最小化總加權延後時間與碳排放量之雙機批次處理排程問題

批次處理(batch processing)已經廣泛用於不同的加工環境下，如化工製程中的冷處理、熱處理，及半導體製程中的預燒製程，並隨者環保意識的興起，各工廠開始減少生產過程程中的碳排放量，因此，本研究在雙機批次處理排程中將碳排放量設為目標值，並與總加權延後時間做雙目標排程問題。本研究針對最小化總加權延後時間與碳排放量之雙機批次處理排程問題提出修正型多目標反覆貪婪演算法(Modified Iterated Pareto Greedy; MIPG)，經由實驗結果證實本研究所提出之演算法之求解績效較多目標反覆貪婪演算法(Iterated Pareto Greedy; IPG)優異，相關研究成果可提供業者實務應用及後續相關學術研究的參考。

關鍵字

排程；雙機批次處理；碳排放量；反覆貪婪演算法

並列摘要

Batch processing machines (BPMs) are encountered in many different environments such as chemical processes performed in tanks or kilns and burn-in operations in semiconductor industrials. With the rise of environmental awareness, each factory heavily emphasizes reducing carbon emission. Therefore, this research dealt with the problem of scheduling jobs in a two machine flowshops with batch processing machines to minimize the carbon emission and total weighted tardiness minimized, and proposed a modified iterated Pareto greedy (MIPG) algorithm to solve the problem. The experimental results showed that the proposed MIPG algorithm outperformed the iterated Pareto greedy algorithm.

並列關鍵字

Scheduling ； Two Machine Flowshop with Batch Process ； Carbon Emission ； Iterated Greedy heuristic

參考文獻

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最小化總加權延後時間與碳排放量之雙機批次處理排程問題

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