Engineering Analytics: Advances in Research and Applications

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Synopsis of Engineering Analytics
Acknowledgments
Editor Biographies
Introduction
References
1 Interactive Visualization to Support Data and Analytics-Driven Supply Chain Design Decisions
1.1 Introduction
1.2 Decision Making In Supply Chain Design
1.2.1 Characteristics of the Supply Chain Design Decision-Making Problem
1.2.2 Decision Making in the Context of Supply Chain Design
1.2.3 New Perspectives On Decision Making in Supply Chain Design
1.2.4 Synthesis
1.3 Interactive Visual Analytics In Supply Chain Design
1.4 Application to Practice
1.4.1 Distribution Network Design for a Multi-National Chemical Company
1.4.2 Supply Chain Design for a Multi-National Pharmaceutical Company
1.5 Conclusion and Future Research
References
2 Resilience-Based Analysis of Road Closures in Colombia: An Unsupervised Learning Approach
2.1 Introduction
2.1.1 Problem Statement
2.2 Previous Related Works
2.3 Solution Approach for Resilience-Based Analysis of Road Closures
2.4 Road Networks Disruption Analysis
2.4.1 Pre-Processing
2.4.2 Modeling
2.4.3 Key Findings
2.5 Effects of Road Disruptions on Downstream Supply Chains
2.6 Conclusions
Acknowledgements
References
3 Characterization of Freight Transportation in Colombia Using the National Registry....
3.1 Introduction
3.2 Methodology
3.2.1 Data Pre-Processing
3.2.2 Exploring the Potential of Data Through a Visualization Tool
3.2.3 Identification of Behavioral Patterns in Freight Transportation
3.3 Results
3.3.1 Pre-Processing of Information
3.3.2 Visualization and Characterization of Freight Transportation
3.3.2.1 Types of Vehicles
3.3.2.2 Main Origins and Destinations
3.3.2.3 Liquid Cargo and Solid Cargo
3.3.2.4 Routes With the Highest Cargo Flow
3.3.2.5 Most Transported Products
3.3.2.6 Variations in the Main Freight Transportation Variables
3.4 Conclusions
References
4 Data and Its Implications in Engineering Analytics
4.1 Data is a Valuable Resource in Organizations
4.2 A Brief History of Data Analysis
4.3 Descriptive Analytics
4.4 Visual Analytics
4.5 Analytical Tools
4.6 Conclusions
References
5 Assessing the Potential of Implementing Blockchain in Supply Chains Using Agent-Based Simulation and Deep Learning
5.1 Introduction
5.2 Basic Concepts
5.2.1 Supply Chain
5.2.2 Blockchain
5.2.3 Deep Learning
5.2.4 Simulation
5.2.4.1 Agent-Based Simulation
5.2.5 Summary of Agents, Deep Learning, and Blockchain
5.3 Problem Statement and Objective
5.4 Methodology and Framework
5.5 Case Study
5.6 Implementation
5.6.1 Current P2P Organization
5.6.2 Addition of IT Security System Modeled By Using Deep Learning
5.6.3 Addition of Blockchain
5.7 Results
5.8 Conclusions
References
6 Market Behavior Analysis and Product Demand Prediction Using Hybrid Simulation Modeling
6.1 Understanding the Market And Estimating Product Demand
6.2 Markets, Complex Systems, Modeling, and Simulation
6.3 Using System Dynamics and Agent-Based Simulation to Estimate Car Demand
6.3.1 Modeling Market at the Aggregate Level (System Dynamics)
6.3.2 Modeling Market at the Disaggregate Level (Agent-Based)
6.3.3 Integration of Simulation Paradigms
6.3.4 Simulation Runs
6.3.5 Model Optimization
6.3.5.1 The Optimal Number of Simulation Runs
6.3.5.2 The Optimal Number of Agents
6.3.5 Model Validation and Sensitivity Analysis
6.4 Conclusions
References
7 Beyond the Seaport: Assessing the Impact of Policies...
7.1 Introduction
7.2 Literature Review
7.2.1 International Container Transportation
7.2.2 Policymaking for Seaports
7.2.3 The Research Gap and Opportunity
7.3 Methodology
7.3.1 Process and Stakeholder’s Mapping
7.3.2 Secondary Data Collection
7.3.3 System Dynamics Model
7.3.4 Model Validation
7.4 Case Study: Jordan’s Container Transport Chain
7.4.1 Problem Description
7.4.2 Mapping the Process
7.4.3 The Conceptual Framework
7.4.4 Driving System Dynamics Into Practice: A Simulation Approach
7.5 Discussion and Analysis of Results
7.5.1 Status Quo
7.5.2 Results of Status Quo
7.5.3 Results for Multiple Scenarios
7.5.5 Simulation for a One-Year Period
7.5.6 Managerial Insights and Potential Policymaking
7.6 Conclusion and Future Research
References
8 Challenges and Approaches of Data Transformation: Big Data in Pandemic...
8.1 Introduction
8.1.1 COVID-19 and Its Predecessors
8.1.2 Data Collection: Past and Now
8.2 Data And Methods
8.2.1 Data Inconsistencies
8.2.1.1 Data Release Without Verification
8.2.1.2 Poor Standardization of the Collected Data
