Advances in Computational Logistics and Supply Chain Analytics (Unsupervised and Semi-Supervised Learning)

✍ Scribed by Ibraheem Alharbi (editor), Chiheb-Eddine Ben Ncir (editor), Bader Alyoubi (editor), Hajer Ben-Romdhane (editor)

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 205
• Edition: 2024
• Category: Library

✦ Synopsis

This book provides advances in computational logistics and supply chain analytics. The authors include innovative data-driven and learning-based approaches, methods, algorithms, techniques, and tools that have been designed or applied to create and implement a successful logistics and supply chain management process. This book highlights the state of the art and challenges related to the design and the application of computational methods to solve logistic and supply chain management problems. The authors present recent computational logistic methods and supply chain analytics techniques designed and applied to support managers in improving such complex processes. This book broadly covers recent computational methods and techniques applied to ensure continuous improvement of transport, logistic, and supply chain management processes. Readers can rapidly explore these new methods and their applications to solve such complex problems.

✦ Table of Contents

Preface
Acknowledgements
Contents
About the Editors
The Dynamic Vehicle Routing Problem: A Comprehensive Survey
1 Introduction
2 Survey Methodology
3 Presentation of the Vehicle Routing Problem
3.1 Problem Statement
3.2 Main Variants of the VRP
4 Dynamic Vehicle Routing Problem
4.1 Problem Description
4.2 The Degree of Dynamism
4.3 Main Factors Leading to the Emergence of Dynamic VRP Variants
5 Main Variants of the DVRP
6 Dynamic Multi-Objective Routing Problem
7 Solution Methods for the DVRP
7.1 Periodic Re-optimization
7.2 Continuous Re-optimization
7.3 Other Strategies
8 Performance Measures
9 Benchmarks for the DVRP
10 Real-World Applications of the DVRP
11 Trends and Future Directions of Dynamic Vehicle Routing Problem
12 Conclusion
References
Multi-Objective Optimization for Electric Vehicle Routing Problem: Literature Review
1 Introduction
2 Multi-Objective Optimization
3 Electric Vehicle Routing Problem
3.1 EVRP Variants
3.1.1 Electric Traveling Salesman Problem
3.1.2 EVRP with Time Windows
3.1.3 Two-Echelon EVRP
3.1.4 EVRP with Battery Swapping Stations
3.2 Energy Sources
3.3 Charging Strategies
3.4 Vehicles Characteristics
4 Multi-Objective Electric Vehicle Routing Problem
5 State-of-the-Art Methods for the MO-EVRP
5.1 Exact Methods
5.1.1 Branch and Price Method
5.1.2 Mixed Integer Programming
5.2 Meta-Heuristics
5.2.1 Adaptive Large Neighborhood Search
5.2.2 Local Search
5.2.3 Iterated Local Search
5.2.4 Genetic Algorithm
5.3 Hybrid Methods
5.4 Review Summary
6 Challenges and Future Research Directions
7 Conclusion
References
A Decision Support System for Solving the Windy Rural Postman Problem
1 Introduction
2 Related Work
3 Neighborhood Search-Based Approach
4 Decision Support System
4.1 Data Inputs
4.2 Optimization Tools
4.3 Numeric and Cartographic Display Solution
5 Illustrative Example
5.1 Neighborhood Search
5.2 Tabu Search Details
6 Real-World Application
7 Conclusion
References
A Hybrid Meta-Heuristic to Solve Flexible Job Shop SchedulingProblem
1 Introduction
2 The Solution Representation
3 The Proposed Hybrid Algorithm
3.1 Simulated Annealing
3.2 Genetic Algorithm
4 Experimental Results
5 Conclusion
References
Multimodal Freight Transport Optimization Based on Economic and Ecological Constraint
1 Introduction
2 State of the Art
3 MFT Problem Description
3.1 Dimensions
3.2 Decision Variables
3.3 Parameters
3.4 Objectives
3.5 Constraints
4 Proposed Solution Methods
4.1 Tabu Search-Based Solution
4.2 Genetic Algorithm-Based Solution
4.3 Chromosome Encoding
4.4 Selection Operator
4.5 Variation Operators
5 Experimentation and Results
5.1 Data Source Description
5.2 Parameters Exploration
5.2.1 Tabu Search Parameters Exploration
5.2.2 The Genetic Algorithm Parameters Exploration
5.3 Comparative Results
6 Conclusion
References
Solving Hierarchical Production–Distribution Problem Based on MDVRP Under Flexibility Depot Resources in Supply Chain Management
1 Introduction
2 Bi-level Optimization: Basic Concepts
3 Hierarchical Production–Distribution Problem Under Flexibility Depot Resources
3.1 PD-Related Works
3.2 Mathematical Formulation
4 A Co-evolutionary Decomposition-Based Algorithm for the Bi-MDVRPFD
4.1 Upper-Level Procedure
4.2 Lower-Level Procedure
5 Experimental Study
5.1 Benchmark Problems
5.2 Parameter Setting
5.3 Adopted Statistical Methodology
5.4 Comparative Results
6 Conclusion
Appendix A: CODBA (A CO-evolutionary Decomposition-Based Algorithm)
References
Analysis of Inhibitors to Implementing Digital Supply Chain in Saudi Arabia: An Interpretive Structural Modeling (ISM) Approach
1 Introduction
2 Literature Review
3 Inhibitors to Digital Supply Chain in Saudi Arabia
3.1 No Urgent Need
3.2 Unsuitable Organizational Structure
3.3 Lack of Digital Vision and Strategic Orientation
3.4 Rigidity of Business Processes
3.5 Biased Business Objectives
3.6 Difficult Adaptation with Digital Business Transformation
3.7 Risk Aversion in Taking Initiative
3.8 Lack of Industrial Accurate Guidelines
3.9 Great Implementation Cost
3.10 Lack of Top Management Support
3.11 Lack of Digital Competencies
3.12 Apprehension of Cyber Security Risk
3.13 Uncertain Return on Digital Investment
3.14 Fear of Transaction Risk
3.15 Legal Issues
4 Research Methodology
5 The Process of ISM Methodology
5.1 Stage 1: Listing Variables of the Understudied System
5.2 Stage 2: Description of the Relationship Between Variables
5.3 Stage 3: Identification of the Key Variables
6 Findings: MICMAC Analysis
7 Conclusions and Managerial Implications
References
Financial Performance Measurement of Logistics Companies: Empirical Evidence from Saudi Arabia
1 Introduction
2 Logistics and Supply Chain Management
2.1 What Is Logistics?
2.2 The Relationship Between Logistics and Supply Chain Management
3 The Role and Importance of Logistics
3.1 Role and Importance of Logistics in the Global Economy
3.2 The Logistics Industry in Saudi Arabia: An Overview
4 Literature Review
5 Methodology and Data
5.1 Entropy Method
5.2 Data
6 Results and Analysis
6.1 Entropy Results
6.2 Financial Performance Measurement Analysis Based on Entropy Results
7 Conclusion
References
Index