Real-World Decision Support Systems (eBook)

Jason Papathanasiou is an Assistant Professor at the Department of Business Administration, University of Macedonia, Greece. His PhD was in Operational Research and Informatics and he has worked for a number of years at various institutes. He has organized and participated in many international scientific conferences and workshops. He has published more than 100 papers in international peer referred journals, conferences and edited volumes and has participated in various research projects in FP6, FP7, Interreg and COST; he served also as a member of the TDP Panel of COST and currently serves at the coordination board of the EURO Working Group of Decision Support Systems. His research interests include Decision Support Systems, Operational Research and Multicriteria Decision Making.Nikolaos Ploskas is a Postdoctoral Researcher at the Department of Chemical Engineering, Carnegie Mellon University, USA. His primary research interests are in operations research, decision support systems, mathematical programming, linear programming, and parallel programming. He has participated in several international and national research projects. He is author of more than 40 publications in high-impact journals, book chapters and conferences. He has also served as reviewer in many scientific journals. He was awarded with an honorary award from HELORS (HELlenic Operations Research Society) for the best doctoral dissertation in operations research (2014).Isabelle Linden is a Professor of Information Management at the University of Namur in Belgium, Department of Business Administration. She obtained her PhD in Computer Sciences from the University of Namur. She also holds Masters degrees in Philosophy and in Mathematics from the University of Liege, Belgium. She is member of the CoordiNam Laboratory and the FoCuS Research Group. Combining theoretical computer science and business administration, her main research domain regards information, knowledge and artificial intelligence. She explores their integration within systems as EIS, DSS and BI systems. Her works can be found in several international edited books, journals, books chapters and conferences. She serves as reviewer and program committee member in several international journals, conferences and workshops.

Foreword 7
Preface 9
References 11
Contents 12
Contributors 14
List of Reviewers 16
About the Editors 17
1 Computerized Decision Support Case Study Research: Concepts and Suggestions 19
1.1 Introduction 19
1.2 Understanding Decision Support Systems 20
1.3 Decision Support Case Studies 22
1.4 Examples of DSS Case Studies 24
1.5 How Useful Are DSS Case Studies 27
1.6 Conclusions and Recommendations 28
Note 29
References 30
2 ArgMed: A Support System for Medical Decision Making Based on the Analysis of Clinical Discussions 32
2.1 Introduction 32
2.2 An Iterative Approach to System Development: From Requirements Collection to Field Testing 35
2.3 Requirements and System Architecture 36
2.3.1 ArgMed Requirements 36
2.3.2 System Architecture 37
2.4 System Design and Implementation 39
2.4.1 Discussion Documentation 39
2.4.2 Discussion Interpretation 41
2.4.3 Discussion Analysis 45
2.4.4 ArgMed Implementation 46
2.5 User Interaction 46
2.5.1 A Clinical Discussion 46
2.5.2 Discussion Documentation 48
2.5.3 Discussion Interpretation 49
2.5.4 Discussion Analysis 50
2.6 ArgMed Experimentation 51
2.7 Conclusions 55
References 57
3 The Integration of Decision Analysis Techniques in High-Throughput Clinical Analyzers 59
3.1 Introduction 60
3.2 The Technological Issues of Immunoassay Analyzers 63
3.2.1 The Biochemical Point of View 63
3.2.2 The Engineering Point of View 64
3.3 The Operational Planning in High-Throughput Clinical Analyzers 66
3.4 OR-Driven Solutions to Operational Planning 67
3.4.1 The Proposed Optimization Algorithm: SPT2 67
