Disruptive Technologies and Optimization Towards Industry 4.0 Logistics (Springer Optimization and Its Applications, 214)

Preface
Contents
Contributors
Managing Risks in Smart Warehouses from the Perspective of Industry 4.0
	1 Introduction
	2 Background Knowledge
		2.1 Additive Manufacturing or 3D Printing
		2.2 Artificial Intelligence and Machine Learning: AI/ML
		2.3 Augmented Reality: AR
		2.4 Big Data: BD
		2.5 Blockchain: BC
		2.6 Cyber-Physical System: CPS
		2.7 Digital Twins: DT
		2.8 Internet of Things: IoT
		2.9 Robotics and Robotic Process Automation: RPA
	3 Research Methodology
	4 Results
		4.1 Warehouse Operational Risks and I4.0
			4.1.1 Theoretical Contributions
			4.1.2 Empirical Contributions
		4.2 Warehouse Risks: Receiving
			4.2.1 Theoretical Contributions
			4.2.2 Empirical Contributions
		4.3 Warehouse Risks: Storing
			4.3.1 Theoretical Contributions
			4.3.2 Empirical Contributions
		4.4 Warehouse Risks: Order Dispatching (Combining Picking & Shipping)
			4.4.1 Theoretical Contributions
			4.4.2 Empirical Contributions
		4.5 Warehouse Risks: General Focus
	5 Discussion
	6 Conclusions
	Annex
	Bibliography
Blockchain Technologies as a 4th Industrial Revolution Facilitator in Logistics
	1 Introduction
	2 Theoretical Foundation
		2.1 Introduction
		2.2 Blockchain Technology
			2.2.1 History of Blockchain Technology
			2.2.2 How Blockchain Technology Works
			2.2.3 Applications of Blockchain in Logistics
			2.2.4 Shortcomings and Challenges of Blockchain Technology
			2.2.5 Benefits of Blockchain in Logistics
		2.3 4th Industrial Revolution
			2.3.1 History and Evolution of the Industrial Revolutions
			2.3.2 Key Technologies of the 4th Industrial Revolution
			2.3.3 Potential Applications of the 4th Industrial Revolution in Logistics
			2.3.4 Shortcomings and Challenges of the 4th Industrial Revolution
			2.3.5 Benefits of the 4th Industrial Revolution in Logistics
			2.3.6 Initiatives to Embrace the 4th Industrial Revolution
		2.4 The Logistics Sector
			2.4.1 History of the Logistics Sector
			2.4.2 Subsectors of the Logistics Sector
			2.4.3 Economic Significance of the Logistics Sector
			2.4.4 Incorporation of Technologies in the Logistics Sector
			2.4.5 Challenges Faced by the Logistics Sector
	3 Research Methodology
		3.1 Systematic Literature Review Protocol
		3.2 Systematic Literature Review Results
		3.3 Literature Review
			3.3.1 Transparency in Logistics
			3.3.2 Traceability in Logistics
			3.3.3 Regional and Localized Research
			3.3.4 “Smartification” of Logistics
			3.3.5 Blockchain in Maritime Logistics
	4 Case Studies
		4.1 Introduction
		4.2 The COVID-19 Case
			4.2.1 Impact of the COVID-19 Pandemic on the Logistics Sector
			4.2.2 Recovery and Adaptation in the Logistics Sector
			4.2.3 Recovery from the COVID-19 Pandemic: The Role of 4th Industrial Revolution Technologies and Blockchain in the Logistics Sector
