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研究生： 陳昱任
Chen, Yu-JEN
論文名稱： 產能規劃於精密加工產業之應用
Capacity Planning for Precision Machinery Industry
指導教授： 陳建良
Chen, James c.
口試委員: 陳子立
Chen, Tzu-Li
羅明琇
Lo, Ming-Shiow
學位類別： 碩士
Master
系所名稱： 工學院 - 工業工程與工程管理學系
Department of Industrial Engineering and Engineering Management
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 60
中文關鍵詞： 產能規劃 、實驗設計 、最佳設定 、精密加工產業
外文關鍵詞： capacity planning, experimental design, optimal setting, precision machinery industry
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    • 精密加工廣泛應用於各個製造相關行業，終端產品包含從消費性電子、汽車零件到醫療和實驗室測試儀器等。日漸複雜的客戶需求造成製造業的高度競爭，因此如何提升生產效率及滿足顧客需求會是重要因素。本研究之目的為開發一套產能規劃系統（CPS）幫助精密加工業進行產能規劃與規劃出合適的排程，此系統藉由Microsoft Visual Basic應用程式（VBA）進行開發。產能規劃系統在考慮生產資源與製程特性的情況下，制定出可行之生產計劃，以增加生產效率。此產能規劃系統包含七大產能規劃模組：訂單處理模組（OTM）、訂單排序模組（OPM）、批量加工模組（LRM）、負荷累積模組（WAM）、負荷平衡模組（WBM）、治具管理模組（FMM）及途程選擇模組（RSM）。
    實驗結果顯示，CPS可有效安排每日機台負荷量，避免負荷量過高。本研究除了開發CPS外，亦利用實驗設計來評估CPS與各種因素和水準的效率。另外，此研究利用變異數分析（ANOVA）評估並分析因子針對反應值的影響。最後，此研究利用八種不同的環境因子、兩種策略控制因子求出可行解，期協助決策者在不同生產環境下，找出最佳策略與訂單排序。

    Precision machinery industry services a wide range of end markets which ranges from the automotive parts, consumer electronics to medical and laboratory test equipment. Increasingly complicated requirement makes the manufacturing industry highly competitive. Therefore, it is critical to increase production efficiency and satisfy the customer demand. The aim of the proposed research is to develop a Capacity Planning System (CPS) to assist precision machinery industry in calculating the production capacity and finding the appropriate schedule. CPS is developed using Microsoft Visual Basic for Application (VBA) to generate a feasible production schedules and avoid overcapacity by considering production resource and process characteristics. This can be accomplished by implementing seven major modules in the system: Order Treatment Module (OTM), Order Priority Module (OPM), Lot Release Module (LRM), Workload Accumulation Module (WAM), Workload Balance Module (WBM), Fixture Management Module (FMM), and Routing Selection Module (RSM).
    CPS results indicated that CPS could effectively reduce overcapacity. In addition, experimental design is used to evaluate the effectiveness and efficiency of the proposed CPS. Analysis of variance (ANOVA) is used to evaluate the factors in accordance to the responses. Moreover, the optimal settings for the control factors under 8 combinations of different environment factors are also evaluated. This can assist decision makers to use the optimal settings of control factors to generate the best favorable response under different environment factors.

    摘要 II Abstract III 致謝 IV Contents V List of Figure VII List of Table VIII Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Objectives 3 1.3 Research Method 4 1.4 Organization of Thesis 5 Chapter 2 Literature Review 6 2.1 Production Scheduling 6 2.2 Scheduling Problem in CNC machine 7 2.3 Capacity Planning System in Various Industries 9 Chapter 3 Methodology 15 3.1 Problem Description 15 3.1.1 Assumptions and Notations 20 3.2 Capacity Planning System 23 3.2.1 Order Treatment Module (OTM) 24 3.2.2 Order Priority Module (OPM) 25 3.2.3 Lot Release Module (LRM) 26 3.2.4 Workload Accumulation Module (WAM) 28 3.2.5 Workload Balance Module (WBM) 29 3.2.6 Fixture Management Module (FMM) 33 3.2.7 Routing Selection Module (RSM) 33 Chapter 4 Experiments and Results 34 4.1 CPS Simulation Environment 34 4.2 CPS Result 37 4.3 Analysis of Experimental Design 39 4.3.1 Experimental Design Factors 39 4.3.2 Experimental Design Responses 41 4.4 Experimental Results and Analysis 42 4.4.1 ANOVA Result 42 4.4.2 The Main Effects Plot 46 4.4.3 The Interaction Plot 48 4.4.4 Optimal Design under Different Environment Factors 49 Chapter 5 Conclusion 54 Reference 56

