客服中心為企業和顧客聯繫時的主要方式，且在全球各地都具有一定程度的發展。近年來在作業研究和管理科學方面，對於客服中心問題的研究也日益增加。客服中心主要是勞力密集的經營模式，由於資訊設備的快速發展，因此硬體設備不再是客服中心主要的成本。目前客服中心主要的成本花費60%至70%為人員僱用成本，故考量如何能在滿足一定程度的服務水準(service levels)之下，使得人員僱用成本最小，此為客服中心最主要探討的問題。由於在估計服務水準(service levels)時的複雜度較高，因此適合利用系統模擬建構模型，並且估計客服中心問題的服務水準限制式。Atlason et al.(2004,2008)利用系統模擬建構客服中心模型，再分別使用切面法及中心切面法求解客服中心問題，此二種方法在樣本數夠大時演算法都能收斂至近似最佳解，但在模擬成本受限之下，並不能保證所得到的解為可行解，且一般樣本數目愈大代表花費的成本愈大。

有鑑於此，本研究以離散型模擬最佳化(DOvS)演算法中之隨機搜尋(Random Search)為基礎，利用Hong and Nelson(2006)COMPASS演算法中MPA(Most Promising Area)概念，增加隨機搜尋的收斂速度，並結合排序與選取程序(R&S)中Batur and Kim (2010)發展之多重可行性驗證程序(MFCP)，針對多重隨機限制式進行可行性驗證，進而挑選出可行解集合，再從可行解集合中依照確定性目標式選出最適解，在1-α信心水準之下保證所找到的解為可行解。

Call center is an important way of businesses to communicate with their customers, and have a certain degree of development around the world. In recent years the research of call center is also increasing in operations research and management science. The call center is labor-intensive business model, due to the rapid development of information technology equipment, so the hardware is no longer the major cost of call center.

The major cost of the call center to spend 60 % to 70 % for the cost of labor, so the most important of call center is considered how to satisfy certain service levels under the staffing level. It’s complexity to estimate the service levels, therefore we use simulation to construct the model and estimate the call center service level constraint. Atlason et al (2004, 2008) use of simulation to construct the call center model, using the cutting plane method and the center cutting plane method for solving the problem of call center, respectively. In the number of samples is large enough for two kinds of methods can converge to optimal solution but in the simulation costs limited, does not guarantee the solution is feasible solution, and in general the more number of samples representative the more costs.

We use the random search in the discrete optimization via simulation, based on the MPA of Hong and Nelson (2006) COMPASS concept to increase the convergence rate of random search, combined with Batur and Kim(2010) ranking and selection procedure (R&S), the development of multiple feasibility check procedure (MFCP) for the case of multiple stochastic constraints, pick out the feasible solution into a feasible solution set, according to the deterministic objective function selection the optimal solution from the feasible solution set with 1 - α confidence level guarantee to find the solution as a feasible solution.

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