| 研究生: |
宋偉欽 Sung, Wei-Chin |
|---|---|
| 論文名稱: |
以模擬最佳化求解自行車鏈條生產系統之批量決策問題 The Use of Simulation Optimization in Solving the Production Batching Problem from Bicycle Chain Manufacturing |
| 指導教授: |
楊大和
Yang, Ta-Ho |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 製造資訊與系統研究所 Institute of Manufacturing Information and Systems |
| 論文出版年: | 2017 |
| 畢業學年度: | 105 |
| 語文別: | 中文 |
| 論文頁數: | 87 |
| 中文關鍵詞: | 精實生產 、價值流圖 、批量 、CONWIP 、離散事件模擬 、模擬最佳化 |
| 外文關鍵詞: | Lean production, VSM, Batch size, CONWIP, Discrete-event simulation, Simulation optimization |
| 相關次數: | 點閱:163 下載:22 |
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本研究探討之案例公司為一家自行車鏈條製造廠,目前面臨少量多樣產品及銷售量不穩定的情況下,如何在既有的生產環境下改善生產流程的流暢性,降低生產的前置時間及縮短交期來增加製造系統的彈性;並且在滿足顧客訂單的情況下,以最低的庫存成本來進行有效的庫存管理,是目前自行車產業所面臨的重要議題。因此本研究將透過導入精實的生產的觀點,以小批量及CONWIP拉式生產系統的導入,進一步縮短生產前置時間及降低的在製品水準。
首先以價值流圖的繪製,找出現況之改善契機,在考慮產業特性及生產限制下,發展出可行的改善設計方案,進一步以模擬及最佳化工具產生各方案之績效值,接著以敏感度分析探討在需求變動的情況下,各方案對系統績效的影響。
根據實驗分析之結果,案例公司目前推行的改善方案前置時間改善百分比為25%,在製品數量改善百分比為21%;而案例公司未來的目標最佳化方案能使前置時間改善百分比為76%,在製品數量改善百分比為81%,由此可看出,導入CONWIP拉式生產系統並同時縮小製程中的加工批量及移轉批量,可以有效的縮短生產前置時間和降低在製品數量,能夠以更短的時間滿足顧客需求,提升系統績效。
Lean production is one of the initiatives that major businesses in the world have been trying to adopt in order to maintain competitive in an increasingly global markets.In this case, bicycle chain manufacturing presented here to be example, currently face to the small-volume, large-variety products and sales instability. Under the existing production environment how to improve reduce production lead time and shorten the production delivery to increase the flexibility of the manufacturing system.
First, we use Value Stream Mapping(VSM)to find out the potential improvement opportunities in the system. Second, This study is conducted to the case firm, we proposed appropriate lean principle to design the four scenario. And how to measure the performance of every scenario? We used discrete-event simulation to generate the value of performance index, and also used optimization tool to find out better production batching problem and Constant Work-in-process(CONWIP)level in order to reduce lead time and Work-in-process (WIP) level. Finally, we conduct a sensitivity analysis to discuss the different scenario performance under the circumstances of customer’s variable demand.
After experiment, the results shows that the proposed plans could reduce the WIP and lead time when throughput didn’t impacted. Based on these reasons, it represents the true efficiency in the manufacturing system. And at the end of the experiment, we use sensitivity analysis to see that when demand change if system need to meet service level. From sensitivity analysis can be proved that the proposed plan is still effective in different situations. Now, case firm have already impelled the improvement plan and it have brought advantages to them obviously.
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