Development of the new business intelligent employee scheduling solution in complex service operations

GaiaDigits专注于零售、制造、酒店和其他现代服务业的劳动力管理解决方案。本研究旨在与GaiaDigits合作开发基于服务业劳动力调度系统鲁棒优化的数学规划模型和算法。劳动力调度是服务行业中非常重要的操作组件，例如，餐馆零售店在服务业中设计劳动力调度系统存在困难。首先，由于劳动力规模和规划范围的长度，问题规模可能相当大。其次，劳动力调度系统的不确定性可能很大。这些不确定性包括需求不确定性和员工不确定性。需求不确定性是指工作人员应提供的可变服务时间。工作人员的不确定性是指工作人员可以提供的可变服务时间。可用的员工服务时间可能是不稳定的，由于不断变化的时间表，缺席等，在这个程序中，我们将使用强大的优化方法来设计劳动力调度系统。鲁棒优化方法可以利用历史数据构造一个不确定性集合来表示不确定性，同时计算效率比随机规划方法高。本研究的目标包括：1）开发一个强大的基于优化的解决方案的方法，劳动力调度; 2）开发一个软件原型，这种方法。一旦这个项目完成，我们可以为服务行业带来更好，更有效的员工计划工具。

GaiaDigits focus on Workforce Management Solution in retail, manufacturing, hospitality, and other modern services. This research seeks to develop mathematical programming models and algorithms based on robust optimization for work force scheduling systems for service industry in collaboration with GaiaDigits. Workforce scheduling is a very important operational component within the service industry, e.g., restaurant, retail. There are difficulties in designing workforce scheduling systems in service industry. First, the problem scale could be quite large due to the workforce size and planning horizon length. Second, the uncertainties in the workforce scheduling systems could be large. These uncertainties include demand uncertainty and staff uncertainty. Demand uncertainty refers to the variable service hours that should be provided by the staff. Staff uncertainty refers to the variable service hours that could be provided by the staff. The available staff service hours could be volatile due to changing schedules, absence, etc. In this program, we will use the robust optimization approach to design the workforce scheduling systems. The robust optimization approach, can use historical data to construct an uncertainty set to demonstrate uncertainties, while is more computationally efficient than stochastic program. The objectives of this research include: 1) developing a robust optimization based solution methodology for workforce scheduling; 2) developing a software prototype for this methodology. Once this project is finished, we can bring a better and more effective Employee Schedule tool to the service industry.