Informs 2015

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INFORMS Philadelphia – 2015
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4 - On Integrating DEA and AHP for the Facility Layout Design in
Manufacturing Systems
Toloo Mehdi, Technical University of Ostrava, Ostracew,
Czech Republic,
Analytic hierarchy process (AHP) is a decomposition multiple-attribute decision
making (MADM) method, which can represent human decision making process
and help to achieve better judgments based on hierarchy, pair-wise comparisons,
judgment scales, allocation of criteria weights and selection of the best alternative
from a finite number of variants by calculation of their utility functions. Data
envelopment analysis (DEA) is a well-known non-parametric method to evaluate
the performance of a set of homogeneous decision making units (DMUs) with
multiple inputs and multiple outputs.
5 - The Efficiency of Tunisian Universities: An Application of a
Two-Stage Dea Approach
Samir Srairi, Ministry of Higher Education and Scientific
Research, 14 Avenue de Tunis, Arian, 2080, Tunisia,
This paper examines the efficiency of eleven universities in Tunisia during the
period 2009-2013. Regression analysis suggests that a higher share of professors, a
higher number of women in academic staff and a better quality of student in
secondary education improve the efficiency of the university.
TD56
56-Room 109A, CC
Project Selection, Evaluation and Collaboration
Cluster: New Product Development
Invited Session
Chair: Yaozhong Wu, National University of Singapore, NUS Business
School, Singapore, Singapore,
1 - An Experiemental Study of Idea Selection Process
Zhijian Cui, Assistant Professor of Operations and Technology
Management, IE Business School, Calle de Maria de Molina 12,
Madrid, 28006, Spain, , Shijith Payyadak,
Dilney Gonçalves
In this study, we design several online experiements to compares the efficacy of
two commonly observed processes of idea selection: scoring vs. ranking. In
scoring process, subjects are asked to evaluate the quality of each idea and give a
score while in ranking process, subjects are asked to only rank the ideas according
to their prefereces. We find that the choice of idea selection process depends on
some contextual factors.
2 - Overvaluation of Process Innovation Ideas
Fabian Sting, Erasmus University Rotterdam, Rotterdam,
Netherlands, Christoph Fuchs, Maik Schlickel
Ideas by employees are a vital source for innovation. But are such ideas
overvalued by their creators? If so, which ideas in particular? Drawing on a
unique data set that comprises the generation, election, and implementation of
process improvement ideas of an automotive supplier, we identify antecedents of
overvalued ideas. Overvaluation is greater for ideas generated by higher-level
employees, collaboratively versus individually, and by employees with previously
lower ideation success.
3 - Project Evaluation and Selection via Risk-adjusted Net
Present Value
Nicholas G. Hall, The Ohio State University, Fisher College of
Business, Columbus, OH, United States of America,
, Zhixin Liu, Wenhui Zhao
We consider a project with risk that declines over time as its tasks are completed,
as reflected in a declining discount rate. The objective is to maximize the NPV of
the project. This problem is highly nonlinear, since the discount rate at any point
in time is a function of previous scheduling decisions. We solve this model and
show that risk-adjusted NPV varies significantly from traditional NPV, and that the
use of the risk-adjusted measure significantly improves project selection decisions.
4 - Resource Competitions for Research Projects
Pascale Crama, Singapore Management University, 50 Stamford
Road, Singapore, 178899, Singapore, , Anand
Nandkumar, Reddi Kotha
Academic research is funded by governments, but is often seeded through grants
from university administered research funds (UARF) and other charitable
institutions. We compare the effectiveness of UARF and other sources of funds in
obtaining subsequent federal funding and value creation. We build a
parsimonious model that can explain the superior productivity of UARF funding
and make recommendations on the ideal way to organize UARF funding.
TD57
57-Room 109B, CC
Modeling the Economics of Low-Carbon
Power Systems
Sponsor: ENRE – Energy I – Electricity
Sponsored Session
Chair: Todd Levin, Energy Systems Engineer, Argonne National
Laboratory, 9700 S Cass Ave, Lemont, IL, 60439,
United States of America,
1 - Revenue Sufficiency and Resource Adequacy in Systems with
Variable Generation Resources
Todd Levin, Energy Systems Engineer, Argonne National
Laboratory, 9700 S Cass Ave, Lemont, IL, 60439,
United States of America, , Audun Botterud
An efficient MIP framework is applied to analyze the impact of increasing wind
power capacity on generator profitability. The model is executed with hourly time
steps on a test case that approximates the ERCOT system for a range of wind
capacity levels. We analyze three market policies that support resource adequacy
and find that some additional market incentives may be required to ensure long
term revenue sufficiency and resource adequacy in systems with significant
variable energy resources.
2 - An Approximate Model for Scheduling Energy and Reserve in
Renewable-Dominated Power Systems
Miguel Carrion, Universidad de Castilla - La Mancha,
Av. Carlos III, s/n, Toledo, Spain,
Rafael Zarate-miñano
Since most of renewable energies are non-dispatchable, an appropriate schedule
of reserves in renewable-dominated power systems is crucial. We propose an
alternative formulation that co-optimizes energy and reserve considering the
uncertainty involved in demand and renewable production. This formulation
requires a significantly smaller number of variables and constraints than the
classical stochastic economic dispatch problem. The proposed formulation is tested
in a realistic case study.
3 - Hydroelectric Bid Optimization under Uncertainty
Andy Philpott, University of Auckland, Engineering Science
Department, Private Bag 92019, Auckland, 1025, New Zealand,
, Faisal Wahid, Frederic Bonnans,
Cedric Gouvernet
We consider the problem faced by the operator of a cascade of hydroelectric
generating plants offering energy to a wholesale electricity pool market to
maximize revenue. Both energy prices and uncontrolled inflows to the reservoirs
of the cascade are assumed to be stochastic. We describe a stochastic dynamic
programming model that generates an optimal offer for the next period given
current observed prices. This is solved using SDDP when value functions are
concave or MIDAS when they are not.
4 - Optimal Timing to Invest, Mothball, Reactivate, and
Decommission a Coal Power Plant
Paul Rebeiz, Doctoral Candidate In Operations Mangement,
UCLA Anderson School of Management, 110 Westwood Plaza,
Los Angeles, CA, 90025, United States of America,
Christian Blanco
Transitioning to a low-carbon economy will require most coal power plants to be
replaced by other sources of generation such as wind and solar. We present a
dynamic program to solve for the optimal price signals to invest, mothball,
reactivate, and decommission a coal power plant. We find that our results are
consistent with current industry trends. We conclude with some insights on the
effect of renewable energy policy on mothballing and retiring a coal plant.
TD57