當企業高階主管面臨是否允收客戶訂單之決策課題時，其往往依據過往經驗或是較粗略之產線產能評估作出客戶訂單是否允收之決策。然而，較主觀或較粗略之產線產能評估往往使企業高階主管誤判客戶訂單之允收決策，導致訂單流失而喪失訂單獲利機會，或導致訂單嚴重延遲而須額外支付訂單延遲費用、甚至影響企業商譽。另一方面，企業高階主管往往需要參照諸多供需兩端之關鍵資訊進行允收決策，如訂購產品、訂購數量、訂單期望交期、歷史生產數據、當前產線之待生產訂單數量、…等多項供需資訊。而此決策過程涉及諸多供需資訊的整合，企業高階主管往往需耗費較多時間才能作出較合理之接單決策。為解決上述問題，針對一到達之新訂單（本研究乃以「目標訂單」稱之），本研究乃發展一套「客戶訂單允收分析」模式，以透過此模式計算目標訂單之完工時間及預期利潤，並依據是否能使企業獲利判定目標訂單之允收結論，以協助企業高階主管進行訂單允收決策。此模式可分為「前置資料建構」前置階段與「客戶訂單允收決策分析」方法論兩部份。於「前置資料建構」前置階段中，本研究首先取得本研究所提出之三大類供需資訊，並以所取得之三大類供需資訊為基礎，利用數據配適之機率統計方法取得各產品之生產時間分佈；之後，本研究乃根據前置階段所取得之三大類供需資訊及各產品之生產時間分佈資訊，發展「客戶訂單允收決策分析」方法論。「客戶訂單允收決策分析」方法乃基於各待生產產品（目標訂單中產品及當前產線待生產產品）之生產時間分佈，推算於使用者所期望之目標訂單完工機率下目標訂單中各產品之完工時間。之後，本研究乃考量客戶重要度、目標訂單完工收益、目標訂單延遲成本等因素求得目標訂單之預期利潤。最後，本研究乃依據是否能使企業獲利判定目標訂單之允收結論，以協助企業高階主管進行訂單允收決策。

When order decision makers are faced with the decision of whether to accept customer orders, they often make decisions based on past experience or rough estimates of production capacity. However, subjective or rough customer order acceptance decisions often cause the order decision makers to misestimate the production capacity, resulting in loss of orders and also profit, or serious delays in orders and additional payment of order delay, even affect corporate goodwill. On the other hand, order decision makers often need consider a lot of supply and demand key information to make acceptance decisions, such as ordering products, ordering quantities, order expected delivery dates, historical production data, quantity of current order,... etc. This decision-making process involves the integration of a lot of supply and demand information, and order decision makers often need time to make a more reasonable order decision. In order to solve the above problems, for new orders that arrive (this research is referred to as "target orders"), this research develops a model of "customer order acceptance analysis" to calculate the completion time and expected profit of target orders, and based on whether it can make the enterprise profitable to determine the acceptance conclusion of the target order, so as to assist the order decision maker in the order acceptance decision.

1. Schuh, G., Prote, J. P., Sauermann, F. and Franzkoch, B., 2019, “Databased Prediction of Order-Specific Transition Times,” CIRP Annals, Vol. 68, No. 1, pp. 467-470.
2. Yang, D., Hu, L. and Qian, Y., 2017, “Due Date Assignment in a Dynamic Job Shop with the Orthogonal Kernel Least Squares Algorithm,” Materials Science and Engineering, Vol. 212, No. 1, pp. 12-22.
3. Lee, G. C., 2014, “Real-Time Order Flowtime Estimation Methods for Two-Stage Hybrid Flowshops,” Omega, Vol. 42, No. 1, pp. 1-8.
4. Wu, H. C. and Chen, T., 2014, “CART–BPN Approach for Estimating Cycle Time In Wafer Fabrication,” Journal of Ambient Intelligence and Humanized Computing, Vol. 6, No. 1, pp. 57-67.
5. Chen, T., 2011, “Job Cycle Time Estimation in a Wafer Fabrication Factory with a Bi-Directional Classifying Fuzzy-Neural Approach,” The International Journal of Advanced Manufacturing Technology, Vol. 56, pp. 1007-1018.
