Index
Applications of Management Science
ISBN: 978-1-83867-001-6, eISBN: 978-1-83867-000-9
ISSN: 0276-8976
Publication date: 11 September 2020
This content is currently only available as a PDF
Citation
(2020), "Index", Lawrence, K.D. and Pai, D.R. (Ed.) Applications of Management Science (Applications of Management Science, Vol. 20), Emerald Publishing Limited, Leeds, pp. 233-237. https://doi.org/10.1108/S0276-897620200000020018
Publisher
:Emerald Publishing Limited
Copyright © 2020 Emerald Publishing Limited
INDEX
Adaptive Boosting (AdaBoost)
, 60, 64, 67
Additive model (ADD)
, 166
Aggregation
, 130–131, 133, 136–137
Agrarian society
of depressed economies
, 99–102
Ukrainian agrarian society
, 101–102
Allocative efficiency
, 167, 170
Analogues
, 109
Analytic Hierarchy Process (AHP)
, 36
AHP-GP
, 45
Analytic network process (ANP)
, 35, 36
Analytical models
, 60
Approximate methods
, 34–35
Artificial intelligence (AI)
, 60
Artificial neuro-fuzzy systems (ANFIS)
, 60–61
Australian universities
, 145
“Background” poverty
, 101–102
Benchmark private universities
, 144
Benchmark public universities
, 144, 151
Benchmarking
, 201
DEA
, 210–214
OWA
, 210–214
results
, 208
WLC model
, 209–210
“Best” practices
, 208
Branch-and-bound method
, 32, 34
Branding of rural areas
, 99
Business activity
, 108
Capacity monitoring of Poverty
, 104–105
Carbon emission
, 14, 16
CART algorithm
, 63
Case-based reasoning (CBR)
, 35
Charnes, Cooper, and Rhodes DEA model (CCR DEA model)
, 158–159, 215–216
Charnes, Cooper, and Rhodes model (CCR model)
, 148, 165–166
Classical MPNP model
, 9–10
Climate change
, 137
Closed loop supply chain network (CLSC network)
, 2–3, 10
linear PP
, 6–9
literature review
, 3–5
MCLP
, 6
numerical example
, 19
problem description and assumptions
, 9–11
problem notation and formulation
, 11–19
product substitution
, 5–6
results
, 19–21
Composite indicators
, 118, 124, 126, 133–136
Composite vulnerability indices
, 124–129
Conditional value at risk (CVaR)
, 4, 7, 8–9, 11–12, 33, 38–39
Constant returns to scale technology (CRS technology)
, 153, 172
efficiency
, 172
Constraints
, 16–19, 47–48
order-to-period assignment constraints
, 47
producer capacity
, 47
risk constraints
, 48
supplier selection constraints
, 47
Consumer credit
, 59–60
Consumer drug stores
, 159
Consumer loan evaluation using machine learning techniques
AdaBoost
, 64, 67
data
, 61–62
decision trees
, 63–64
empirical analysis
, 66–67
methodology
, 62–63
SVMs
, 37, 66
Consumer satisfaction
, 175
CPLEX Solver
results
, 50
sets and parameters in
, 48
Cross-efficiency model
, 166
CVS
, 157, 159–160
Data
, 61–62
Data envelope
, 216–217
Data envelopment analysis (DEA)
, 35, 118, 144, 158–159, 165–166, 188–189, 208–209, 210–214
data and methodology
, 150–153
drug store efficiency
, 160
empirical analysis
, 153–155
mathematical description
, 120–122
model
, 146–150
model results
, 222
results
, 160
runs for consumer drug stores
, 159
Decision strategy
, 122
Decision trees
, 63–64
learning
, 63
models
, 67
Decision-makers
, 118
Decision-making units (DMUs)
, 35, 146–147, 158, 188, 209, 217, 224
composite performance measures
, 209
geometric visualization
, 190–201
performances measures
, 219–223
real-life numerical example
, 201–204
standardized dataset
, 218
technically efficient
, 191, 198, 200
δ-degree of freedom (DF)
, 189–190
δ-SBM
, 188–189, 194–199, 201, 204
Demand estimation
, 26
Dynamic programming
, 34
Economic
behavior model
, 99
efficiency
, 167, 170
poverty
, 101
Economical objective
, 2–3
Effectiveness
, 173, 175
Efficiency
, 147, 157–158, 167, 175
measurement
, 170–173
of operation
, 208
Empirical analysis
