本研究以半導體封裝廠為例，探討混合流線型生產環境的批量流與派工法則之排程問題。在已經訂單資訊情況下，考慮各站中不同機群之非等效平行機台與相同機群內完全相同平行機台；各訂單之產品依照規格不同有其適用之機台群限制；各機台加工時間具有隨機現象；加工過程中有其載具轉換所造成之特殊生產特性：黏晶粒站加工之拆批行為決策、模壓站前之集批特性，以最小化訂單之平均流程時間(F ̅)為目標，求得最合適之規劃決策。
本研究面臨之規劃問題主要為三大部分：一為工作站之派工法則選定、二為訂單拆批決策、三為訂單機群指派。由於產業特殊之生產環境問題，且加工時間具有隨機性情況下，其指派問題相當困難且複雜，因此本研究運用模擬最佳化手法進行問題求解，利用基因演算法進行解空間之搜尋與改良法最佳運算資源分配(EGOCBA, Elite Group Optimal Computing Budget Allocation)有效地分配模擬資源以節省模擬時間，求得在同時考量派工法則與批量流之生產情況更能有效降低平流程時間且將方案結果提供給半導體封裝業參考。

This study is a case for Semiconductor Assembly Factory and consider hybrid flow shop problem for lot streaming and dispatching rule. Consider orders with a known arrival time, unrelated parallel machines for machine group, identical parallel machines within machine group, machine eligibility about different product, the processing time with randomness, specific production about lot streaming and batch processor to a scheduling problem. The objective function is to minimize mean flow time of the orders in the study.
The problem is to determine the dispatching rule of each stage, the lot size of each job after the first stage, and the assignment machine group of each job to process in each stage. Because of specific production environment, the assignment problem is completive. To address this problem, we develop the simulation optimization approach. To overcome too many alternatives to exhaust, genetic algorithm is used when the search space is large. Elite Group Optimal computing budget allocation(EGOCBA) is used to reduce simulation budget and time while processing time of machines has randomness. The conclusion of this study presents the superior mean flow time in the problem of dispatching rule with lot streaming.

第一章 緒論
1.1 研究背景及動機
1.2 研究目的
1.3 研究範圍
1.4 研究步驟
第二章 文獻回顧
2.1 混合流線型生產排程問題
2.2 拆批行為
2.3 派工法則
2.4 派工法則與其他生產特性結合之應用
2.5 模擬最佳化
2.5.1. 基因演算法（Genetic Algorithm, GA
2.5.1. 資源分配最佳化（Optimal Computing Budget Allocation)
第三章 考慮批量流之生產排程問題
3.1 問題定義
3.2 問題模型
3.3 數學解與趨近解之比較
3.3.1 數學模型之數學解
3.3.2 基因演算法之趨近解
3.3.3 數學解與趨近解之比較
第四章 考量派工法則之批量流生產排程問題
4.1 問題定義
4.2 問題複雜度
4.3 研究方法論
4.4 模擬模式建構與驗證
4.4.1 模擬模式建構
4.4.2 模擬模式確認與驗證
4.5 實驗設計與分析
4.5.1. 兩站別小型生產系統之實驗
4.5.2. 考量派工法則下之派工法則實驗探討
4.5.3. 考量派工法則下之派工法則與批量的相關性實驗
第五章 隨機環境下之批量流生產排程問題
5.1 問題定義
5.2 模擬最佳化方法論
5.3 模擬模式建構
5.3.1. 模擬模式建構
5.3.2. 模擬模式確認與驗證
5.4 實驗設計與分析
5.4.1 改良型OCBA與OCBA之比較
5.4.2 改良型OCBA參數校準
第六章 結論與建議
6.1 結論
6.2 建議
參考文獻
附件

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