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**Introduction**

Quantum-inspired optimization has gained much attention in recent years as a promising approach to solve complex optimization problems efficiently. Supply chain logistics is a critical area where optimization plays a vital role in ensuring the smooth flow of goods and services. Traditional optimization techniques often struggle to handle the complexity and uncertainty inherent in supply chain logistics. Quantum-inspired optimization techniques offer a new perspective on tackling these challenges by harnessing principles of quantum physics to find optimal solutions.

This thesis explores the application of quantum-inspired optimization techniques in supply chain logistics to improve decision-making processes and enhance operational efficiency. The study aims to investigate how quantum-inspired algorithms can be applied to address various optimization problems in supply chain logistics, such as inventory management, transportation routing, and facility location.

**Table of Contents**

**Chapter 1: Introduction**
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

**Chapter 2: Literature Review**
2.1 Overview of Supply Chain Logistics
2.2 Traditional Optimization Techniques in Supply Chain Logistics
2.3 Quantum Computing and Optimization
2.4 Quantum-inspired Optimization Algorithms
2.5 Applications of Quantum-inspired Optimization in Supply Chain Logistics
2.6 Challenges and Limitations of Quantum-inspired Optimization
2.7 Comparative Analysis of Quantum-inspired and Traditional Optimization
2.8 Future Trends in Quantum-inspired Optimization for Supply Chain Logistics
2.9 Summary of Literature Review

**Chapter 3: Research Methodology**
3.1 Research Design
3.2 Data Collection
3.3 Experimental Setup
3.4 Quantum-inspired Optimization Models
3.5 Case Studies
3.6 Performance Metrics
3.7 Data Analysis Techniques
3.8 Validation Methods
3.9 Ethical Considerations

**Chapter 4: Discussion of Findings**
4.1 Inventory Management Optimization
4.2 Transportation Routing Optimization
4.3 Facility Location Optimization
4.4 Comparative Analysis of Quantum-inspired Algorithms
4.5 Impact of Quantum-inspired Optimization on Supply Chain Logistics
4.6 Managerial Implications
4.7 Practical Recommendations
4.8 Future Research Directions

**Chapter 5: Conclusion**
5.1 Summary of Findings
5.2 Contributions to Knowledge
5.3 Implications for Practice
5.4 Limitations of the Study
5.5 Recommendations for Future Research
5.6 Conclusion

**Thesis Overview**

Quantum-inspired optimization techniques have emerged as a promising approach to solving complex optimization problems efficiently. In the context of supply chain logistics, where decision-making processes are critical, these techniques offer a new perspective on addressing challenges such as inventory management, transportation routing, and facility location. This thesis aims to explore the application of quantum-inspired optimization in supply chain logistics, with a focus on improving operational efficiency and decision-making processes.

The literature review will provide an in-depth analysis of traditional optimization techniques in supply chain logistics, the principles of quantum computing and optimization, and the applications of quantum-inspired algorithms in the field. The research methodology will outline the experimental setup, data collection methods, quantum-inspired optimization models, and case studies to be conducted. The discussion of findings will present the results of the research, including insights on inventory management, transportation routing, and facility location optimization.

In conclusion, this thesis seeks to contribute to the body of knowledge on the application of quantum-inspired optimization in supply chain logistics. By exploring the benefits, challenges, and future trends of these techniques, the study aims to provide practical recommendations for improving decision-making processes and operational efficiency in supply chain logistics.

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