使用改進的混合粒子群演算法 (IH-PSO) 於流程作業批量問題之評估

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研究生：	John Michael Power John Michael Power
論文名稱：	使用改進的混合粒子群演算法 (IH-PSO) 於流程作業批量問題之評估 Evaluate Batch Processing Performance by Using Improved-Hybrid Particle Swarm Optimization (IH-PSO)
指導教授：	歐陽超 Chao Ou-Yang
口試委員:	羅士哲 Shih-Che Lo 郭人介 Ren-Jieh Kuo
學位類別：	碩士 Master
系所名稱：	管理學院 - 工業管理系 Department of Industrial Management
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	82
中文關鍵詞：	批量處理、批量大小、處理時間、等候時間、混成粒子群最佳化
外文關鍵詞：	Batch Processing, Batch Size, Processing Time, Waiting Time, IH-PSO
相關次數：	點閱：238 下載：6
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現今許多公司以過程導向法作為管理商業流程方式，意指將商業流程紀錄成程序模式，並通常藉由商業流程管理系統實現自動化。一直以來，管理商業流程內的資源被認為是重要的議題。然而，關於商業流程時如何善用資源的相關研究並不多。改進商業流程的其中一種方法為採用批量處理。

本研究提出一個架構用於衡量在流程中處理時間和等候時間的最佳批量大小。因此，公司能藉由在商業流程執行適合的批量處理以獲得益處。然而，由於沒有太多相關研究證明批量處理的有效性，故本研究的目標為對此方法做進一步探究。因此，本研究提出一格架構，搭配研究案例實現批量處理，可以得到最大限度的降低等候與處理的時間。

再者，本研究採用混成粒子群最佳化(IH-PSO)啟發式演算法能給予每個活動不同批量組合以達到最小化處理與等候時間。最初始狀態的處理時間為3050.75分鐘，而經由IH-PSO演算法讓處理時間降為3003.65分鐘。此方法被證明比傳統尋找最佳批量的方法更有效率。因此，此方法讓研究案例達到批量處理的合適條件。

Nowadays, many companies managed their business processes in a process-oriented fashion. It means that they are documented as process models and then often automatized with the help of business process management systems. Managing resources inside business processes has been recognized as an essential topic for business process execution since a long time ago. However, not so much works had been performed about how resources were applied while processing business processes. One way to improve their business processes is by doing batch processing.
The research proposes a framework to measure an optimal batch size in processing time and waiting time in a processing activity. Thus, companies could benefit if they make the right decision by implementing batch processing in a business process activity. However, due to the limitation of not so many researches have performed the usefulness of batch processing, it would be the primary goal of this research to understand it better. Therefore, this research's findings propose a framework for implementing batch processing in a case scenario that could minimize waiting and processing time.
Furthermore, implementing the meta-heuristics approach such as Improved-Hybrid Particle Swarm Optimization (IH-PSO) could give a batching size combination for each activity, resulting in the minimum processing and waiting time. The initial state gives a total processing time of 3050.75 minutes, where the IH-PSO gives a better result with 3003.65 minutes. This approach is proven to be more efficient than the traditional approach to finding the optimum batching size. Therefore, a suitable condition for implementing batch processing in a case scenario has been achieved.

Abstract    I
摘要    II
Acknowledgment    III
   LIST OF FIGURES    VI
   LIST OF TABLES    VII
   CHAPTER 1 INTRODUCTION    1
1     Background    1
2    Problem Description    5
3    Research Objectives    6
4    Research Limitations    6
5    Organization of Thesis    6
CHAPTER 2 LITERATURE REVIEW    8
1.    Batch Processing Issues    8
2.    Meta-Heuristics Approaches    10
2.1 Genetic Algorithms    10
2.2 Particle Swarm Optimization (PSO)    11
2.3 Simulated Annealing    12
2.4 Improved Hybrid Particle Swarm Optimization    13
3.    Batch Organization of Work Insights    15
4.    Colored Petri Nets Tool    17
CHAPTER 3 Methodology    20
1.    Event Log Data Collection    22
2.    Mining Process Model    23
3.    Batch Processing Initialization    23
4.    Finding Optimal Solution with IH-PSO    25
4.1 Brief Review of Defining Fitness Function    25
4.2 Fitness Function for IH-PSO Algorithm    28
4.3 Assumptions and Limitations    30
4.4 Design Process for Simulation Modelling    30
4.5 Re-design Process for Simulation Modelling    31
4.6 Analyze the Simulation Output    31
CHAPTER 4 EXPERIMENTS ANALYSIS    32
1    Empirical Experiment Case    32
2    Optimization Batch Size using Improved Hybrid Particle Swarm Optimization (IH-PSO) Algorithm    34
3    Simulation Results & Analysis    36
4    Sensitivity Analysis for Flexible Batch Processing    42
5    Application of Batch Processing in Real Case    48
CHAPTER 5 CONCLUSION & FUTURE RESEARCH    56
1    Conclusion    56
2    Research Contribution    58
3    Future Research    58
REFERENCES    60
APPENDIX    64
Appendix 1 Rebuild Simulation Model on CPN Tool Scenario 1    64
Appendix 2 Rebuild Simulation Model on CPN Tool Scenario 2    68
Appendix 3 Rebuild Simulation Model on CPN Tool Scenario 3    71

                                

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