Adaptive Control and Monitoring for Dynamic Inbound Supply Chain Management

다국어 초록 (Multilingual Abstract)

Supply chain management is one of the most important issues to improve enterprises’ profit, therefore, most research endeavors have focused on its structural and operational approaches in global viewpoint. Supply chains can be classified into inbound, intra-company, and outbound supply chain. In operation perspective, intra-company supply chain has been well addressed at the manufacturing system, and outbound supply chain has been also secured attentions such as vendor managed inventory, transshipment, and inventory routing problem. Inbound supply chain, however, has been unnoticed as much as other supply chain due to the structural similarity with outbound supply chain, instead, some operation approaches derived from the outbound case have been employed via transformation.

The inbound supply chain is often faced with uncertain contexts in a dynamic manufacturing environment. While some methodologies in the outbound supply chain works well on the inbound environment, it still has limitations due to differences of employed assumption. In most cases, various constraints in the inbound flows are not considered, which makes it harder to operate than the other supply chain because its failures directly affect the manufacturing site, furthermore entire supply networks. Therefore, it is necessary to develop simultaneous and adaptive control models for a dynamic inbound supply chain management in operational perspective.

The objective of this research is to develop a simultaneous and adaptive control approach for a dynamic inbound supply chain. The specific objectives of this research are as follows: 1) to develop the overall system architecture for a dynamic inbound supply chain management, 2) to propose the simultaneous control method for vehicle routing and inventory management, 3) to propose the adaptive vehicle scheduling approach for a dynamic inbound logistics, and 4) to demonstrate operational perspectives of the proposed working mechanisms and validate.

목차 (Table of Contents)

• CHAPTER I. INTRODUCTION １
• I.1 Rational for the Research １
• I.2 Research Objectives and Scope ３
• I.3 Organization of Proposal ５
• CHAPTER II. LITERATURE REVIEW ６
• II.1 Supply Chain Management ６
• II.1.1 Characteristics of the inbound supply chain ７
• II.1.2 Risk models for the inbound supply chain ９
• II.1.3 Adaptive and flexible supply chain management １０
• II.2 Simultaneous Control １１
• II.2.1 Supply chain scheduling and planning １１
• II.2.2 Inventory routing problem １２
• II.2.3 Simulation-based scheduling １４
• CHAPTER III. CONCEPTUAL FRAMEWORK FOR ADAPTIVE VEHICLE SCHEDULING １７
• III.1 Conceptual Framework １７
• III.1.1 Constraints in inbound SCM operation １８
• III.1.2 Rule-based planning system １９
• III.1.3 Real-time vehicle scheduling system ２２
• III.2 Operation Procedure ２４
• III.3 Numerical Example ２６
• III.3.1 Simulation set-up ２６
• III.3.2 Simulation result ２７
• III.3.3 Discussion ３１
• III.4 Chapter Summary ３２
• CHAPTER IV. SIMULTANEOUS CONTROL OF VEHICLE ROUTING AND INVENTORY ３４
• IV.1 Introduction ３４
• IV.2 Related Works ３７
• IV.2.1 Inbound supply chain in a dynamic environment ３７
• IV.2.2 Simultaneous control ３８
• IV.3 Vendor-based Vehicle Scheduling Method ４１
• IV.3.1 Vendor-based vehicle scheduling procedure ４１
• IV.3.2 Goal programming model ４３
• IV.4 Simulation ４４
• IV.4.1 Simulation condition ４５
• IV.4.2 Simulation result ４６
• IV.5 Discussion ５０
• IV.6 Chapter Summary ５４
• CHAPTER V. ADAPTIVE VEHICLE SCHEDULING APPROACH ５７
• V.1 Introduction ５７
• V.2 Related Works ５９
• V.2.1 Adaptive supply chain management ５９
• V.2.2 Inventory routing problem ６０
• V.3 Period-based Vehicle Scheduling Approach ６２
• V.3.1 Preprocessing ６３
• V.3.2 Vehicle route design ６４
• V.3.3 Vehicle scheduling ７０
• V.4 Simulation ７３
• V.4.1 Simulation result ７３
• V.4.2 Discussion ８１
• V.5 Chapter Summary ８５
• CHAPTER VI. FUZZY INFERENCE-BASED MONITORING MODEL ８８
• VI.1 Introduction ８８
• VI.2 Fuzzy Set Theory ９０
• VI.3 Framework for Monitoring System ９２
• VI.3.1 Performance evaluation module ９３
• VI.3.2 Decision making module ９３
• VI.4 Fuzzy Inference System for Suitability Evaluation ９３
• VI.4.1 Suitability ９４
• VI.4.2 Fuzzy sets and membership functions ９５
• VI.4.3 Fuzzy rule design ９９
• VI.4.4 Defuzzification １００
• VI.4.5 Procedure of suitability evaluation １０１
• VI.5 Example of Suitability Evaluation １０４
• VI.6 Chapter Summary １０７
• CHAPTER VII. CONCLUDING REMARKS １０９
• VII.1 Summary of Dissertation １０９
• VII.2 Contributions and Future Works １１２
• SUMMARY IN KOREAN １１４
• REFERENCES １１６
• ACKNOWLEDGEMENTS １２５
• CURRICULUM VITAE １２７