8.2.1.3 File Format Change
8.2.2 Data Cleansing and Preparation for Analysis
8.2.2.1 Initial Inspection and Cleansing
8.2.2.2 Transitions Correction
8.2.3 Methods for Data Correction
8.2.3.1 K-Medoids
8.2.3.2 Silhouette Cluster Validity Index
8.2.3.3 Transition Matrix
8.3 Results
8.3.1 Confirmation of Transitions Through Dynamic Windows
8.3.2 Transition Probabilities
8.4 Discussion
8.4.1 Strategies for Improving Data Collection
8.4.1.1 Variable Definition
8.4.1.2 File Naming for Storage
8.4.1.3 File Type and Properties
8.4.1.4 Missing Data
8.4.2 Data Cleansing Techniques
8.5 Final Note
References
9 An Agent-Based Methodology for Seaport Decision Making
9.1 Introduction
9.2 Complexity of the Decision-Making Environment In Seaports
9.3 The Need for a Methodology to Support Seaport Decision Making
9.4 Is Agent-Based Methodology the Key?
9.5 Specifying An Interaction/Communication Protocol In An Agent-Based Model
9.5.1 Properties of an Agent-Based Seaport Decision Maker
9.5.2 Multi-Agent Interaction and Communication Protocols
9.5.2.1 IEEE-FIPA
9.5.2.2 BSPL
9.5.3 The Knowledge/Epistemological Level of an Agent-Based Behavior
9.6 Future Research Directions
9.7 Conclusions
References
10 Simulation and Reinforcement Learning Framework to Find Scheduling...
10.1 Introduction
10.2 Planning and Scheduling For Production Systems
10.2.1 Production Scheduling Environments
10.2.2 Integration of Operational and Executional Level
10.3 Learning Scheduling
10.3.1 Markov Decision Process
10.3.2 Learning and Scheduling of Jobs Framework
10.4 Illustrative Example
10.5 Conclusions
Acknowledgments
References
11 An Advanced Analytical Proposal for Sales and Operations Planning
11.1 Introduction
11.2 Background
11.3 Procedures
11.3.1 Predicting Sales
11.3.2 Model for Prescribing Decisions
11.4 Experiment
11.4.1 Using the Random Forest Regressor in Real Data
11.4.2 Using Real Data in a Reduced Supply Chain
11.5 Conclusions
11.6 Future Research
APPENDIX 11A: Random Forest Regressor For The Required Forecasts
APPENDIX 11B: Mixed-Integer Model For The S&Op Support
12 Deep Neural Networks Applied in Autonomous Vehicle Software Architecture
12.1 Introduction
12.2 Materials and Methods
12.2.1 Software Architecture
12.2.2 Convolutional Neural Networks
12.2.3 Convolutional Neural Networks Example
12.2.3.1 Training Workflow
12.3 Results for Autonomous Vehicles With Deep Neural Networks
12.3.1 Data Analysis
12.3.2 Pre-Processing and Data Augmentation
12.3.3 CNN Architecture
12.3.4 Autonomous Vehicle Implementation
12.4 Conclusion
References
13 Optimizing Supply Chain Networks for Specialty Coffee
13.1 The Coffee Industry and Socio-Economic Costs for Coffee Farmers
13.2 Coffee Supply Chains and a Regional Look at Caldas, Colombia
13.2.1 Impact of the Coffee Production Characteristics On the Supply Chain
13.2.2 Shipping Coffee Overseas From Caldas, Colombia
13.3 Structuring the Coffee Supply Chain Network
13.3.1 Supply Chain Network Design
13.3.1.1 Model Formulation
13.3.2 Validating With a Case From Colombia: Café Botero
13.3.2.1 Validation Scenarios
13.3.2.2 Results of the Scenarios and Saving Opportunities
13.3.2.3 Recommendations for Café Botero
13.4 Active Steps Down the Supply Chain to Reduce Costs
13.5 Agenda for Future Research in Coffee Supply Chains
References
14 Spatial Analysis of Fresh Food Retailers in Sabana Centro, Colombia
14.1 Introduction
14.2 Literature Review
14.2.1 Trends and Facts About Food Insecurity
14.2.2 The Link Between Accessibility, Availability, and Affordability
14.2.3 Coupling Supply and Demand for Fruits and Vegetables in Food Environments
14.2.4 Gaps and Contributions
14.3 Methodology
14.3.1 Data Collection
14.3.2 Conceptual Framework
14.3.2.1 Geographical Attributes
14.3.2.2 Demographic and Socio-Economic Characteristics
14.3.2.3 Retail Landscape
14.3.3 Data Modeling and Tools for Analysis
14.3.3.1 Catchment Areas and Buffer Rings
14.3.3.2 Hierarchical Clustering
14.3.3.3 Voronoi Diagrams
14.4 Results and Analysis
14.4.1 Preliminary Distribution Patterns
14.4.2 Socio-Economic Clustering Analysis
14.4.3 Demand and Supply Analysis
14.5 Conclusions
References
15 Analysis of Internet of Things Implementations Using Agent-Based Modeling: Two Case Studies
15.1 Introduction
15.2 Related Work
15.3 Case Study 1
15.3.1 Simulation Model
15.3.2 Three Different Scenarios of the ABM
15.3.3 Conclusion
15.4 Case Study 2
15.4.1 Process Model
15.4.2 Simulation Model
15.4.2 ABM Results
15.4.4 The Return On Investment for the Project
15.4.5 Discussion
References
Index