研究生: |
陳建宏 Chien-Hung, Chen |
---|---|
論文名稱: |
利用遺傳演算法與彩色時間性裴氏圖在一般性生產排程系統中建構具優化機制之排程產生器 Using GA and CTPN for Modeling the Optimization-based Schedule Generator of a Generic Production Scheduling System |
指導教授: |
簡禎富
Chen-Fu, Chien |
口試委員: | |
學位類別: |
碩士 Master |
系所名稱: |
工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management |
論文出版年: | 2003 |
畢業學年度: | 91 |
語文別: | 英文 |
論文頁數: | 101 |
中文關鍵詞: | 非等效平行機台排程 、彩色時間性裴氏圖 、三階段離散事件模擬 、遺傳演算法 、混合整數規劃 、派工法則 |
外文關鍵詞: | unrelated parallel machine scheduling, colored timed Petri nets, three-phase discrete event simulation, genetic algorithm, MILP, dispatching rule |
相關次數: | 點閱:4 下載:0 |
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本研究從理論方法與實作角度切入,考慮實務上高科技製造產業製造管理中生產排程問題的複雜性,在一般性生產排程(GPS)系統下,提出一針對一般性非等效平行機台排程問題(GUPMSP)之管理與求解架構。所謂GUPMSP,係指具有:非等效平行機台環境、動態工件到達、工件加工不可中斷性、不可分割順序相依前置時間、多重資源需求、一般性加工順序限制,以及迴流製程等特性之排程問題。首先,本研究提出一個具優化機制之排程產生器(OptSG),以求解GUPMSP的最佳解或近似最佳解。OptSG由彩色時間性裴氏圖(CTPN)、三階段模擬邏輯(Three-phase simulation logics),以及遺傳演算法(GA)所構成。透過模式結構與模式組態的分離架構,使得OptSG具有結構獨立性,使其無論在問題求解或問題分析上均具有強健性與便利性。此外,本研究另提出一個混合整數線性規劃(MILP)模式,以求解GUPMSP的最佳解。MILP模式的角色,主要在提供GUPMSP的最佳解作為標竿(benchmark),以評估OptSG的求解效度。MILP模式中,其同時考慮不可分割順序相依前置時間與多重資源需求兩項排程特性,為文獻中所未有。本研究最後進行實驗,以實際數據比較MILP、OptSG,以及單一派工法則(DRBH)的求解品質與求解時間。實驗結果驗證本研究提出之模式在理論上之求解最佳性與實務上之實作可行性。
In this study, considering the complex problem nature in practical high-tech manufacturing environment from the viewpoints of both theoretical approaches and on-line implementation, a managerial framework in a generic production scheduling (GPS) system from which the generalized unrelated parallel machine scheduling problem (GUPMSP) arises was proposed. GUPMSP is characterized by the following characteristics: unrelated parallel machine environment, dynamic job arrival, non-preemption, inseparable sequence-dependent setup time, multiple resources requirement, general precedence constraint, and job re-circulation. We proposed the optimization-based schedule generator (OptSG) for the approximation of GUPMSP. Separation of model structure and model configuration in OptSG contributes to the structural independence, which makes OptSG robust and convenient in analysis and problem solving of GUPMSP in real settings with changing properties. Meanwhile, we proposed a mixed-integer-linear-programming (MILP) model for the optimization of GUPMSP. This MILP model was developed as a benchmark to estimate the validity of OptSG. Inseparable sequence-dependent setup time and multiple resources requirement that have not been addressed simultaneously in the literature were considered in the MILP model. Finally, we conducted several experiments to compare the solutions of MILP model, OptSG, and dispatching rule-based heuristics (DRBH). The results validated the practical viability of this study.
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