    Abedinnia, H., Glock, C. H., & Schneider, M. D. (2017). Machine scheduling in production: a content analysis. Applied Mathematical Modelling, 50, 279-299.
    Atan, M. O., & Selim Akturk, M. (2008). Single CNC machine scheduling with controllable processing times and multiple due dates. International Journal of Production Research, 46(21), 6087-6111.
    Chan, F. T. S., Wong, T. C., & Chan, L. Y. (2006). Flexible job-shop scheduling problem under resource constraints. International Journal of Production Research, 44(11), 2071-2089.
    Chen, J. C., Chen, C. W., Lin, C. J., & Rau, H. (2005). Capacity planning with capability for multiple semiconductor manufacturing fabs. Computers & Industrial Engineering, 48(4), 709-732.
    Chen, J. C., Chen, T. L., & Harianto, H. (2017). Capacity planning for packaging industry. Journal of manufacturing systems, 42, 153-169.
    Chen, J. C., Chen, T. L., Pratama, B. R., & Tu, Q. F. (2016). Capacity planning in thin film transistor - Liquid crystal display cell process. Journal of Manufacturing Systems, 39, 63-78.
    Chen, J. C., Chen, T. L., Pratama, B. R., & Tu, Q. F. (2016). Capacity planning with ant colony optimization for TFT-LCD array manufacturing. Journal of Intelligent Manufacturing, 1-19.
    Chen, J. C., Fan, Y. C., & Chen, C. W. (2009). Capacity requirements planning for twin Fabs of wafer fabrication. International Journal of Production Research, 47(16), 4473-4496.
    Chen, J. C., Su, L. H., Sun, C. J., & Hsu, M. F. (2010). Infinite capacity planning for IC packaging plants. International Journal of Production Research, 48(19), 5729-5748.
    Chen, J. C., Sun, C. J., & Chen, T. L. (2015). Capacity planning for integrated circuit final test plants. International Journal of Computer Integrated Manufacturing, 28(12), 1262-1274. doi:10.1080/0951192x.2014.964324
    Chen, T. L., Lin, J. T., & Wu, C. H. (2014). Coordinated capacity planning in two-stage thin-film-transistor liquid-crystal-display (TFT-LCD) production networks. Omega-International Journal of Management Science, 42(1), 141-156.
    Choi, B. K., & Seo, J. C. (2009). Capacity-filtering algorithms for finite-capacity planning of a flexible flow line. International Journal of Production Research, 47(12), 3363-3386.
    Costa, N. R., Lourenco, J., & Pereira, Z. L. (2011). Desirability function approach: A review and performance evaluation in adverse conditions. Chemometrics and Intelligent Laboratory Systems, 107(2), 234-244.
    Derringer, G., & Suich, R. (1980). Simultaneous-Optimization of Several Response Variables. Journal of Quality Technology, 12(4), 214-219.
    Fuchigami, H. Y., & Rangel, S. (2017). A survey of case studies in production scheduling: Analysis and perspectives. Journal of Computational Science.
    Graves, S. C. (1981). A review of production scheduling. Operations research, 29(4), 646-675.
    Gurel, S., & Selim Akturk, M. (2008). Scheduling preventive maintenance on a single CNC machine. International Journal of Production Research, 46(24), 6797-6821.
    Harjunkoski, I., Maravelias, C. T., Bongers, P., Castro, P. M., Engell, S., Grossmann, I. E., ... & Wassick, J. (2014). Scope for industrial applications of production scheduling models and solution methods. Computers & Chemical Engineering, 62, 161-193.
    Jahromi, M. H. M. A., Tavakkoli-Moghaddam, R., Makui, A., & Saghaei, A. (2017). A novel mathematical model for a scheduling problem of dynamic machine-tool selection and operation allocation in a flexible manufacturing system: A modified evolutionary algorithm. Scientia Iranica. Transaction E, Industrial Engineering, 24(2), 765.
    Khan, B. K., Gupta, B. D., Gupta, D. S., & Kumar, K. D. (2000). A generalized procedure for minimizing tool changeovers of two parallel and identical CNC machining centres. Production Planning & Control, 11(1), 62-72.
    Lasemi, A., Xue, D., & Gu, P. (2010). Recent development in CNC machining of freeform surfaces: A state-of-the-art review. Computer-Aided Design, 42(7), 641-654.
    Liao, C. J., & Juan, H. C. (2007). An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups. Computers & Operations Research, 34(7), 1899-1909.
    Lin, J. T., Chen, T. L., & Chu, H. C. (2014). A stochastic dynamic programming approach for multi-site capacity planning in TFT-LCD manufacturing under demand uncertainty. International Journal of Production Economics, 148, 21-36.
    Mauergauz, Y. (2017). Job and tool group scheduling for a machining center. International Journal of Management Science and Engineering Management, 1-8.
    Mhiri, E., Jacomino, M., Mangione, F., Vialletelle, P., & Lepelletier, G. (2014). A step toward capacity planning at finite capacity in semiconductor manufacturing. Paper presented at the Proceedings of the 2014 Winter Simulation Conference.
    Mhiri, E., Jacomino, M., Mangione, F., Vialletelle, P., & Lepelletier, G. (2015). Finite capacity planning algorithm for semiconductor industry considering lots priority. IFAC-PapersOnLine, 48(3), 1598-1603.
    Mousakhani, M. (2013). Sequence-dependent setup time flexible job shop scheduling problem to minimise total tardiness. International Journal of Production Research, 51(12), 3476-3487.
    Naderi, B., Ruiz, R., & Zandieh, M. (2010). Algorithms for a realistic variant of flowshop scheduling. Computers & Operations Research, 37(2), 236-246.
    Precision Parts Market - Global Outlook and Forecast 2017-2022. Retrieved from:
    https://www.arizton.com/market-reports/precision-parts-market-report
    Shivanand, H. K. (2006). Flexible manufacturing system. New Age International.
    Tao, F., Cheng, Y., Zhang, L., & Nee, A. Y. (2017). Advanced manufacturing systems: socialization characteristics and trends. Journal of Intelligent Manufacturing, 28(5), 1079-1094.
    Turkcan, A., Akturk, M. S., & Storer, R. H. (2007). Due date and cost-based FMS loading, scheduling and tool management. International Journal of Production Research, 45(5), 1183-1213.
    Turkcan, A., Akturk, M. S., & Storer, R. H. (2009). Predictive/reactive scheduling with controllable processing times and earliness-tardiness penalties. IIE Transactions, 41(12), 1080-1095.
    Xu, X. W., & Newman, S. T. (2006). Making CNC machine tools more open, interoperable and intelligent—a review of the technologies. Computers in Industry, 57(2), 141-152.

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