2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
322
TC57
Music Row 5- Omni
New Topics in Behavioral Operations
Sponsored: Behavioral Operations Management
Sponsored Session
Chair: Leon Matias Valdes, Massachusetts Institute of Technology,
77 Massachusetts Avenue, Cambridge, MA, 02139, United States,
1 - Observational Learning Through Inventory Availability Information:
Empirical And Field Evidence
Ruomeng Cui, Kelley School of Business, Indiana University,
Bloomington, IN, 47401, United States,
Achal Bassamboo, Dennis Zhang
Consumers, when making such purchasing decisions, tend to be influenced by
others’ actions, i.e., observational learning, or the out-of-stock pressure, i.e.,
product availability information. Using a unique dataset of more than thousands
of daily deals, we empirically measure the herding effect. Well-sold products in
the last hour tend to attract more customers in the next hour. The phenomenon
persists after controlling for alternative explanations such as consumer reviews,
search/experience goods and discount depth. We study the underlying drivers:
observational learning or out-of-stock risk.
2 - The Decision To Recall: A Behavioral Investigation In The Medical
Device Industry
George Ball, Indiana University, Kelley School of Business,
Karen L Donohue, Rachna Shah
The decision to recall can impact a manager’s career and the performance of the
firm. We identify a set of situational and dispositional factors that may influence
the recall decision despite not being specified by the FDA. We test these factors
through an experiment with a Fortune 500 firm. We find that a physician’s
inability to detect a defect in the product and understanding the root cause of the
defect increases the likelihood of recalling the product but these factors vary
across individuals. We find that an individual’s cognitive reflection level helps
divide managers into two groups, those who are more influenced by situational
factors and those who are more influenced by dispositional factors.
3 - The Behavioral Impact Of Queueing Visibility On Server
Effort Allocation.
Yaroslav Rosokha, Purdue University,
Masha Shunko, Julie Niederhoff
Using behavioral lab experiments we explore the impact of feedback on workers’
effort allocation in a queueing environment with multiple human servers. We
focus on the visibility of workload and the visibility of other servers’ effort as
mechanisms controlling feedback.
4 - Supply Chain Visibility And Social Responsibility: Investigating
Consumers’ Behaviors And Motives
Leon Valdes, Massachusetts Institute of Technology,
Tim Kraft, Yanchong Zheng
We conduct an experiment to investigate: (i) when does supply chain visibility
impact consumers’ valuations of social responsibility (SR)? And (ii) what roles do
reciprocal motives and prosocial orientations play in affecting their valuations?
We show that consumers value visibility when workers are disadvantaged or
when consumers use the lack of visibility as an excuse not to pay for SR. We also
observe that high prosocial consumers do not exhibit reciprocal motives, while
these motives can have a significant impact on low prosocial consumers’
valuations. Our work thus identifies when there is a revenue benefit to greater
visibility and what information best resonates with different consumers.
TC58
Music Row 6- Omni
Energy XII
Contributed Session
Chair: Kenneth Bruninx, Post-doctoral researcher, KU Leuven, Leuven,
Belgium,
1 - The Relationship Between Energy Consumption And
Economic Development
Yuan Qian, Tsinghua University, Beijing, China,
Pingke Li
This paper estimates the causal relationship between aggregate energy
consumption, disaggregate energy consumption and real GDP of China for the
1994-2013 period. Results indicated that bidirectional Granger causality runs
from total energy consumption to real GDP and from industrial energy
consumption to real GDP but no Granger causality between real GDP and
transport energy consumption.
2 - Applying The Modern Portfolio Theory For A Dynamic Energy
Portfolio Allocation In The Electricity Markets
Reinaldo Crispiniano Garcia, Associate Professor, University of
Brasilia - UnB, Faculty of Technology, Industrial Engineering
Department, Brasilia, 70904-970, Brazil, ,
Javier Contreras, Janiele Custodio, Virginia Gonzalez
New energy markets undergoing deregulation induce participants to face
increasing competition and volatility, where the objective of a Generation
Company (Genco) is to maximize their profit while minimizing their risk. This
work proposes two MPT models applying the Mean Variance Criteria (MVC) and
the Conditional Value at Risk (CVaR) one. The MPT models are combined with a
Generalized Autoregressive Conditional Heteroskedastic (GARCH) prediction
technique for a Genco to optimally diversify its energy portfolio. The two models
are applied to the PJM electricity market showing their capabilities and
comparisons between them helping decisions makers to apply these two models
as tools for a Genco.
3 - Carbon Dioxide Source Selection And Analysis
Xin Li, Pennsylvania State University, University Park, PA, 16803,
United States, , Jose Antonio Ventura,
Luis F. Ayala H., Uday Shanbhag
This paper aims at identifying and analyzing potential sources of CO2 from power
plants and industrial facilities in any geographical location in the U.S. that can be
used to supply CO2 for fracturing operation in well pads. Different approaches to
capture CO2 from power plants and industrial sources, as well as their
corresponding technological maturity, are discussed. Detailed models to calculate
costs incurred by CO2 capture in two types of prime candidates for CO2 capture,
coal-fired power plants (CPP) and high-purity CO2 sources (HPS), are presented.
4 - Cooptimization Of Series Facts Device Set Points And Generation
Dispatch
Mostafa Sahraei-Ardakani, Assistant Professor, University of Utah,
1249 E Spence Avenue, Apt 242, Salt Lake City, UT, 85281,
United States,
No energy or market management system today optimizes the set point of flexible
AC transmission system (FACTS) devices along with generation dispatch, due to
the computational complexity of the problem. We propose an extremely effective
and fast linear programming heuristic that facilitates such co-optimization. As a
result, the operation of FACTS devices can be significantly enhanced leading to
substantial economic and reliability gains.
5 - A Probabilistic Unit Commitment Model
Kenneth Bruninx, Post-doctoral researcher, KU Leuven, Leuven,
Belgium, Erik Delarue
Stochastic unit commitment models allow calculating an optimal trade-off
between the cost of scheduling and activating reserves, load shedding and
curtailment, but may become computationally intractable for real-life power
systems. Therefore, we develop a probabilistic unit commitment (PUC)
formulation, which allows internalizing the reserve sizing and allocation in a
deterministic unit commitment problem, considering the full cost of reserve
allocation and activation. This PUC formulation yields UC schedules that are
nearly as cost-effective as the theoretical optimal solution of the stochastic model
in calculation times similar to that of a deterministic equivalent.
TC59
Cumberland 1- Omni
Robust and Reliable Optimization in Transportation
and Logistics
Invited: Transportation Science & Logistics
Invited Session
Chair: Ehsan Jafari, University of Texas, Hart Lane, Austin, TX, 78731,
United States,
1 - Multicriteria Shortest Path Problem For Electric Vehicles In
Stochastic Networks
Ehsan Jafari, University of Texas, Hart Lane, Austin, TX, 78731,
United States, , Stephen Boyles
This presentation focuses on the problem of finding a prior path for a single
electric vehicle in a network with stochastic travel times. There are a number of
non-identical charging stations (different charging prices and charging rates)
through the network and charge depletion rate is modeled as a function of arc
length and arc travel time. We formulate the problem as a multicriteria shortest
path problem with three components: reliability, cost and time.
TC57