3.5 Computational Results 72
3.6 Quantifiable Benefits 73
3.6.1 The Clinical Utility Gain 73
3.6.2 Clinical Benefits 75
3.6.3 Monetary Benefits 77
3.7 System Design and Development 78
3.7.1 Class Diagram 78
3.7.1.1 Batch 79
3.7.1.2 Job 79
3.7.1.3 Machine 79
3.7.1.4 Scheduler 79
3.7.2 Activity Diagram 80
3.7.3 User Interface 80
3.7.4 Classification 81
3.8 Lessons Learned 82
3.9 Conclusions 84
References 84
4 Decision Support Systems for Energy Production Optimization and Network Design in DistrictHeating Applications 86
4.1 Introduction 86
4.2 Business Issue n. 1: District Heating Network Design 88
4.2.1 Literature 88
4.2.2 System Requirements Analysis and System Design 89
4.2.3 System Development and User Interface Design 90
4.2.4 Optimization Module 91
4.2.5 System User Experience 92
4.3 Business Issue n. 2: Energy Production Management 93
4.3.1 Literature 94
4.3.2 System Requirements Analysis and System Design 94
4.3.3 System Development and User Interface Design 96
4.3.4 Optimization Module 99
4.3.5 System User Experience 100
4.4 Conclusions 101
References 101
5 Birth and Evolution of a Decision Support System in the Textile Manufacturing Field 103
5.1 Introduction 103
5.1.1 The Problem 104
5.1.2 The Need for a DSS 104
5.1.3 Target Group 105
5.1.4 Existing Procedures 105
5.1.5 Classification 106
5.1.6 Underlying Technologies 107
5.2 System Requirements Analysis 107
5.2.1 Requirements Gathering 108
5.2.2 Requirement Determination and Definition 109
5.2.3 Final System Proposal 109
5.3 System Design and Development 110
5.3.1 The Problem 111
Terminology 111
5.3.2 Class Diagram 114
Order 114
StockPiece 115
Map 115
FabricSheet 115
UserDefinedConstraint 116
5.3.3 Activity Diagram 116
5.4 User Interface 118
5.4.1 A Typical DSS Usage 119
Import from SAP 119
Parameters Setting 119
User-Defined Constraints 119
Check and Run 121
5.4.2 Results and Graphical Maps 121
Graphical Maps 121
Orders vs Sheets 122
KPIs 123
5.5 System Implementation 123
5.5.1 Problem Statement 124
5.5.2 Pattern Generation 125
5.5.3 ILP Model 125
5.6 System User Experience 127
5.6.1 Lessons Learnt 128
5.6.2 System Sustainability 128
5.6.3 System Upgrade and Maintenance Issues 130
5.7 Conclusions 131
Appendix: Mathematical Model 131
QDL 132
ILP Model 134
QDL Minimization Objective 135
Stock Piece Maximum Length Usage 135
Order Required Quantity 135
Cut-Lengths 135
Finish 135
Secondary Objective 135
References 136
6 A Decision Analytical Perspective on PublicProcurement Processes 138
6.1 Introduction 139
6.2 Decision Analysis for Procurement 141
6.2.1 Unreasonable Precision 144
6.2.2 Handling Value Scales Over Qualitative Estimates 146
6.2.3 Deficiencies in the Handling of Value Scales 147
6.3 System Requirement Analysis 149
6.4 System Design 151
6.4.1 Node Constraint Set 152
6.4.2 Comparing Alternatives 153
6.5 System Development 154
6.6 User Interface Design 155
6.7 System Implementation 156
6.8 System User Experience 158
6.9 Concluding Remarks 161
References 162
7 Evaluation Support System for Open Challenges on Earth Observation Topics 164
7.1 Introduction 165
7.2 Evaluation Support System (ESS) Design 169
7.3 Evaluation Support System Requirements Analysis 171
7.3.1 Criteria Definition 172
7.3.2 Normalization 174
7.3.3 Criteria Relative Importance with Weighting Functions 175
7.4 User Interface Design 177
7.5 Evaluation Support System Process: Hierarchical Synthetizing Process for Rating 178
7.5.1 Bottom-Up Hierarchical Synthetizing Process (HSP) 178
7.5.2 Rating Process 180
7.5.2.1 Rating Layers 4 and 5 (Step 5) 180
7.5.2.2 Rating Layers 1, 2 and 3 (Step 6) 181
7.6 System User Experience (Experimental Cases) 181
7.6.1 Example for Demonstrating Step-by-Step Method 182
7.6.2 Illustrative Case for Demonstrating Peer Comparisonof Results 184
7.7 Conclusions 185
References 186
