2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
498
2 - Sequential Exploration With Geological Dependencies And
Uncertainty In Oil Prices
Babak Jafarizadeh, Statoil, Sandsliveien 90, SE-SV ND2,
Bergen, 5020, Norway,
Economic valuation and analysis of drilling decisions for a cluster of exploration
opportunities can be analytically challenging. If oil is found in one location, the
probability of finding oil in the nearby prospects may increase. Furthermore, the
time required to interpret data and update the geological understanding exposes
these investments to hydrocarbon price dynamics. In this work, we develop a
framework for valuation of clusters of exploration opportunities where prospects
are geologically dependent and uncertainty in oil prices is described as a mean-
reverting stochastic process.
3 - Sequential Sample Allocation For Multiple Attribute
Selection Decisions
Dennis D Leber, NIST, 100 Bureau Drive, MS 8980, Gaithersburg,
MD, 20899-8980, United States, ,
Jeffrey W Herrmann
When faced with a limited budget to collect data in support of a multiple attribute
selection decision, the decision-maker must decide how many samples to observe
from each alternative and attribute. This allocation decision is of particular
importance when the observed attribute values contain uncertainty, such as with
physical measurements. We present a sequential allocation scheme that relies
upon Bayesian updating in an attempt to maximize the probability of correct
selection when the attribute values contain Gaussian measurement error.
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208B-MCC
Perceptions, Behavior, and Decisions
Sponsored: Decision Analysis
Sponsored Session
Chair: Franklyn Koch, Koch Decision Consulting, Eugene, OR,
United States,
Co-Chair: Gregory L Hamm, Stratelytics, LLC, Alameda, CA, United
States,
1 - Perceived Catastrophic Risks In Sequential Social Networks
Shu Huang, Master Candidate, Tsinghua University, Tsinghua
University, Haidian District, Beijing, 100084, China,
Chen Wang
Slovic (1987) proposes to use degrees of dread, unknown and personal exposure
to describe individual risk perception. These factors depend on whether an
individual has undergone disasters or near misses in the past, and are also affected
by experiences and perspectives of his or her neighbors in the social network. We
model each individual’s risk perception of uncertain consequences and likelihood
of personal exposure as a Bayesian learning process to achieve equilibrium of
estimation within each neighborhood. We can then construct a utility map over
the social network to depict the dynamics of risk perception in response to
multiple disasters.
2 - Bipolar Cardinal Ranking For Group Disagreement Evaluations
Aron Larsson, Associate Professor, Stockholm University,
Postbox 7003, Kista, SE-16407, Sweden,
Tobias Fasth
Public decision making involve decisions that affect many stakeholders who have
conflicting opinions. This may cause implementation issues for an authority, and
to avoid this and to identify which stakeholder groups that are credited or
discredited, it is of interest for the authority to understand how their preferences
differ. One way of approaching this is to provide effective means for stakeholders
to state preferences and affect towards proposed alternatives. For this we
developed a web questionnaire using bipolar cardinal ranking where stakeholders
state negative, neutral, or positive affect towards alternatives. The approach is
demonstrated in a real-life case in Upplands Väsby, Sweden.
3 - Effects Of Total Cost Of Ownership on Automobile
Purchasing Decisions
Muhammed Sutcu, Assistant Professor, Abdullah Gul University,
Sumer Campus, Erkilet Bulvari, Kayseri, 38060, Turkey,
We reveal a complete picture of ownership-related expenses and construct a
decision model which helps decision maker to make the optimal choice when
purchasing an automobile. The decision model helps the costumers to understand
what a car will cost beyond its purchase price when customers consider out-of-
pocket expenses like fuel, repair, and insurance. For that purpose, representative
joint probability distributions of a decision maker are approximated using
maximum cumulative residual entropy (CRE) and CRE based first order
dependence tree approaches to elicit decision maker’s preferences to calculate the
total cost of ownership of an automobile.
