Informs 2015

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INFORMS Philadelphia – 2015
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2 - Impact of Top Service Designers on Experience
Service Performance
Gregory Heim, Associate Professor, Mays Business School at Texas
A&M University, Wehner Hall 320, College Station, TX, 77843-
4217, United States of America,
Service firms today leverage many designers known for design excellence. Despite
expanding use of top designers, no research examines practical implications of top
designers as compared to mainstream designers. In considering whether to use
top designers, service managers must consider benefits from investment in high
quality designs. Using data on golf courses and golf course designers, we study
how top designers influence performance of Texas golf courses.
3 - Applying Normalized Systems Theory to Service System Design
Ralph Badinelli, Professor, Virginia Tech, Dept. of Busines
Information Technology, Virginia Tech 0235, Blacksburg, VA,
24061, United States of America,
This paper applies Normalized Systems Theory (NST) as a new framework for the
modular design of service systems. We synthesize a prescription for service system
design from principles of NST and principles of the Viable Systems Approach
(VSA). A mathematical model of the effects of system coupling on evolvability
and entropy is derived. Evolvability and entropy are related to autopoiesis,
homeostasis and viability. An example of an NST-inspired service system design is
provided.
SA53
53-Room 107B, CC
Experiments in Supply Chains
Sponsor: Behavioral Operations Management
Sponsored Session
Chair: Andrew Davis, Cornell University, 401J Sage Hall, Ithaca, NY,
14850, United States of America,
1 - Inventory Decisions under Epistemic and Aleatory
Demand Uncertainty
John Aloysius, University of Arkansas, WCOB 475d University of
Arkansas, Fayetteville, AR, 72701, United States of America,
, Siqi Ma
Research on inventory decisions under uncertainty has primarily focused on
aleatory uncertainty due to stochastic variability. Epistemic uncertainty due to a
lack of confidence in knowledge however routinely features in such decisions.
Our experiment orthogonally manipulates the two forms of uncertainty in a
newsvendor task. We partial out biases due to each form of uncertainty and also
report on learning as well as interaction effects.
2 - Bargaining and the Allocation of Risk in Supply Chains:
An Experimental Study
Kyle Hyndman, University of Texas at Dallas, 800 W Campbell Rd
(SM 31), Richardson, TX, 75080, United States of America,
Andrew Davis
We study the impact of bargaining and inventory risk location in a supply chain.
We conduct a human-subjects experiment where a retailer and supplier engage in
free-form bargaining over wholesale price, order quantity and inventory location.
We show: (1) our bargaining environment leads to higher efficiency than in past
studies; (2) the party incurring the inventory risk always earns a substantially
lower profit than the other party; (3) more restrictive environments lead to lower
efficiency.
3 - Sharing in the Benefits of Learning-by-doing: A Laboratory Study
of Procurement Auction Mechanisms
Blair Flicker, PhD Candidate, The University of Texas at Dallas,
800 W Campbell Road, Mailstop AD23, Richardson, TX, 75208,
United States of America,
Wedad Elmaghraby, Elena Katok
Buyers looking to share in suppliers’ learning-by-doing savings can either (i) draft
long-term contracts with payment reduction schedules or (ii) encourage
competition via sequential short-term contracts. Theoretically, there is minimal
cost difference between the two approaches, but behavioral findings suggest that
humans do respond differentially to the two mechanisms.
4 - Supply Base Diversification in the Presence of High Impact,
Low Probability Supply Disruptions
Doug Thomas, Penn State, 463 Business Building, University
Park, PA, 16802, United States of America, ,
Mirko Kremer, Kyle Goldschmidt, Chris Craighead
We investigate sourcing decisions in the presence of high impact, low probability
supply disruptions. We develop a model that captures a key sourcing tradeoff: A
consolidated (diversified) supply base reduces (increases) transaction costs but
increases (reduces) the exposure to disruptions. We predict, and using a
laboratory experiment, find evidence for, an oscillating pattern - decision makers
diversify immediately after a severe disruption and consolidate during stretches
without disruptions.
SA54
54-Room 108A, CC
Data-Driven Stochastic Programming
using Phi-Divergences
Cluster: Tutorials
Invited Session
Chair: Guzin Bayraksan, Associate Professor, The Ohio State University,
Integrated Systems Engineering, Columbus, OH, 43209, United States
of America,
1 - Data-Driven Stochastic Programming using Phi-Divergences
Guzin Bayraksan, Associate Professor, The Ohio State University,
Integrated Systems Engineering, Columbus, OH, 43209,
United States of America, , David K. Love
Phi-divergences provide a measure of distance between two probability
distributions. They can be used in data-driven stochastic optimization to create an
ambiguity set of distributions centered around a nominal distribution and hedge
against distributional uncertainty. In this tutorial, we present two-stage models
with distributional uncertainty using phi-divergences and tie them to risk-averse
optimization. We examine the value of collecting additional data and discuss
convergence properties.
SA55
55-Room 108B, CC
Airline Economics: Competition and Collaboration
Sponsor: Aviation Applications
Sponsored Session
Chair: Yi Liu, UC Berkeley, 107 McLaughlin Hall, Berkeley, Ca, 94720,
United States of America,
1 - Airline Competition and Market Frequency: A Comparison of the
S-curve and Schedule Delay Models
Yi Liu, UC Berkeley, 107 McLaughlin Hall, Berkeley, CA, 94720,
United States of America, , Mark Hansen
We compare two models for an airline market served by identical carriers who
compete on fare and frequency: s-curve model and schedule delay model. They
only differ structurally with respect to how they handle frequency competition.
The demand side of our model is an approximation of a nested logit model which
yields endogenous travel demand by including not travelling in the choice set.
The results of our comparison support the schedule delay model over the s-curve
model.
2 - Game-theoretic and Empirical Models for Airline Capacity and
Fare Decisions under Competition
Vikrant Vaze, Assistant Professor, Dartmouth, 14 Engineering
Drive, Hanover, NH, 03755, United States of America,
, Reed Harder
Airline capacity and fare decisions under competition affect passenger choice and
the overall efficiency of airline networks. We develop Nash equilibrium models as
well as empirical models to characterize these interactions. We analytically prove
desirable properties of the resulting games and describe numerical experiments
that extend our theoretical results to more complicated competition settings. Our
results, validated against real-world data, provide new insights into airline
competition.
3 - An Equitable and Collaborative Mechanism for Scheduling
Interventions at Congested Airports
Alexandre Jacquillat, PhD Candidate, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Building E40-240,
Cambridge, MA, 02116, United States of America,
, Vikrant Vaze
Flight scheduling interventions can mitigate airport congestion by controlling the
imbalances between peak-hour demand and capacity. We design, optimize and
assess non-monetary congestion-mitigating scheduling mechanisms that ensure
inter-airline equity and enable airline collaboration. Theoretical and
computational results suggest that large equity gains can be achieved at small
efficiency losses and that accounting for airline preferences can improve the
outcome of scheduling interventions.
SA53