6. Chen, T., 2009, “A fuzzy-Neural Knowledge-Based System for Job Completion Time Prediction and Internal Due Date Assignment In A Wafer Fabrication Plant,” International Journal of Systems Science, Vol. 40, No. 8, pp. 889-902.
7. Abdulrahman, A., Scott, A. M. and Theodore, B. T., 2008, “Real-Time Prediction of Order Flowtimes Using Support Vector Regression,” Computers & Operations Research, Vol. 35, No. 11, pp. 3489-3503.
8. Rahul, J. P., 2008, “Using Ensemble and Metaheuristics Learning Principles with Artificial Neural Networks to Improve Due Date Prediction Performance,” International Journal of Production Research, Vol. 46, No. 21, pp. 6009-6027.
9. Chang, P. C. and Liao, T. W., 2006, “Combining SOM and Fuzzy Rule Base for Flow Time Prediction in Semiconductor Manufacturing Factory,” Applied Soft Computing, Vol. 6, No. 2, pp. 198-206.
10. Sha, D. Y. and Hsu, S. Y., 2004, “Due-Date Assignment in Wafer Fabrication Using Artificial Neural Networks,” International Journal of Advanced Manufacturing Technology, Vol. 23, No. 9-10, pp. 768-775.
11. Chang, P. C., Hsieh, J. C. and Liao, T.W., 2001, “A Case-Based Reasoning Approach for Due-Date Assignment in a Wafer Fabrication Factory,” Lecture Notes in Computer Science, Vol. 2080, pp. 648-659.
12. Faisal, A., Abdulaziz, A. and Omar, A., 2017, “An Approach for Rush Order Acceptance Decisions Using Simulation and Multi-Attribute Utility Theory,” European Journal of Industrial Engineering, Vol. 11, No. 5, pp. 607-611.
13. Xu, L., Wang, Q. and Huang, S., 2015, “Dynamic Order Acceptance and Scheduling Problem with Sequence-Dependent Setup Time,” International Journal of Production Research, Vol. 53, No. 19, pp. 5797-5808.
14. Manavizadeh, N., Tavakoli, L. and Rabbani, M., 2013, “A Multi-Objective Mixed-Model Assembly Line Sequencing Problem in Order to Minimize Total Costs in a Make-to-Order Environment with Considering Order Priority,” Journal of Manufacturing Systems, Vol. 32, No. 1, pp. 124-137.
15. Bahriye, C., Ceyda, O. and Salman, F. S., 2012, “A Tabu Search Algorithm for Order Acceptance and Scheduling,” Computers & Operations Research, Vol. 36, No. 6, pp. 1197-1205.
16. Huang, S., Lu, M. and Wan, G., 2011, “Integrated Order Selection and Production Scheduling under MTO Strategy,” International Journal of Production Research, Vol. 49, No. 13, pp. 4085-4101.
17. Mahdokht, K., Masoud, R. and Mahmood, E., 2011, “A Decision Support System for Order Acceptance/Rejection in Hybrid MTS/MTO Production Systems,” Applied Mathematical Modelling, Vol. 35, No. 3, pp. 1363-1377.
18. Ebadian, M., Rabbani, M. and Jolai, F., 2008, “A New Decision-Making Structure for the Order Entry Stage in Make-to-Order Environments,” International Journal of Production Economics, Vol. 111, No. 2, pp. 351-367.
19. Walter, O. R. and Susan, A. S., 2009, “Order Acceptance Using Genetic Algorithms,” Computers & Operations Research, Vol. 36, No. 6, pp. 1758-1767.
20. Gharehgozli, A. H., Rabbani, M. and Zaerpour, N., 2008, “A Comprehensive Decision-Making Structure for Acceptance/Rejection of Incoming Orders in Make-To-Order Environments,” International Journal of Advanced Manufacturing Technology, Vol. 39, pp. 1016-1032.
21. Ebben, M. J., Hans, E. W. and Olde, F. M., 2005, “Workload Based Order Acceptance in Job Shop Environments,” OR Spectrum, Vol. 27, pp. 107-122.
22. Wu, M. C. and Chen, S. Y., 1997, “A Multiple Criteria Decision-Making Model for Justifying the Acceptance of Rush Orders,” Production Planning & Control, Vol. 8, No. 8, pp. 753-761.