OPTIMIZING STAFF SCHEDULING BY MONTE CARLO SIMULATION

医疗机构经常面临艰难的员工调度 有问题。 每一家医院和医疗团体都会迎来一位功能性的， 计算机化 主动优化调度的解决方案。 DOCS算法-结合记账框架， 约束条件，以及 蒙特卡罗模拟--作为一种新的优化方法 工作人员 在医院(企业)范围内进行排班。第二阶段将 发展 Docs/Enterprise，一个解决大规模问题的软件应用程序 工作人员 医院环境中的日程安排问题。 医生/企业将在两个医疗中心接受阿尔法测试 机构应 验证设计和功能以及另外三个 院校 将参与可用性(Beta)测试。 第一阶段证明了DOCS算法非常适合于大型 大小 医务人员的排班问题。因为没有其他商业广告 软件 产品和任何已发布的算法都无法与 医生， 这是一个巨大的发展商机。 的 Docs/Enterprise。每一家医院都将从这些工作人员中受益 排程 系统。对于一家典型的医院来说，每年节省20万美元。

Medical facilities constantly face difficult staff scheduling problems. Every hospital and medical group will welcome a functional, computerized solution that actively optimizes scheduling. The DOCS algorithm - combining an accounting framework, constraints, and a Monte-Carlo stimulation - serves as a new methodology for optimal staff scheduling on a hospital-wide (enterprise) basis. Phase II will develop DOCS/Enterprise, a software application for solving large-scale staff scheduling problems in a hospital environment. DOCS/Enterprise will undergo Alpha testing at two medical institutions to verify the design and functionality and three additional institutions will participate in usability (Beta) testing. Phase I proved that the DOCS algorithm is well-suited for large size medical staff scheduling problems. Since no other commercial software products nor any published algorithms compare to the performance of DOCS, there is a significant commercial opportunity for the development of DOCS/Enterprise. Every hospital will benefit from this staff scheduling system. Savings are $200,000 per year for a typical hospital.