基於需求不確定性之下產能規劃模式的粒子群優化算法和差分進化算法相混合的新型全局優化算法

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研究生：	薛進 Jin - Xue
論文名稱：	基於需求不確定性之下產能規劃模式的粒子群優化算法和差分進化算法相混合的新型全局優化算法 Hybridizing differential evolution with particle swarm optimization for a constrained capacity planning problem under demand uncertainty
指導教授：	王孔政 Kung-Jeng Wang
口試委員:	曹譽鐘 Yu-Chung Tsao 游兆鵬 Jonas Chao-Pen Yu
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：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	67
中文關鍵詞：	產能規劃、混合算法、差分進化算法、粒子群優化算法
外文關鍵詞：	DE, DE-PSO, PSO.
相關次數：	點閱：231 下載：5
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在向工業4.0時代跨越的今天，高靈活度滿足市場需求和精準高效的與產能規劃力，是每一個生產業者所必不可少的兩項技能。具有高準確率的算法可以幫助產業決策者更好的分析由信息爆炸所產生的海量數據，從而規劃產能，應對變幻莫測的市場需求。本文提出一種基於粒子群優化算法（PSO）和差分進化算法（DE）相結合的新型混合全局優化算法DE－PSO。通過生產案例對PSO，DE，GA，RS算法進行比較，證明DE－PSO算法是一種收斂效果好，求解精度高的全局優化算法。

Decisions regarding capacity planning and resource allocation under demand uncertainty are vital in high-tech industries because the resources investment is capital intensive. The decision makers need to consider the trade-off among the cost of investment, profit and fill rate. The need of developing a high quality solution approach is motivated because a slightly improvement of the solution quality might lead to a huge saving. Therefore, the purpose of this study is to propose a novel hybrid algorithm named PSO-DE, which integrates particle swarm optimization (PSO) algorithm with differential evolution (DE) algorithm to solve a constrained capacity planning and resource allocation problem. This hybridizing approach aims to combine two algorithms in a judicious manner such that the resulting algorithm contains the positive features of both the algorithms. For example, speeds up the convergence and improves the algorithm’s performance. Experimental results show that our solution approach outperforms DE, PSO, and genetic algorithm (GA) alone.

致謝	i
Abstract	iv
摘要	v
List of Table	viii
List of Figure	ix
Chapter 1 Introduction	10
1.1   Research background	11
1.2   Research motivations	11
1.3   Research objectives	12
1.4   Research framework	13
1.5 Organization of the dissertation	13
Chapter 2 Literature Review	14
2.1 Capacity planning under demand uncertainty	14
2.2 Differential evolution, particle swarm optimization, and their comparative studies	15
2.3 Hybridization of DE and PSO	16
2.4 Summary	18
Chapter 3 Model development	20
3.1 The capacity planning and resource allocation model under demand uncertainty	20
3.2 Algorithm development	22
3.3 Solution vector and repair mechanism	25
Chapter 4 Experiments	27
4.1 Sensitivity analysis for DE-PSO algorithm parameters	27
4.2 Fitness for control factors and noise factors design experiment	30
4.3 Profits of different demand	33
4.4 Performance evaluation	36
4.5 Summary	43
Chapter 5 Conclusions	44
Appendix 1	46
Appendlx.2	54
Appendlx.3	61
Reference	62





















List of Table
Figure 1 Research framework.	13
Figure 2 Hendtlass’s algorithm (Hendtlass, 2011).	17
Figure 3 Ali’s algorithm (Ali et al., 2009).	18
Figure 4 A procedure of selecting DE or PSO randomly algorithm.	19
Figure 5 DE-PSO algorithm.	25
Figure 6 DE-PSO algorithm.	26
Figure 7 Factor effect	33
Figure 8.	34
Figure 9 Convergence of the fitness value of DE-PSO against stochastic demand	36
Figure 10 Comparing of the results of 5 algorithms for problem size 1	39
Figure 11 Interval plot of fitness	42











List of Figure

Table 1 Factors for DE-PSO algorithm.	28
Table 2 ρ-value of significant factors.	29
Table 3 Multi-level control factors.	30
Table 4 Fitness value for control factors and noise factors Design Experiment	31
Table 5 Response for SNRA and Mean	33
Table 6 Control factors values of different algorithms	37
Table 7 Comparing of the results of PSO-DE with respect to 4 other algorithms	38
Table 8 Kruskal-Wallis Test on fitness	40

                                

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