8 An Optimization Based Decision Support System for Strategic Planning in Process Industries: The Case of a Pharmaceutical Company 188
8.1 Introduction 189
8.2 Literature Review and Motivation 190
8.2.1 Literature Review on Real-World Applications of a DSS 190
8.2.2 Motivation for the Development of the Proposed DSS 191
8.3 Stochastic Linear Programming (SLP): An Illustrative Model 192
8.4 Decision Support System 193
8.4.1 Modeling the Pharmaceutical Industry's Production Operations 193
8.4.1.1 Fundamental Elements of Process Industry Production System 194
8.4.1.2 Model Assumptions 194
8.4.1.3 Optimization Steps 195
8.4.2 Database Structure 196
8.4.3 User Interface Development Experience 197
8.4.4 Model and DSS Validation 200
8.5 Application of the DSS to a Pharmaceutical Company 201
8.5.1 The Scale and Scope of Optimization 201
8.5.2 The Process Flow of Tablet Production 202
8.5.3 Stochastic Optimization and Scenario Experiments 202
8.5.3.1 Stochastic Optimization Model 203
8.5.3.2 Variants of the Model 203
8.5.3.3 Stochastic Experiments Design 204
8.6 Results: Analysis and Discussion 205
8.7 User Experiences 206
8.7.1 Lessons from the End User Perspective 206
8.7.2 Key Characteristics of a Good Model-Based DSS 207
8.7.3 Challenges Addressed in Model-Based DSS 208
8.8 Conclusions 209
References 210
9 Decision Support in Water Resources Planning and Management: The Nile Basin Decision Support System 212
9.1 Introduction 212
9.2 Main Characteristics of the Nile Basin and the NB DSS 215
9.3 Users and System Requirements 218
9.4 System Design and Components 224
9.5 System Implementation 227
9.6 User Interface Design 229
9.7 Cases Analyzed with NB DSS 230
9.8 Experiences and Future Prospects for NB DSS 232
9.9 Conclusions 233
References 234
10 The AFM-ToolBox to Support Adaptive Forest Management Under Climate Change 236
10.1 Introduction 237
10.2 System Requirements Analysis 239
10.3 System Design 240
10.3.1 ToolBox DataBase and ToolBox Client 241
10.3.2 Content Management System and Knowledge Base 242
10.3.3 Tools 242
10.4 System Development 243
10.5 User Interface Design 245
10.6 System Implementation 248
10.7 System User Experience 249
10.8 Conclusions 251
References 252
11 SINGRAR—A Distributed Expert System for Emergency Management: Context and Design 255
11.1 Introduction 255
11.1.1 General Considerations 255
11.1.2 Problem Characterization 258
11.1.3 SINGRAR General Characteristics 263
11.2 System Requirements Analysis 265
11.2.1 Context of Use 266
11.2.2 User and Organizational Requirements 268
11.3 System Design 268
11.3.1 Knowledge Management 270
11.3.1.1 Knowledge Acquisition 271
11.3.1.2 Knowledge Coding 272
11.3.1.3 Knowledge Inferencing 273
11.3.1.4 Knowledge Transfer 275
11.3.2 Other SINGRAR Design Features 276
11.4 System Development 277
11.4.1 System Architecture 278
11.4.2 Intelligent System's Typology 280
11.4.3 Knowledge Domains 280
11.4.4 Customization 281
11.4.5 SINGAR Project Chronology 282
11.5 Conclusion 284
References 284
12 SINGRAR—A Distributed Expert System for Emergency Management: Implementation and Validation 287
12.1 Introduction 287
12.2 User Interface Design 288
12.3 System Implementation 293
12.3.1 Repair Priorities Inference Model 294
12.3.2 Resource Assignment Inference Model 296
12.3.3 Forward and Backward Chaining in the Inference Process 305
12.4 System Usability 307
12.4.1 SINGRAR Usability Analysis Using SUMI Method 309
12.4.2 Dynamic Analysis of the Application 310
12.4.3 Analysis of Interfaces and User Interaction 310
12.5 Conclusions 312
References 313
13 Crop Protection Online—Weeds: A Case Study for Agricultural Decision Support Systems 315
13.1 Introduction 316
13.2 System Requirements Analysis 318
13.3 System Design and Problem Solving Technic 319
13.4 User Interface Design 325
13.5 System Implementation 327
13.6 System User Experience 327
13.7 Conclusions 330
References 331
Index 333