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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/78954


    Title: 整合巨量資料與計算型智慧技術探討預鑄構件施作難易程度分級、勞工成本管控以及人力派遣最佳化之研究(II);Integrating Precast Big Data and Computational Intelligence to Classify Construction Difficulty, Manage Direct Labor Costs, and Optimize Manpower Allocation(Ii)
    Authors: 陳介豪
    Contributors: 國立中央大學營建管理研究所
    Keywords: 預鑄構件;分群演算法;人力資源;成本;最佳化演算法;;construction precast;clustering;human resource;cost control;optimization algorithm
    Date: 2018-12-19
    Issue Date: 2018-12-20 14:20:56 (UTC+8)
    Publisher: 科技部
    Abstract: 我國之預鑄產業若面臨大產量訂單,往往因為無法準確估算人力成本,致使利潤無法穩定掌 控,甚至因此造成派工困難,或是生產線阻塞等問題。一直以來面臨構件施作需仰賴密集技術工 且又同時存在勞工超時、人力分派不均之現象,預鑄廠至今仍處於尋求解決之道階段。本計畫之 目的即為解決『預鑄構件難易程度分級』、『勞工超時成本控管』以及建構『人力派遣最佳化』模 型。本計畫之文獻回顧探討可分為:預鑄工廠生產模式及流程、分群演算法及資料探勘、人力資 源和成本管控、最佳化策略及探討,研究方法透過文獻探討與專家訪談建立。研究數據的蒐集係 仰賴實地訪查,二年期分別蒐集相對應的數據,並各自進行統計分析,以達成各年期之研究目的。 第二年期的研究資料將結合前述研究成果,預鑄構件分級與勞工成本控管,並且加上勞工成本資 料,最終以改良式粒子群演算法得出人力派遣最佳化成果。本研究預期之貢獻,在學術研究方面, 各年期的研究均可以各自發展出國際SCI 期刊論文。在國家發展方面,本研究之成果,提供預 鑄工廠作最佳人力配置規劃之用,以降低預鑄工廠勞工成本之開銷。最後,本研究將可作為如何 提升整體專案利潤研究之基石。 ;Companies in the construction precast industry usually face lack of skilled manpower, overtime working, and difficulty of manpower allocation. The objectives of this research are to identify the difficulty level of precast elements, to reduce overtime working, and to optimize manpower allocation for construction precast companies. After conducting a comprehensive literature review regarding precast production, clustering, classification, cost management, manpower allocation, and optimization, expertise from field/headquarter supervision will lead the way to SIP clustering algorithm that drives collected data converted to certain clusters. These levels are anticipated to be the difficulty levels. Integrating recourse leveling techniques with the difficulty levels, we can re-arrange manpower to reduce unnecessary overtime so as to save some costs. In the last research stage, the outputs from the previous two stages will be utilized as the restrictions to optimize the manpower allocation. The anticipated findings should be not only directly used for construction precast companies to improve cost control and management for manpower, but verify the feasibility for integrating computational algorithm with practical big data.
    Relation: 財團法人國家實驗研究院科技政策研究與資訊中心
    Appears in Collections:[營建管理研究所 ] 研究計畫

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