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209A-MCC
Simulation and Optimization
Contributed Session
Chair: B Vermeulen, Eindhoven University of Technology, Eindhoven,
Netherlands,
1 - Bi-level Stochastic Approximation For Decomposable Stochastic
Optimization Formulations
Soumyadip Ghosh, IBM TJ Watson Research Center, 1101
Kitchawan Road, Route 134, Yorktown Heights, NY, 10598, United
States, , Ebisa Wollega, Mark S Squillante
We propose a bi-level algorithm to solve stochastic optimization formulations
with a certain decomposable structure. Consider, for example, the problem of
maximizing total revenue by jointly maximizing unit sales, subject to non-linear
market conditions, and minimizing costs, which for fixed sales is a two-stage
linear program (2SLP). An outer loop runs stochastic approximation (SA)
accounting for the non-linear part. An inner loop solves the 2SLP. The gradient of
the objective function of the 2SLP is used in the outer SA, and is obtained using
parametric programming. We analyze the convergence of this bi-level SA, and
provide experimental evidence of its efficacy on an energy-domain problem.
2 - A Decentralized Solution To The Car Pooling Problem
Pawel J. Kalczynski, Professor of IS and Decision Sciences,
California State University-Fullerton, 800 N State College Blvd.,
Fullerton, CA, 92834-6848, United States,
Malgorzata Miklas-Kalczynska
Existing carpool optimization techniques based on the centralized approach serve
policy-makers’ goals, but neglect the realities of participants. Moreover, absent
strict enforcement, participants often ignore centralized solutions and maximize
their own utility. We present a new model (formulated and tested on real-world
and simulated problem instances) that mimics a decentralized carpool self-
organization process. Our findings reveal savings similar to centralized models,
and a potential strategy for improving carpool utilization.
3 - Avoiding Singularities In Parallel Robotic System Design
Cameron Turner, Associate Professor, Clemson University,
206 Fluor Daniel EIB, Clemson, SC, 29634-0921, United States,
Sean Fry
Singular configurations in robotic systems present significant design and control
problems. In parallel robotic systems, the complex nature of singularities makes
their consideration during the design process even more significant when high
precision motions are desired. Using surrogate-based optimization techniques, this
paper achieves a solution for the design of a parallel robotic system for engineered
material characterization.
4 - MO-COMPASS For Constrained Simulation Optimization
Haobin Li, Institute of High Performance Computing, A*STAR,
1 Fusionopolis Way, #16-16 Connexis North, Singapore, 138632,
Singapore, Xiaofeng Yin, Wanbing Zhang,
Loo Hay Lee
MO-COMPASS is recently developed for multi-objective simulation optimization,
which has limitation that only linear constraints on the decisions space can be
explicitly handled. Whereas, for non-linear or stochastic constraints as simulation
outputs, the default approach that penalizes the constraint violation on the
objective values can be inefficient with the “most-promising-area” (MPA)
structure. In this study, we propose a novel approach for constraint handling in
the multi-objective environment, by considering Pareto optimality in different
feasibility layers with generalized MPAs. So we extend MO-COMPASS to solve
constrained optimization problems in an efficient manner.
5 - Approximate, Adaptive Maintenance Scheduling Of Maritime
Assets Under Different Operating Modes
B Vermeulen, Eindhoven University of Technology, Eindhoven,
Netherlands, Sena Eruguz, Tarkan Tan,
Geert-Jan Van Houtum
In the maritime sector, vessels are used under different operating modes with
different degradation rates, different maintenance setup and downtime costs, and
particular maintenance/ replacement options. Given uncertainty on actual
degradation rates for components, asset owners follow the OEMs’ hyper-
conservative and hence costly maintenance and replacement policies. We provide
an approximate dynamic programming model (and proprietary software tool)
with online adjustment of degradation rates and adaptive planning of
maintenance activities given the schedule of operating modes.
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