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