Optimizing Supply Chain Resilience Using Machine Learning Algorithms in Industrial Engineering

1. Introduction 1.1 Background and Motivation 1.2 Problem Statement 1.3 Objectives of the Study 1.4 Structure of the Thesis 2. Literature Review 2.1 Supply Chain Resilience 2.2 Machine Learning in Industrial Engineering 2.3 Integration of ML for Supply Chain 2.4 Current Challenges and Gaps 3. Theoretical Framework 3.1 Concepts of Supply Chain Resilience 3.2 Overview of Machine Learning Algorithms 3.3 Risk Management in Industrial Engineering 4. Research Methodology 4.1 Research Design and Approach 4.2 Data Collection Methods 4.3 Data Analysis Techniques 4.4 Ethical Considerations 5. Machine Learning Algorithm Selection 5.1 Criteria for Algorithm Selection 5.2 Comparison of Algorithms 5.3 Justification of Selected Algorithms 6. Implementation in Supply Chain 6.1 Simulation of Machine Learning Models 6.2 Integration into Supply Chain Systems 6.3 Evaluation of Implementation Success 7. Case Studies and Results 7.1 Case Study Methodology 7.2 Analysis of Results 7.3 Discussion of Findings 8. Conclusion and Recommendations 8.1 Summary of Key Findings 8.2 Implications for Industrial Engineering 8.3 Future Research Directions 8.4 Practical Recommendations