2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Resource Allocation Decisions With Deep Uncertainty
Cameron MacKenzie, Iowa State University,
Mathematical models to help public policy decision makers often have a great
amount of uncertainty, sometimes called deep uncertainty. Decision makers may
also be skeptical about solely relying on model recommendations. A solution to
this deep uncertainty and a decision maker’s skepticism is for the model output to
consist of ranges or intervals rather than point solutions. This presentation will
offer a method for identifying intervals for resource allocation models in which
every solution within the interval differs from the optimal solution by a
predetermined value.
4 - Economic Contagion And The Role Of Beliefs: Findings From A
Borrower-lender Game
Jonathan William Welburn, University of Wisconsin - Madison,
We present a within-period sequential-move game with multiple borrower
countries and a single common lender to model cross-country contagion. We
discuss the role of beliefs, modeled through Bayesian updating, and determine
equilibrium solutions using nonlinear optimization. The model is calibrated to the
2010 Eurozone crisis, but sensitivity analysis is used to identify conditions under
for contagion. Results demonstrate that what appears to be contagion may be the
result of a crisis of confidence. Findings and their implications for decision making
and policy are discussed.
TC44
208B-MCC
Decisions, Sensitivity and Applications
Sponsored: Decision Analysis
Sponsored Session
Chair: Emanuele Borgonovo, Bocconi University, Via Roentgen 1,
Milano, 20833, Italy,
1 - Strength Of Preferences In Repeated Prospects
Alessandra Cillo, Assistant Professor, Bocconi University, Milan,
20146, Italy, , Enrico G De Giorgi
Experimental studies have found that people reject a single lottery but accept a
repeated play of the same lottery. Other studies have also found that the higher
acceptance rates for the repeated play when the overall distribution is displayed
depends on the type of prospect. These results have critical managerial relevance,
but they are based on acceptance rates. The paper provides a theoretical
framework, which allows quantifying the strength of preferences in repeated
prospects. We provide an experiment to test possible editing processes in the
context of repeated prospects.
2 - Tolerance Sensitivity And Maximum Regret In
Linear Programming
Richard E. Wendell, University of Pittsburgh, Pittsburgh, PA,
15260, United States, ,
Emanuele Borgonovo
Within a tolerance framework for linear programming, we present a new
approach for calculating optimal coefficient sets. The approach solves an
otherwise NP hard problem and, moreover, allows us to streamline the
computation of regret functions.
3 - Randomized Differential Sensitivity
Sumeda Siriwardena, Bocconi University,
Emanuele Borgonovo
Sensitivity analysis is an integral part of the decision analysis process. In several
situations, analysts have not only the dataset of realizations of the model output
but also of the corresponding partial derivatives. We introduce a new method
based on the randomization of the differential importance measure. This
sensitivity indicator does not require independence and possesses the additivity
property, which makes the calculation of joint sensitivities seamless. We study
numerical estimation and obtain the expression of the convergence rate.
Managerial insights are discussed in detail.
4 - Estimating Strategic Impacts Of Foreclosed Housing
Redevelopment Using Spatial Analysis
Michael Johnson, University of Massachusetts Boston, MA,
Community-based organizations engaged in foreclosure response wish to quantify
the relative value of housing units for redevelopment. We measure the ‘strategic
value’ of property acquisition candidates based on proximity to site-specific
neighborhood amenities and disamenities, given the relative importance of that
proximity to CDC organizational and community objectives. We show that
strategic values can differ in systematic ways depending on the types of amenities
and disamenities identified as relevant for acquisition decisions, the relative
importance assigned to those amenities and disamenities, and the utility
maximization objectives of the organization.
TC45
209A-MCC
Multi-Objective Optimization Via Simulation
Sponsored: Simulation
Sponsored Session
Chair: Susan R Hunter, Purdue University, West Lafayette, IN, United
States,
Co-Chair: Enlu Zhou, Georgia Institute of Technology, na, Atlanta, GA,
na, United States,
1 - A Partition-based Random Search For Stochastic Multi-objective
Optimization Via Simulation
Loo Hay Lee, National University of Singapore, Singapore,
Singapore, Weizhi Liu, Siyang Gao
We proposed two parallel partition-based random search methods to solve the
stochastic multi-objective optimization via simulation considering Pareto
optimality for constrained and unconstrained case. The idea is to explore the
whole feasible region and exploit on current most promising regions in the same
time. Partition methods are used to shrink current most promising regions
iteratively, and simulation allocation rules are adopted to decrease the noise. Both
methods are proven to converge to the global Pareto set with probability one.
Numerical experiments are conducted to demonstrate the effectiveness and
robustness of the proposed algorithm compared to well-known methods.
2 - An Assessment Of Model Based Methods In Multi-objective
Optimization
Joshua Hale, Georgia Institute of Technology, 755 Ferst Drive, NW,
Atlanta, GA, Atlanta, GA, United States,
Helin Zhu, Enlu Zhou
We propose domination measure as a new concept to measure the quality of
solutions in multi-objective optimization. The domination measure of a solution
can be intuitively interpreted as the size of the portion of the decision space that
dominates that solution. We reformulate the multi-objective problem to a single-
objective stochastic problem and solve it using a model-based approach. The
numerical experiment shows that our proposed algorithm is effective at
approximating the optimal Pareto set and is competitive with some previously
proposed methods.
3 - On Multi-objective Ranking And Selection Methods
Susan Hunter, Purdue University, , Guy
Feldman, Raghu Pasupathy
Consider the context of selecting Pareto-optimal systems from a finite set of
systems based on multiple stochastic objectives. We seek a characterization of the
asymptotically optimal sample allocation that maximizes the rate of decay of the
probability of misclassification, i.e., the probability a Pareto system is falsely
estimated as non-Pareto, or a non-Pareto system is falsely estimated as Pareto. We
discuss recent advances in solving this problem.
4 - Precision Irrigation System Optimization Using Subsurface Water
Retention Technology For Multiple Conflicting Objectives
Kalyanmoy Deb, Michigan State University,
Water is precious and recent efforts to achieve precision irrigation with minimum
use of water through subsurface water retention technology (SWRT) are getting
popular. In this study, we have linked a water permeation simulation process with
a multi-objective optimization algorithm to obtain optimized solutions involving
shape and location of subsurface impermeable membranes and simultaneously
obtain optimal surface water supply. The procedure is pragmatic and is
customized for specific soil and crop combination and average precipitation level.
TC45