		4.3 Blockchain Applications in Logistics
			4.3.1 Inventory Tracking
			4.3.2 Shipping
			4.3.3 Invoicing and Payments
			4.3.4 Authenticity and Transparency
			4.3.5 Dispute Resolution
	5 Conclusions
		5.1 Discussion on Results
		5.2 Further Research Proposals
	A.1 Appendix. List of Articles Selected in the SLR Process
	References
		Online Resources
Blockchain Technology for Information Sharing to Mitigate the Bullwhip Effect
	Abbreviations
	1 Introduction
		1.1 Background
		1.2 Problem Statement
		1.3 Research Questions
			1.3.1 Developments of Research Questions
	2 Literature Review
		2.1 Supply Chain Coordination
		2.2 The Bullwhip Effect
			2.2.1 The Negative Impact of BWE on SCP
			2.2.2 Causes of BWE
			2.2.3 Measuring of BWE
			2.2.4 Information Sharing a Remedy to Mitigate the BWE
			2.2.5 BWE in Service Supply Chain
		2.3 Information Sharing
		2.4 Types of Information Sharing
		2.5 How to Share Information?
		2.6 Benefits of Information Sharing
		2.7 Blockchain Technology
			2.7.1 Blockchain and Supply Chain Management
			2.7.2 Categories of Blockchain Technology
	3 Our Contributions
	4 Conclusion and Limitations
	References
Autonomous Vehicle Routing Optimization: A Survey
	1 Introduction
	2 Methodology
		2.1 Identification of Research Gap and Formulation of Research Questions
		2.2 Locating Studies and Selection
		2.3 Analysis of the Literature
	3 Results
		3.1 Optimization Criteria
		3.2 Optimization Models
		3.3 Optimization Methods
	4 Conclusions
	References
Assessing Path Planning Algorithms of Mobile Robots: A ROS-Based Simulation Framework
	Abbreviations
	1 Introduction
	2 Mobile Robots & ROS-Enabled Path Planning Algorithms
		2.1 Background
			2.1.1 Classical Algorithms
			2.1.2 Reactive Algorithms
			2.1.3 Hybrid Approaches
		2.2 Categorization
			2.2.1 Grid Approaches
			2.2.2 Simulation Approaches
			2.2.3 Real-World Approaches
		2.3 Critical Taxonomy
	3 Mobile Robots Path Planning: A ROS-Enabled Software
		3.1 Framework Architecture
			3.1.1 Phase #1: Layout Creation & Path Planning Algorithms Programming
			3.1.2 Phase #2: ROS Integration
		3.2 Simulation and Real-World
	4 Discussion and Conclusions
	References
Logistics System Network Design Towards Sustainability in the Era of Data Analytics
	1 Introduction
	2 Logistics Network Strategies and Decision Models
		2.1 Supply Chain Network Design
		2.2 Facility Location
		2.3 Vehicle Routing
		2.4 Location-Routing
	3 Solution Methods
		3.1 Construction Heuristics
		3.2 Local Search
		3.3 Metaheuristics
			3.3.1 Variable Neighborhood Search and Variable Neighborhood Descent
			3.3.2 Ant Colony Optimization
	4 Our Contributions
		4.1 Location-Routing
		4.2 Backward Time-Period Optimization
		4.3 Hierarchical Multi-Switch Multi-Echelon VRP
			4.3.1 Considering Service Times
	5 Conclusions and Future Research
	References
Strategic Evaluation of Environmental Trade-Off Solution in Logistics: A Multi Criteria Decision Making Approach
	1 Introduction
	2 Theoretical Framework
		2.1 Logistic Network
			2.1.1 Ocean Freight
			2.1.2 Road Transportation
		2.2 Assessment of Transportation Emissions
			2.2.1 Emission Metric Measure
		2.3 Solution Approaches
			2.3.1 The TOPSIS Method
			2.3.2 The VIKOR Method
			2.3.3 Comparison of TOPSIS and VIKOR
	3 Presentation of the Company Under Study
		3.1 Transportation Network
			3.1.1 Current Situation
	4 Data
		4.1 Assessment of Environmental Data
		4.2 Internal Emission Calculations
			4.2.1 Emission Factor Calculation
			4.2.2 Volume Calculation
			4.2.3 Distance Calculation
	5 Results and Analysis
		5.1 Cost
		5.2 Time
		5.3 Weights
		5.4 Results
		5.5 Evaluation of the Optimization
			5.5.1 Comparison to As-Is Analysis
			5.5.2 Stakeholders
			5.5.3 Strategic Decisions
			5.5.4 Evaluation of Emissions
	6 Conclusion and Recommendations
		6.1 Short-Term Recommendations
		6.2 Long-Term Recommendations
		6.3 Limitations
	References
A Simulated Annealing Heuristic Approach for the Energy Minimizing Electric Vehicle Routing Problem with Drones
	1 Introduction
	2 Literature Review
		2.1 Electric Vehicle Routing Problems
		2.2 Routing Problems with Drones
	3 The Electric Vehicle Routing Problem with Drones
		3.1 Mathematical Formulation of the EVRPD
	4 Simulated Annealing Heuristic
		4.1 Population Initialization
		4.2 Neighborhood Operators
		4.3 Temperature Reduction
	5 Computational Results
		5.1 Parameter Sensitivity
		5.2 Experimental Results
	6 Conclusions
	References
A Greedy and Variable Neighborhood Search Metaheuristic Approach for the Cumulative Unmanned Aerial Vehicle RoutingProblem
	1 Introduction
	2 Literature Review
	3 The Cumulative Unmanned Aerial Vehicle Routing Problem
		3.1 Humanitarian Coverage Path Planning Problem
		3.2 Converting CPP to VRP
		3.3 Mathematical Formulation of the CUAVRP
	4 Greedy and Variable Neighborhood Search Approach
		4.1 Initial Solution Construction
		4.2 Greedy Shaking
		4.3 Neighborhood Shaking
		4.4 Variable Neighborhood Descent
			4.4.1 2-Opt
			4.4.2 Inter-Route Exchange
			4.4.3 Inter-Route Relocation
	5 Computational Results
		5.1 Greedy Shaking Parameter Sensitivity
		5.2 CUAVRP Results
	6 Conclusions
	References
Spatiotemporal Analysis for the Impact of Traffic Incidents: Optimization Models Consistent with the Propagation of Shockwaves
	1 Introduction
	2 The Optimization Models
		2.1 Notation and Terminology
		2.2 The Optimization Model with Bi-Level Traffic Status
			2.2.1 The Constraints
			2.2.2 The Objective Function
		2.3 The Optimization Model with Multiple Congestion Levels
			2.3.1 The Constraints
			2.3.2 The Objective Function
	3 Numerical Experiments
		3.1 Results Based on Simulation Data
		3.2 Results Based on Real Data
	4 Conclusion
	References