A Multi-Objective Hybrid Genetic Algorithm Approach To The Intergraded Problem Of Assembly Planning And Line Balancing
陳明華、曾懷恩 ; 郭文宏
E-mail: [email protected]
ABSTRACT
To face the variety of the market change, a flexible assembly system which can fabricate the products in small batches and meet specific orders is necessary to for quick response the customer’s need. Thus, the integration for assembly sequence planning and assembly line system actually play an important role in the assembly design of product. An assembly sequence planning is considered to be optimal when the sequence satisfies specific assembly constraint such as the assembly direction and tool change. On the other side, the problem of assembly line balancing involves the minimization of the idle time in the assembly line design. In this research, Equal Piles method based on the imbalance concept is suggested in ALB. This study proposes a multi-objective hybrid genetic algorithm (MOHGA) approach to integrate the viewpoint of ASP and ALB. The MOHGA combines with evolutionary
multi-objective optimization algorithms, grouping genetic algorithms and priority-based genetic algorithms. MOHGA is aim to find Pareto-optimal solution set (all non-dominated solutions) based on the multi-objective optimal search method. In addition, a decision model which can find only one solution in terms of decision maker’s preference is proposed. Finally, several examples were offered to verify the feasibility of MOHGA. Experiment results indicate that the MOHGA can efficiently produce Pareto-optimal solution set and support the design of flexible assembly system.
Keywords : Assembly Planning ; Assembly Line Balancing ; Evolutionary Multi-Objective Optimization ; Grouping Genetic Algorithms ; Hybrid Genetic Algorithms ; Multi-Objective Optimization
Table of Contents
第一章 緒論 1 第二章 研究方法 7 2.1 ASP與ALB規劃分析 7 2.1.2 ASP規劃分析 9 2.1.3 ALB規劃分析 11 2.1.4 小結 15 2.2 多 目標混合基因演算法的架構 17 2.2.1 目標函數的設定 17 2.2.2 多目標最佳化 19 2.2.3 基因演算法設計 22 2.3 決策分析 27 第 三章 多目標混合基因演算法 28 3.1 編碼表示法 28 3.2 解碼程式 29 3.3 初始族群的產生 30 3.4 更新柏拉圖最佳解 34 3.5 選擇 運運算元 34 3.6 交配運運算元設計 35 3.6.1 第一層基因之交配方式 35 3.6.2 第二層基因之交配方式 37 3.7 突變運運算元設 計 39 3.8 精華保留策略 39 3.9 基因局部搜尋演算法 40 3.9.2 競爭式法則 42 3.9.3 鄰近解搜尋方式 42 第四章 範例測試與分析 43 4.1 測試問題 43 4.2 參數設定 44 4.3 柏拉圖最佳解績效衡量 46 4.4 測試結果 47 4.5 結果分析 50 4.6 柏拉圖最佳解決策分 析 58 4.7 輔助決策之使用者介面 60 第五章 結論與未來研究方向 63 5.1 結論 63 5.2 未來研究方向 64 參考文獻 65 附錄 71 REFERENCES
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