October
12, 2010) (2010)
System and process for detecting
outliers for insurance underwriting suitable for use by an automated system, P. Bonissone, N. Iyer, US Patent 7,801,748, (September
21, 2010) (2009)
System and process for dominance classification for insurance underwriting
suitable for use by an automated system, P. Bonissone, N. Iyer, US Patent 7,567,914 (July 28, 2009) (2009)
Systems and
methods for multi-objective portfolio analysis and decision-making using
visualization techniques, P. Bonissone, S. Bollapragada, K.
Chalermkraivuth, N. Eklund, N. Iyer, R. Subbu, US Patent 7,630,928 (Dec 8, 2009) (2009) Systems and methods for multi-objective portfolio optimization, K. Chalermkraivuth, S. Bollapragada, P. Bonissone, M. Clark, N. Eklund, N. White N. Iyer, R. Subbu, US Patent 7,542,932 (June 2, 2009) (2009) Method and system for performing model-based multi-objective asset
optimization and decision-making, K. Chalermkraivuth, S. Bollapragada,
P. Bonissone, M. Clark, N. Eklund, N. Iyer, R. Subbu, US Patent 7,536,364 (May 19, 2009), (WO2005081902) (2008)
Systems
and methods for efficient frontier supplementation in multi-objective
portfolio analysis, P. Bonissone, S.
Bollapragada, C. Chalermkraivuth, N. Eklund, N. Iyer, R; Subbu, US Patent No. 7,469,228 (Dec 23, 2008) (2008) Systems and methods for multi-level optimizing control systems for boilers, V. Badami, R. Subbu, A. Taware, P. Bonissone, N. Widmer, US Patent No. 7,389,151 (June 17, 2008) (2006) Method and system
for performing model-based multi-objective asset optimization and
decision-making, (165411), R. Subbu, P. Bonissone, N. Eklund N. Iyer, R Shah, W.
Yan, C. Knodle, J. Schmid, filed (2006) Method and system for performing multi-objective
predictive modeling, monitoring, and update for an asset, R. Subbu, P. Bonissone, N.
Eklund N. Iyer, R Shah, W. Yan, C. Knodle; J.
Schmid, PUB_20060247798, filed (2005) Systems and methods for initial sampling in
multi-objective portfolio analysis, S. Bollapragada, P. Bonissone, K.
Chalermkraivuth, N. Eklund, N. Iyer, R. Subbu, PUB_20050187849, filed (2005) Systems and methods for multi-objective
portfolio analysis using Pareto sorting evolutionary algorithms, R. Subbu, S. Bollapragada, P. Bonissone,
K. Chalermkraivuth, N. Eklund, N. Iyer, PUB_20050187846, filed (2005) Systems and methods for multi-objective
portfolio analysis using dominance filtering, N. Eklund, S. Bollapragada, P. Bonissone,
K. Chalermkraivuth, N. Iyer, R. Subbu, PUB_20050187845, filed |
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MS Theses: (2004) Multiobjective Evolutionary Algorithms:
Targeting Migrant Election and Selection Schemes on an (2003) A Memory Enabled
Nondominated Sorting Genetic Algorithm, M. Abrams, ECSE Dept.[Advisor:
P.P. Bonissone] |
Projects
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Home Page| GE Research
Computer and Decision Sciences
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