English  |  正體中文  |  简体中文  |  Items with full text/Total items : 70588/70588 (100%)
Visitors : 23072617      Online Users : 627
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/73880


    Title: 以基因演算法規劃啤酒釀造排程;Applying Genetic Algorithm to Schedule Brewery Production
    Authors: 裴春全;Toan, Bui Xuan
    Contributors: 工業管理研究所
    Keywords: 排程規劃;批量;啤酒工業;兩階段生產法;基因演算法;scheduling;lot sizing;brewery industry;two-stage production;GA
    Date: 2017-07-17
    Issue Date: 2017-10-27 12:28:20 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 中文摘要
    排程規劃在製造業有舉足輕重的地位,本研究就是站在這角度上進行研究的延伸。
    由於排程規劃是指數型成長的複雜性問題,在限制求解時間的條件下,此劃問題可以被
    歸類為Np-hard 的問題。因為上述原因,目前在規劃排程的應用上,不是站在求得最佳
    解的角度去尋找方向,而是使用啟發式的演算法來解決排程上的研究議題。
    目前有許多學者研究如何使用不同地啟發式演算法來解決排程議題,如區域搜
    尋、禁忌搜尋和基因演算法等方法。根據近幾年的學術研究趨勢,基因演算法被認定為
    一個有效率的解決排程議題的演算法,也由於基因演算法逐漸地被重視下,其所討論的
    研究領域與應用類型更為全面。
    在啤酒製造工業,作物的發酵過程是整個釀造過程中的關鍵。此過程決定了啤酒的
    品質、配方、風味以及生產線上的流程規劃。原料發酵的過程佔據了整個生產流程約41
    天的時間,而每一種不同類型的啤酒也有其發酵需求時間和限制,有效地規劃發酵流程
    對於啤酒釀造是非常重要的。本研究提出如何使用基因演算法,進行排程規劃求解最適
    宜的生產排程方式,期望藉由有效地生產計劃與分析工具,利用機台的排程規劃搭配,
    減少生產排程中,可能發生的瓶頸時間以及準備時間。
    關鍵字:排程規劃、批量、啤酒工業、兩階段生產法、基因演算法。;Abstract
    Scheduling is one of the most important problems in any manufacturing industry.
    Therefore, the problem has been studied extendedly. Since, the scheduling problem is classified
    as NP-hard problem, which means the time required for finding the optimal solution of the
    problem is grown exponentially with the size of the problem. Therefore, it is unrealistic to find
    optimal solution for the scheduling problem in the scene of the real world industrial case, even
    with today advanced computer system.
    There are many heuristic algorithms have been proposal to solve the scheduling problem.
    They are beam search, local search technique, tabular search and Genetic Algorithm (GA), to
    name a few. In recent years, GA has become a noticeable candidate for solving the scheduling
    problem effectively. The idea of mimicking the evolutionary process is very interesting to
    researchers. And the recent advanced in heuristic GA has sparked more attention toward new
    research and application in the field of GA.
    In Brewery industry, the fermentation process is the most crucial components of the whole
    manufacturing process. It will decide the quality, taste of the products as well as the
    productivity of the production line. Since, the fermentation time can take up to 41 days, and the
    requirement time is varying a lot between different types of beers, therefore finding a good
    scheduling solution to dealing with this complexity is crucial for beer manufacturers. This
    research will propose a GA to solve the scheduling problem in beer production. The proposed
    methodology will serve as a planning and analysis tool to utilize assets (tanks, filling lines)
    effectively, reduce congestion and synchronize the production process between the two
    production stages (liquid preparation and bottling).
    Keywords: scheduling, lot sizing, brewery industry, two-stage production, GA.
    Appears in Collections:[工業管理研究所 ] 博碩士論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML314View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明