This study aims to optimize the information logistics (IL) design of a manufacturing execution system that controls the flow of goods in manufacturing factories. Corporate management is faced with the severe challenge of fierce price competition and sharp profit declines due to rising energy costs and supply-demand imbalances, especially in heavy industry and traditional manufacturing industries. Important decisions in the industrial world rely on expert experience and existing knowledge, which can lead to the waste of logistics costs in the absence of systematic thinking and technical application. Therefore, in this study, the AI Genetic Algorithm was applied to the informatization of the logistics mode and the manufacturing execution system (MES) of a manufacturing factory (Company A) in order to improve the accuracy of the factory logistics and accurately dispatch the existing production resources, thus reducing excess costs and optimizing the logistics supply end. In addition, the AI Genetic Algorithm was applied in by-product transporting and logistics optimization research in a steel factory (Company C) with the expectation of solving the bottlenecks of traditional decision-making and to optimize transporting logistics decisions based on AI. In the problem formulation, considering the path information, the vehicle path systemization, and the transporting demand frequency in the factory, a model for by-product transporting and logistics in the steel factory was established. The improved variable-length chromosome termination technique and the Dual-Layer Genetic Algorithm were proposed to effectively solve the problem of transporting in different zones. The experimental results showed that the zoning result obtained by this method had a slightly shorter total transporting time than the existing expert-based task scheduling but had far better fairness. In addition, the decision generation speed of this method was tens of minutes, which represented a marked improvement compared to the decision generation speed in the expert-based task scheduling, which requires days.

1. Introduction 1
1.1 Research Background and Reason 1
1.2 Research Motives and Purposes 7
1.3 Research Scope 11
1.4 Contribution and Organization of this thesis 17
2. Literature Review 19
2.1 Significance of Logistics Management and Literature Review 19
2.1.1 Material management in the manufacturing industry 19
2.1.2 Significance of Logistics management in the manufacturing industry 20
2.1.3 Logistics informatization of the manufacturing industry 21
2.1.4 Manufacturing Execution System (MES) 25
2.2. Operation Mode of Logistics Zoning Planning 27
2.3 Flexible Job Shop Scheduling Problem 28
2.4 Establishment of Cloud Server Database 28
2.5 Intelligent Mobile Devices 29
2.6 Establishment of QR Code 30
2.7 QR Code Generator 32
3. Research Method 36
3.1 Problem Formulation and Restrictions 36
3.1.1 Problem Formulation 36
3.1.2 Operation Reporting System 38
3.1.3 Process Production Card System 41
3.2 Import of MES 42
3.3 Proposed Double-Layer Genetic Algorithms (DLGA) 43
3.4 Make recommendations DLGA 44
3.5 DLGA based on Checkerboard Chromosome with foamed gene 45
3.6 Optimal Design Process of DLGA 49
3.7 Optimal Analysis and Result Discussion 52
3.8 Compare the Difference between Zoning Planning and DLGA 55
3.9 Results of six Districts in case study 60
3.10 DLGA Planning Pareto Solution 64
4. Case Discussion and Analysis 66
4.1. Optimization of Material Transporting Method in the Manufacturing Factory
4.1.1 Analysis of Manual Operation Situation 66
4.1.2 Determination of Material coding and Movement 67
4.1.3 Material Transporting Method 69
4.1.4 Filing of Production Schedule 76
4.1.5 Analysis of Traditional Manual Operation Situation 76
4.1.6 Code Format and Coding Design of the Material 77
4.1.7 AI-based QR code Description and Data reading 78
4.1.8 Optimization Analysis and Results of Material Transporting Method (WIP) of the Case Company 79
4.2 Optimal Design for Transport 84
4.3 Comparison between Manual Partition and DLGA Solution. 84
4.4 Analysis of Results from one to six Zones of Case Companies 93
5. Conclusion and Future Work 104
5.1 Conclusion and Suggestions 104
5.2 Future Work 105
6 References 107

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