, 66–67
of DEA
, 153–155
Enhanced Russell Measure (ERM)
, 165–166
Environmental objective
, 2–3
Environmentally conscious manufacturing and product recovery (ECMPRO)
, 3
Epsilon KAM (ε-KAM)
, 167, 176–180, 182–183
Exact methods
, 34
Flower selling
, 13–15
Fractional linear programming model
, 147
Fractional program
, 147–148
Free disposal hull technology (FDH technology)
, 172
Fuzzy programming method
, 3–4
Fuzzy set theory
, 35
General circulation models (GCMs)
, 119–120
Generalized inverse distribution function
, 11
Genetic algorithm (GA)
, 35
Gini impurity
, 63
Global warming
, 2
Goal programming
, 36
Government appropriations (GA)
, 151–152
Greenhouse gases (GHGs)
, 2
Heuristics
, 35
Higher education in United States
, 144
Hydrologic Unit Codes (HUCs)
, 118
composite vulnerability indices for
, 124–129
relative ranks
, 130
Industrialization
, 1–2
Information
gain
, 63
protection
, 43
Innovative portfolio approach
, 40
Institute for Water Resources (IWR)
, 118
Integrated AHP-GP approach
, 36–37
Integrated Climate and Land Use Scenarios (ICLUS)
, 119–120
Interactive poverty maps
, 99
International Civil Aviation Organization
, 180–181
KAM
, 180–184
Kroger
, 157, 159–160
Labor efficiency
, 157–158
Linear physical programming (Linear PP)
, 6–9
soft class functions
, 8
Linear programming
model
, 159
problem
, 149
Linear δ-SBM
, 189
Machine learning (see also Consumer loan evaluation using machine learning techniques)
, 62–65
“Marginal” poverty
, 101
Market transformations
, 98–99
Mathematical programming
, 35–36
Maximal Covering Location Problem (MCLP)
, 6
Mean-variance analysis
, 6–7
Membership coverage function
, 16
Methodology
, 62–63
Mixed integer programming models
, 33
Mixed-integer linear programming model
, 37
Modified (Outputs-Only) Data Envelopment Analysis
, 122–123, 216–219
Moody’s Investors Services
, 144
Multicriteria techniques
, 208–209
Multiobjective optimization problems
, 37
Multiple criteria optimization
, 37, 45
Multiple discriminant analysis models
, 60–61
Multiple-criteria decision making (MCDM)
, 38
approaches for supplier selection
, 38
evaluation criteria
, 38
Multiproduct newsvendor problem (MPNP)
, 4–5
classical MPNP model
, 9–10
comparison between models
, 21
flowers
, 13–15
literature review
, 5–7
models
, 15–20
practical examples
, 13
results
, 16
risk aversion
, 10–11
risk measures in newsvendor problem
, 7–9
VaR and CVaR
, 11–12
Newsvendor model
, 4–5
scripts of
, 21, 25–26
Newsvendor problem
, 4
risk measures in newsvendor problem
, 7–9
Non-CRS technical inefficiency
, 172–173
Noncontrollable factor
, 180–181
Nontechnical efficiency
, 170
Objective function
, 47
“One-vs-rest” approach
, 65
Operational effectiveness (OE)
, 226, 226
Operational efficiency
, 226
Optimization methods
, 33–34
Order rated effectiveness model (ORE model)
, 118, 224–229
comparison with WLC, and DEA results
, 131–136
comparison with WLC results
, 130–131
data description for six constituent indicator measures of vulnerability
, 119–120
mathematical description
, 120–122
model
, 123–124
model results
, 228
optimization weights
, 131
Order-to-period assignment constraints
, 47
Ordered weighted averaging (OWA)
, 118, 208–214
applying to input data
, 124–129
maximum entropy weights for six indicators
, 124
rated effectiveness model
, 123–124, 226–229
Outputs-only DEA model (BOD model)
, 216–217
Overall efficiency
, 167, 170
Physical programming approach (PP approach)
, 2–3
Poverty
, 98
Price efficiency
, 167, 170
Private universities
, 144, 151
statistics of data
, 151–152
Problem formulation
, 46–48
constraints
, 47–48
objective function
, 47
sets and parameters
, 46, 48
