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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/9677

    Title: 模糊多階多目標生產規劃模式之研究-以國內某網路設備供應鏈為例(II)
    Authors: 邱煥能
    Contributors: 管理學院
    Keywords: 生產規劃;模糊多目標決策;多階多目標生產規劃;基因演算法
    production planning;fuzzy multiple objective;decision making;multi-level
    Date: 2007
    Issue Date: 2010-05-14 16:20:03 (UTC+8)
    Abstract: 在今日競爭激烈的全球商業環境下,產品日益翻新及生命週期較過去為短,因此,適時與正確的生產規劃(production planning)已成為一個管理者最重要的決策問題。然而,生產規劃本質上是一種複雜的多目標決策(multiple objective decision making,MODM),決策過程中所需的資訊通常存在不確定(uncertain)、含糊(vague)或不明確(imprecise)的資料。在文獻上,多目標決策主要應用在「規劃面/設計面」,通常探討在不同的限制條件下追求多個目標的同時達成,且獲得一個有效解(efficient solutions)或非劣解(non-inferior solutions) 之解集合。去年本計畫著重於解模糊二階多目標生產規劃模式,該模式是運用Sakawa方法求取二階廠商雙方最大滿意水準。今年本計畫修正去年研究方法應用於三階( 含) 以上之模糊多階多目標生產規劃模式(fuzzy multi-levelmulti-objective production planning models),再利用基因演算法求解並比較兩者的求解績效。
    限制下利潤最大化、成本最小化、人員變動最小化和服務水準最大化)的決策方法,甚少考慮到在批發商(或零售商,或顧客)與製造商間、製造商與供料商間相互影響下的生產規劃問題。本計畫將配銷對象及供料商的目標及限制式納入製造商生產規劃決策內,探討在模糊的環境下,以製造商為核心,同時滿足供應鏈所有成員利益的多階多目標生產規劃問題。本計畫首先在說明去年的研究成果,接著建構多目標函數及限制式。其次,修正Sakawa 方法求解模糊多階多目標生產規劃模式,再提出一種有效的基因演算法來求解,並進行實驗設計及求解績效比較分析。最後,本計畫將所發展的基因演算法應用於國內某知名電腦網路設備製造供應鏈,本研究的研究結果可作為業界實務運用之重要參考。
    1. 相關文獻探討。
    2. 研究問題描述。
    3. 建構模糊多階多目標生產規劃模式之目標函數與限制式。
    4. 介紹Sakawa及其他現行模糊多階多目標規劃模式之求解方法,並說明本計畫修正Sakawa方法的理由。
    5. 自行發展一種有效的基因演算法,並進行實驗設計及求解績效比較分析。
    6. 實務個案應用研究。
    multi-objective production planning, genetic algorithm Nowadays, due to the drastic competition in a global business environment, products are renovated quickly and their life cycles are shorter than before. To prepare an effective production plan is the most crucial decision problem for a production manager. In fact, production planning is essentially a complicated multiple objective decision making (MODM). The information required in the decision process usually involves uncertain, vague or imprecise data. In the literature, the MODM is applied to some aspects, including planning and design. It aims at achieving the best levels of relevant objectives simultaneously subject to some constraints, and getting a set of efficient solutions (i.e., non-inferior solutions). In the last year, this study focused on solving fuzzy two-level production planning models by using Sakawa’s method to obtain the maximized satisfaction level. In this year, however, we will revise Sakawa’s method, propose a genetic algorithm, and then compare the performance of the two methods in solving fuzzy multi-level multi-objective production planning models. In the past, most related researches were concentrated on multiple objectives (e.g., maximization of profit, minimization of cost, minimization of change rate of workforce level, and maximization of service level) set only by the manufacturers. The interdependent relationship between a wholesaler (or a retailer or a customer) and a manufacturer as well as that between a manufacturer and a supplier had been rarely discussed. This study will consider these relationships in terms of a manufacturer’s fuzzy production planning to incorporate its upstream and downstream members’ objectives and constraints into the manufacturer’s production planning decision process so that the benefits of all members in the supply chain can be attained. In this study, the results of the first year’s research will be described at first and then several fuzzy multi-level multi-objective functions and their related constraints will be built. Next, for a multi-level supply chain, we will revise Sakawa’s method, and propose an efficient genetic algorithm, and analyze the performance of the two proposed methods through conducting an experimental design. Finally, the proposed genetic algorithm will be applied to a well-known Taiwanese network equipment manufacturing supply chain. The conclusions drawn from this case study will be referred by practitioners.
    This study can be divided into the following six parts:
    1. Review the related literature.
    2. Describe the research problem.
    3. Build fuzzy multi-level multi-objective production planning functions and their related constraints.
    4. Introduce Sakawa’s method and existing approaches for solving fuzzy multi-level multi-objective planning models and explain the reason why Sakawa’s method will be revised.
    5. Develop an efficient genetic algorithm and a revised Sakawa’s method, design an experiment, and compare the performance of the two proposed methods.
    6. Study a practical case of a well-known Taiwanese network equipment manufacturing supply chain.
    Appears in Collections:[經營管理學系 ] 科技部研究計畫

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