在快速變化的消費性商品市場中，面對高度的市場競爭，產品必須不斷推陳出新，方可搶得市場商機。然而，消費者的喜好與需求變化得相當快，經常造成廠商因為缺貨而錯失商機，或者因為高估了需求量而賣不出去，而導致折價出清。為了因應快速變化的市場，許多公司開始採用了快速反應製造系統(Quick Response Manufacturing, QRM)，以縮短總前置時間的方式，來達成快速交貨。在快速反應生產系統中，工廠在接到訂單後，從各地供應商取得零件，並加工成為顧客所需的產品，並運送至各地的市場。為了能夠縮短在整個供應鏈中的前置時間，廠商在決定工廠位置時，除了必須考慮建廠成本與製造成本，也必須同時考慮供應鏈中前置時間以及需求的不確定因素，方可訂定出能快速反應市場需求之最佳設廠與配送策略，以獲取最大利潤。

本研究提出一個三階層的快速反應製造系統供應鏈模型，包含材料供應商、工廠以及市場。假設產品上市時間對於某產品的市場價值具有顯著影響，客戶會在合約與工廠約定，不同產品交期支付不同價格，包含提前交貨獎勵以及延遲交貨罰款的概念。此模型在考慮需求不確定以及前置時間不確定的因素下，決定最佳工廠設置選址及供應鏈配置，以求取最大利益。本研究利用基因演算法(GA)來求解此二階隨機規劃問題，問題研究結果發現，基因演算法能夠有效率的求解此問題。本實驗設計了一個問題模型，並計算出了其在經過一段時間後之預期總利潤，並透過前置時間以及需求量因子的敏感度分析，可以計算出前置時間對於總利潤的影響程度，以協助管理者評估設廠風險。另外，也分析出攤平設廠成本所需要的期數，以作為投資決策的另一項參考依據。

In the fast-changing consumer's product market, producers must keep launching new products to sustain its market competitiveness. However, the trend and the demand of products changes very fast, which often cause the stock-out or overstock. In order to respond to fast-changing market, many companies start to implement Quick Response Manufacturing (QRM) system in order to shorter the time to market. In the QRM system, when the producers received a product order, they start the process of procurement of materials, production and delivery to markets. In order to shorter all the lead time during these processes, when designing a supply chain, it's very important to consider the factors of uncertainty of lead time and demand.
This research proposed a 3-echelon QRM supply chain model, which includes material suppliers, factories and markets. Assume that the time-to-market (TTM) of a product would influence its market value, in the contract that signed by producer and customer, it notifies the varying price related to its delivery date, which includes the concept of early-delivery incentive and late-delivery penalty. This model is to decide the optimal factory allocation and supply chain design considering both the demand and lead time uncertainty. The genetic algorithm is applied to solve this two-stage stochastic programming problem. In the experiment, a model problem is designed and its accumulated profit after some periods is also calculated. The result showed that genetic algorithm is an effective method to solve this problem.

References
Alonso-Rasgado, T., & Thompson, G. (2006). A rapid design process for Total Care Product creation. Journal of Engineering Design, 17(6), 509–531. doi:10.1080/09544820600750579
De Treville, S., Bicer, I., Chavez-Demoulin, V., Hagspiel, V., Schurhoff, N., Tasserit, C., & Wager, S. (2014). Valuing Lead Time. Journal of Operations Management.
Fisher, M., Hammond, J., Obermeyer, W., & A. Raman. (1997). Configuring a supply chain to reduce the cost of demand uncertainty, 6 (3), pp. 211–225.
Fisher, M. L. (2004). The Lagrangian Relaxation Method for Solving Integer Programming Problems. Management Science, 50(12_supplement), 1861–1871. doi:10.1287/mnsc.1040.0263
Fisher, M., & Raman, A. (1996). Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales. Operations Research, 44(1), 87–99. doi:10.1287/opre.44.1.87
Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 13(5), 533–549.
Holland, J. H. (1975). Adaptation in natural and artificial systems.
Mousavi, S. M., & Niaki, S. T. A. (2013). Capacitated location allocation problem with stochastic location and fuzzy demand: A hybrid algorithm. Applied Mathematical Modelling, 37(7), 5109–5119.
SENJYU, T., SHIMABUKURO, K., YAMASHIRO, H., UEZATO, K., & FUNABASHI, T. (2004). Determination of Location and Capacity of Power Facilities by Genetic Algorithm. Electric Power Components and Systems, 32(4), 375–390.
Suri, R. (1998). Quick Response Manufacturing. Productivity Press, Portland, OR.
Suri, R. (2010). It’s About Time. The Competitive Advantage of Quick Response Manufacturing. Productivity Press, Portland, OR.
The Future Of Fashion Retailing -- The H&M Approach. (2012). Forbes.
Walters, D. C., & Sheble, G. B. (1993). Genetic algorithm solution of economic dispatch with valve point loading. IEEE Transactions on Power Systems, 8(3), 1325–1332.
Wang, K.-J., Makond, B., & Liu, S.-Y. (2011). Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution. Expert Systems with Applications, 38(5), 6125–6131.
Yalcinoz, T., Cory, B. J., & Short, M. J. (2001). Hopfield neural network approaches to economic dispatch problems. International Journal of Electrical Power & Energy Systems, 23(6), 435–442.