包裝業是快速發展的勞力密集型產業，在全球一體化的趨勢下，產業內企業間競爭日益劇烈。本研究利用Visual Basic for Application (VBA)建構一產能規劃系統於包裝業工廠，不僅能對訂單自動排程以滿足訂單交期，而且能平衡同一製程內各機台的負荷。
此外，利用多目標基因演算法優化包裝業產能規劃系統中訂單排序，同時考量兩目標：(1) 最小化訂單延誤、(2) 最小化機台負荷平衡差異。本研究採用實驗設計方法評估不同參數組合之績效，並利用變異數分析評估成果。本研究可提供包裝業訂單排程之參考，有效降低訂單延誤，平衡機台負荷。

Packaging industry is a rapid developing labor-intensive industry. Facing the global trend of integration, intra-industry enterprises compete with each other fiercely. This study develops a capacity planning system (CPS) for packaging plant by using Visual Basic for Application (VBA). It can not only automatically generate order scheduling to meet the delivery of orders, but also balance the workload between machines in the same process.
Besides, this study proposes a modified multi-objective genetic algorithm (MOGA) to solve multi-objective order scheduling problem. Two criteria are simultaneously considered for optimization: to minimize the lateness, and to minimize machine workload balance deviation. Design of experiments (DOE) is adopted to validate the performance and analysis of variance (ANOVA) to evaluate the output. This study can provide some reference to packaging plants to make better order scheduling decisions, efficiently reducing lateness and balancing machine workload.

Chapter 1: Introduction 1
1.1 Background and Motivation 1
1.2 Research Objectives 3
1.3 Research Method 4
1.4 Organization of Thesis 5
Chapter 2: Literature Review 6
2.1 Production Scheduling 6
2.2 Capacity Planning System 8
2.3 Multi-Objective Genetic Algorithm 10
Chapter 3: Methodology 13
3.1 Problem Statement 13
3.2 Capacity Planning System 17
3.2.1 Order Treatment Module (OTM) 20
3.2.2 Order Priority Module (OPM) 20
3.2.3 Lot Release Module (LRM) 21
3.2.4 Workload Accumulation Module (WAM) 22
3.2.5 Workload Balance Module (WBM) 22
3.3 Multi-objective Genetic Algorithm Approach 24
3.3.1 Chromosome Encoding 25
3.3.2 Population Initialization 25
3.3.3 Fitness Function Evaluation 26
3.3.4 Selection 28
3.3.5 Crossover 29
3.3.6 Mutation 30
3.3.7 Next Population Generation 31
3.3.8 Termination 32
3.3.9 Example of Multi-Objective Genetic Algorithm Approach 32
Chapter 4: Experiments and Results 36
4.1 Simulation Environment 36
4.2 MOGA Parameters Setting 39
4.3 CPS Parameters Setting 44
Chapter 5: Conclusion 55
Reference 57

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