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題名:造紙業損耗與存貨控制的新決策模型之研究
作者:劉豐泰
作者(外文):Feng-Tai Liu
校院名稱:國立臺灣科技大學
系所名稱:管理研究所
指導教授:王福琨
學位類別:博士
出版日期:2014
主題關鍵詞:彈性存貨配置原料裁切問題存貨決策模型排程損耗損耗最小化問題Flexible Stock AllocationCutting Stock ProblemInventoryDecision ModelSchedulingTrim LossTrim Loss Minimization Problem
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在造紙工業中,許多的研究探討訂單分配和生產損耗(Trim Loss)的最佳化,但這些結果會因某些存貨過多或低於可容忍排程量時,而不被企業所採用。為了讓整個結果能更符合實際的需求,本研究提出排程量的調整與存貨量的限制,取得不同的損耗比例數據與存貨分佈,再利用差異分析篩選損耗比例數據,以供決策者決策的一個新的決策模型,本研究使用一個實際案例說明了該方法的應用,研究結果顯示,我們提出的方法優於人工在排程量與損耗所使用的方法。
本研究也考量一維原料裁切問題(Cutting Stock Problem,簡稱CSP),針對工業用紙所產生非訂單的存貨寬度,且可以在未來使用時,提出了彈性存貨配置與損耗控制,以決定生產的一個新模型。而評估的方法是使用一實務的數據,展示其能夠解決產業排程的問題,同時也考量整體裁切的處理,例如在同一機器上彙整訂單與多種的存貨寬度以及不同的裁切模式。此外,本研究也進行與其他的模型比較,包括損耗最小化問題(Trim Loss Minimization Problem,簡稱TLMP)與原料裁切問題(CSP),研究結果顯示,本模型的整體彈性和損耗率優於這二種模型。
In the paper industry, numerous studies have explored optimizing order allocation and cutting trim loss. But, enterprises may not adopt the resulting solutions because some widths of the inventory are excessive or less than tolerable scheduling. To ensure the results better suit actual requirements, we present a new decision model based on the adjustment of scheduling and limitation of inventory quantity to differentiate data of trim loss and inventory distribution. Differential analysis is used for trim loss data filtering and the information is valuable for decision-making. A numerical example is presented to illustrate the applicability of the proposed method. The results show that our proposed method outperforms the manual method regarding scheduling quantity and trim loss.
In this paper, we also consider a one-dimensional cutting stock problem (CSP) in which the stock widths are not used to fulfill the order but kept for use in the future for the industrial-use paper production. We present a new model based on the flexible stock allocation and trim loss control to determine the production quantity. We evaluate our approach using some illustrative examples and show that we are able to solve industrial-size scheduling problems, while also addressing common cutting considerations such as aggregation of orders, multiple stock widths and cutting different patterns on the same machine. In addition, we compare our model with others, including trim loss minimization problem (TLMP) and cutting stock problem (CSP). The results show that the proposed model outperforms the other two models regarding total flexibility and trim loss ratio.
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