variables
, 46
Producer capacity
, 47
Product substitution
, 5–6
Production function (production frontier)
, 168–169, 173–174
Production Possibility Set (PPS)
, 165–166, 168–169
Production theory
, 168
Productive efficiency
, 167
Productivity
, 157–158, 167
measurement
, 173–175
Public universities
, 144
statistics of data
, 151–152
variables to evaluating
, 153
Ranking DMUs
, 201
Redundancy strategy
, 45
Relative efficiency
, 167, 170, 172
Risk aversion
, 10–11
Risk constraints
, 48
Risk measures
in newsvendor problem
, 7–9
in supply chain
, 38–39
Risk spectrum function
, 11
Rite Aid
, 157, 159–160
Rural development
, 99
Scalarized problem
, 37
Sets and parameters
, 46, 48
Simple multiattribute rating technique (SMART)
, 35
Slack-based measure model (SBM model)
, 166
Socio-economic development of rural areas
, 102–104
Specific heuristics
, 35
Standards
, 109
Supplier selection
, 38
constraints
, 47
MCDM for
, 38
Supply chain
, 31–32
AHP
, 36
approximate methods
, 34–35
case study
, 48–49
DEA
, 35
evaluation criteria
, 38
exact methods
, 34
goal programming
, 36
managerial insights
, 44–46
mathematical programming
, 36
MCDM
, 38
mitigating impact of disruptions in
, 39–41
multiple criteria optimization
, 37
optimization methods
, 33–34
practical example
, 46
prevention strategies
, 41–42
problem formulation
, 46–48
protection strategies
, 42–43
recovery strategies
, 43–44
response strategies
, 42
results
, 49–53
risk measures in
, 38–39
strategies for dealing with risks
, 44
Support vector machines (SVM)
, 60, 64–66
Technical efficiency
, 167–168, 170, 172, 175
measurement
, 168–169
Technically inefficient production function
, 168
Territorial branding
, 102–104, 106–108
capacity monitoring
, 104–105
model and overcoming in Agrarian society of depressed economies
, 99–102
monitoring of experience
, 106–109
socioeconomic indicators of studied rural areas
, 110–111
Ukrainian agrarian society
, 101–102
Ukrainian analog
, 105
Uncontrollable factors
, 180–184
Universities
, 144
efficiency scores for
, 154
Value at risk (VaR)
, 6–9, 11–12, 38–39
Variable returns to scale technology (VRS technology)
, 172, 203
Variables
, 46
Virtual input
, 146–147
Virtual output
, 146–147
Vulnerability
, 118
data description for six constituent indicator measures
, 119–120
Walgreens
, 157, 159–160
Walmart
, 157, 159
Weighted average (see Weighted Linear Combination (WLC))
Weighted linear combination (WLC)
, 118, 122, 208–210
method results
, 221
model
, 209–210
Xerox Corporation
, 2
- Prelims
- Section A Applications of Optimization
- Multiobjective Newsvendor Models with CVaR for Flower Industry
- Selected Multiple Criteria Supply Chain Optimization Problems
- Evaluating Consumer Loans Using Machine Learning Techniques
- Use of Maximal Covering Location Problem to Design a Closed Loop Supply Chain Network Under Product Substitution
- Territorial Branding as an Instrument for Competitiveness of Rural Development
- Section B Data Envelopment Analysis Applications
- Developing Composite Indices for Assessing Vulnerability to Floods due to Climate Change: Applying the Order Rated Effectiveness Modeling Method
- A Comparative Analysis of Public and Private Universities in the United States Using Data Envelopment Analysis Models
- Productivity in the US Consumer Drug Store Business
- Section C Data Envelopment Analysis
- Decision-making and Productivity Measurement
- How to Deal with Numbers of Decision-Making Units and Number of Variables in Multiple Input-Output Production Functions
- Evaluating Effective Performance Using Ordered Weighted Averaging and Data Envelopment Analysis Optimization
- Index