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INFORMS Philadelphia – 2015
111
4 - Contract or Trust? An Experimental Study Based on VMI Setting
Dezhen Si, Tsinghua University, Shunde Building, Beijing, China,
sidezhen@126.com, Zuo-jun Max Shen, Xiaobo Zhao
We conduct experiments to study decision behaviors in trust game and contract
game under a VMI setting. We recruit both strangers and acquaintances as
subjects to participate in our experiments. The results show that preferences such
as reciprocity and fairness exist in the games, and as a result, acquaintances in the
trust game perform the best. We also develop behavioral models to explain the
findings.
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54-Room 108A, CC
Uncertainty in Demand Response – Identification,
Estimation, and Learning
Cluster: Tutorials
Invited Session
Chair: Josh Taylor, Assistant Professor, University of Toronto,
10 King’s College Rd., SF 1021C, Toronto, ON, M5S3G4, Canada,
josh.taylor@utoronto.ca1 - Uncertainty in Demand Response – Identification, Estimation,
and Learning
Josh Taylor, Assistant Professor, University of Toronto, 10 King’s
College Rd., SF 1021C, Toronto, ON, M5S3G4, Canada,
josh.taylor@utoronto.ca,Johanna Mathieu
Demand response from flexible electric loads such as electric vehicles, air
conditioners and smart home appliances represents a vast, clean and potentially
high-performance resource for the electric power system, but loads are highly
uncertain. In this tutorial, we survey techniques for managing load uncertainty in
demand response for three problem types: identifying load models, estimating
load states and learning these features in conjunction with deploying the loads for
demand response.
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55-Room 108B, CC
Applications of DEA
Cluster: Data Envelopment Analysis
Invited Session
Chair: Kankana Mukherjee, Babson College, Wellesley, Massachusetts,
kmukherjee@babson.edu1 - Analysis of Technological Gap of Agricultural Productivity among
Sub-Saharan African Countries
Olajide Abraham Ajao, PhD, Ladoke Akintola University of
Technology, Agricultural Economics Department, Ogbomos,
Nigeria,
oaajao57@lautech.edu.ng,Ogunniyi Laudia Titilola,
Abdulrasheed Mutolib
The study compared the productivity differences of technical efficiency and
technological gap ratios in SSA agriculture by adapting metafrontier DEA
approach using cross-country panel input-output data obtained from the FAO. It
was found that the metafrontier scores varied widely among the countries and
also, the regional differences in the production technologies was observed
2 - Capacity Utilization and Energy Efficiency in Indian Manufacturing
Kankana Mukherjee, Babson College, Wellesley, MA,
United States of America,
kmukherjee@babson.eduThis study uses Data Envelopment Analysis and data from the Annual Survey of
Industries, India, to measure capacity utilization and explores the relationship
between an energy efficiency index and a capacity utilization index for each of
the energy intensive industries in India over the period 1998-99 through 2007-
08.
3 - The Analysis of Productivity Pattern of Cereals in
Nigeria (1995 - 2006)
Ogunniyi Laudia Titilola, Ladoke Akintola University of
Technology, Agricultural Economics Department, Ogbomos,
Nigeria,
titiogunniyi@yahoo.com,Olajide Abraham Ajao,
Gbenga Fanifosi
This study analysed the productivity pattern of cereals in Nigeria between the
periods of 1995-2006 using Data Envelopment Analysis to estimate total factor
productivity(TFP)index. A decomposition of TFP measures revealed that
productivity is due largely to technological change over the reference period and
the technical efficiency indexes showed Taraba state and the Federal Capital
Territory(FCT) to be consistently efficient and lie on the best - practice frontier.
4 - Economic Measures of Capacity Utilization: A Nonparametric
Cost Function Analysis
John Walden, Economist, NOAA/NMFS/NEFSC, 166 Water St.,
Woods Hole, MA, 02543, United States of America,
john.walden@noaa.gov,Subhash Ray
Capacity utilization (CU) is an important economic metric which conveys
information about a firm’s output level. We adopt the methods proposed by Ray
(2014) to estimate cost based CU using DEA for a group of commercial fishing
vessels which are characterized by a multi-input, multi-output technology.
Results show the cost minimizing output level and CU for vessels operating in the
years 2007-2012, and how these have changed in the light of recent regulatory
shifts.
5 - Technical Efficiency Gains from Two Land Management Options in
Maize Farming, Southwestern Nigeria
Luke Olarinde, Dr, Ladoke Akintola University of Technology,
Department of Agricultural Economics, PMB 4000, Ogbomoso,
Oy, 210001, Nigeria,
loolarinde@lautech.edu.ngThis study investigated the contribution of two Land management (LM) options
(crop protection and crop management practices) to technical efficiencies (TEs) in
Maize farming in Southwestern Nigeria. Data Envelopment Analysis (DEA)
results (for the TE gains) indicate slight differences in the TEs of farms in the two
LM options.
SC56
56-Room 109A, CC
New Directions in Locational Analysis
Sponsor: Location Analysis
Sponsored Session
Chair: Dmitry Krass, Rotman School of Management,
105 St.George Street, M5S 3E6, Canada,
Krass@rotman.utoronto.ca1 - The Big Tetrahedron Small Tetrahedron Method for Three
Dimensional Location Problems
Rina Nakayama, Nanzan University, 18 Yamazato-cho, Showa-
ku, Nagoya, Japan,
m14ss007@nanzan-u.ac.jp,Zvi Drezner,
Atsuo Suzuki, Tammy Drezner
We extend the Big Triangle Small Triangle method to three dimensions. We call it
the Big Tetrahedron Small Tetrahedron method. We apply it to three dimensional
location problems such as three dimensional Weber problem with Attraction and
Repulsion (WAR) and time space location problems.
2 - Locating a New Facility to Maximize its Voronoi Region
Dmitry Krass, Rotman School of Management, 105 St.George
Street, M5S 3E6, Canada,
Krass@rotman.utoronto.ca,Jonathan Lorraine
Consider a set of competing facilities in a planar region where demand is
continuously distributed and the trading area of each facility is its Voronoi cell (all
points closest to the facility). We wish to add a new facility that will capture as
much demand as possible. We develop a fast solution method based on Big
Triangle-Small Triangle approach. The method is applicable to both uniform and
non-uniform demand distributions. Applications to real-life facility sets will be
demonstrated.
3 - Planning Service Maintenance under Disruption Threats
Mozart Menezes, Associate Professor, Kedge Business School-
Bordeaux, 680 Cours de la Libération, Bordeaux, 33405, France,
mozart.menezes@me.com, Dmitry Krass
We investigate the situation where facilities serving nodes may have service
disrupted forcing nodes to be served by facilities providing service at higher cost.
Disruption threats can be reduced when facilities undergo maintenance at a cost.
The decision maker also incurs cost for repairing facilities and for maintaining
facilities. We focus on the trade-off between planned maintenance versus
allowing facilities to continue operation but risking a much higher cost when
disruption happens.
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