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雙機流線型批次排程問題 = A STUDY ON A TWO-MACHI...

蔡仁皓

雙機流線型批次排程問題 = A STUDY ON A TWO-MACHINES FLOWSHOP BATCH SCHEDULING PROBLEM WITH NON-IDENTICAL JOB SIZES

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	A STUDY ON A TWO-MACHINES FLOWSHOP BATCH SCHEDULING PROBLEM WITH NON-IDENTICAL JOB SIZES
作者:	蔡仁皓,
其他作者:	楊達立,
其他團體作者:	國立虎尾科技大學
出版地:	雲林縣
出版者:	國立虎尾科技大學;
出版年:	民96[2007]
版本:	初版
面頁冊數:	82面圖,表 : 30公分;
標題:	基因演算法
標題:	batch processing machine
電子資源:	http://140.130.12.251/ETD-db/ETD-search-c/view_etd?URN=etd-0628107-155602
摘要註:	半導體業在國內發展的相當迅速，而在預燒製程(burn-in)的過程中，常常會以批次處理機台(batch processing machine)來進行。本研究主要在探討雙機流線型(flow shop)批次排程問題並建構兩個混合整數規劃模式，並使用基因演算法(genetic algorithms，GA)及模擬退火法(simulated annealing，SA)來求解批次排程問題。其中工作的處理時間與大小(size)皆為已知，且工作有不相同的大小(size)，並以數個工作形成批次的方式在處理機台上加工，每個批次的大小不能超過機台本身所能處理的最大容量。批次的處理時間是以在同一批次之中所含工作之最長的處理時間定義為此批次的處理時間。本研究所建構的兩個混合整數規劃模式，分別是當兩機之間的暫存區容量(buffer capacity)為無限制與零的模式。目標是在如何將工作分配至批次內與安排批次到機台的工作順序，使得完工時間達到最小化(Minimum Makespan)目標。最後，將基因演算法、模擬退火法與電腦套裝軟體LINGO8.0版三者加以比較，由結果顯示，在小規模問題時，三者的求解能力差不多，但當問題規模變大時，基因演算法不管在求解品質或者計算時間上，皆優於模擬退火法與LINGO許多。 The domestic semiconductor industry expands rapidly, batch processing machines are commonly used in burn-in operations. This paper proposes two mixed integer formulation models to schedule batches of jobs on two machines in a flow shop. Then, GA and SA are applied to the batch scheduling problem. A set of jobs with known processing times and sizes has to be grouped, to form batches, in order to be processed on the batch processing machines. Each machine can process a batch as long as the total size of a batch is less than the machine capacity. The processing time of a batch is the longest processing time of all the jobs in that batch. Mixed integer formulation models are proposed when the buffer capacity is unlimited or zero. This research aims at minimizing the makespan for a batch processing machine. Random instances were used to test the effectiveness of the proposed approach. The results obtained from GA were compared with a SA approach and Lingo. The results indicate that the GA was able to arrive at better makespan with shorter run times.

雙機流線型批次排程問題 = A STUDY ON A TWO-MACHINES FLOWSHOP BATCH SCHEDULING PROBLEM WITH NON-IDENTICAL JOB SIZES
蔡, 仁皓

雙機流線型批次排程問題 = A STUDY ON A TWO-MACHINES FLOWSHOP BATCH SCHEDULING PROBLEM WITH NON-IDENTICAL JOB SIZES / 蔡仁皓撰 - 初版. - 雲林縣 : 國立虎尾科技大學, 民96[2007]. - 82面 ; 圖,表 ; 30公分.
基因演算法batch processing machine

楊, 達立

雙機流線型批次排程問題 = A STUDY ON A TWO-MACHINES FLOWSHOP BATCH SCHEDULING PROBLEM WITH NON-IDENTICAL JOB SIZES
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330 $a 半導體業在國內發展的相當迅速，而在預燒製程(burn-in)的過程中，常常會以批次處理機台(batch processing machine)來進行。本研究主要在探討雙機流線型(flow shop)批次排程問題並建構兩個混合整數規劃模式，並使用基因演算法(genetic algorithms，GA)及模擬退火法(simulated annealing，SA)來求解批次排程問題。其中工作的處理時間與大小(size)皆為已知，且工作有不相同的大小(size)，並以數個工作形成批次的方式在處理機台上加工，每個批次的大小不能超過機台本身所能處理的最大容量。批次的處理時間是以在同一批次之中所含工作之最長的處理時間定義為此批次的處理時間。本研究所建構的兩個混合整數規劃模式，分別是當兩機之間的暫存區容量(buffer capacity)為無限制與零的模式。目標是在如何將工作分配至批次內與安排批次到機台的工作順序，使得完工時間達到最小化(Minimum Makespan)目標。最後，將基因演算法、模擬退火法與電腦套裝軟體LINGO8.0版三者加以比較，由結果顯示，在小規模問題時，三者的求解能力差不多，但當問題規模變大時，基因演算法不管在求解品質或者計算時間上，皆優於模擬退火法與LINGO許多。 The domestic semiconductor industry expands rapidly, batch processing machines are commonly used in burn-in operations. This paper proposes two mixed integer formulation models to schedule batches of jobs on two machines in a flow shop. Then, GA and SA are applied to the batch scheduling problem. A set of jobs with known processing times and sizes has to be grouped, to form batches, in order to be processed on the batch processing machines. Each machine can process a batch as long as the total size of a batch is less than the machine capacity. The processing time of a batch is the longest processing time of all the jobs in that batch. Mixed integer formulation models are proposed when the buffer capacity is unlimited or zero. This research aims at minimizing the makespan for a batch processing machine. Random instances were used to test the effectiveness of the proposed approach. The results obtained from GA were compared with a SA approach and Lingo. The results indicate that the GA was able to arrive at better makespan with shorter run times.
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