Dynamic multivariate optimisation model based mobile-app using autoML machine learning algorithm made for shift workers in retail

轮班管理是复杂的，受许多约束和相互依赖关系的影响。从NHS到零售、军事、政府和许多其他部门，轮班工作在各行各业都有应用。目前的解决方案只提供在日历上放置班次的工具；它们不直接解决复杂性，而是依赖于人为干预来提供决策和管理组件，例如移动轮班。我们打算通过应用线性优化模型来解决轮班工作模式，类似于如何使用旅行推销员问题来解决销售代表的旅行距离，从而大大减少碳足迹和雇主的成本，我们的项目将创建软件，结合数学方法，是公正的，消除潜意识偏见，使用机器学习算法（autoML）和实时数据来动态更新线性优化模型。使人工智能生成的解决方案能够解决轮班调度问题。因此，该项目将使软件能够取代excel和其他涉及数千人的基本直觉轮岗时间表，使软件流程能够交互以请求轮班更改，更新工时，使更多或更少的可用时间，切换班次，并为最佳业务结果部署最佳员工组合。用人工智能生成的解决方案取代负责排班的人，将类似于取消出租车调度员，就像优步（Uber）利用技术所做的那样。项目的预期效益包括更高的效率和性能；降低管理费用;减少压力和焦虑；改善对客户的服务，从而提高声誉；增加公平（例如，消除潜意识的偏见）。

Shift management is complex and is subject to many constraints and interdependencies. Shift working is utilised across industries as large as the NHS to the retail, military, government, and many other sectors. Current solutions only provide tools to place shifts on calendars; they do not address the complexities directly, instead relying on human intervention to provide the decision-making and management components e.g. moving a shift around.We intend to solve for shift working patterns by applying a linear optimisation model, similar to how the travelling salesman problem was used to solve distance travelled by a sales rep and hence massively reduce carbon footprint and cost to the employer, our project will create software that combines a mathematical approach that is impartial, eliminates subconscious biases, uses machine learning algorithms (autoML) and live data to dynamically update the linear optimisation model, enabling AI-generated solutions to the shift scheduling problem.The project will thus enable software to replace excel and other rudimentary gut feel rota schedules where thousands of people are involved, enable software processes to interact to request shift changes, update hours, make more or fewer hours available, switch shifts, and deploy the optimal staff combinations for the best business outcomes.Replacing the person responsible for shift scheduling with an AI-generated solutions will be akin to removing the taxi dispatcher, the way that Uber has done with technology. Anticipated benefits from the project include greater efficiency and performance; reduced overheads; reduced stress and anxiety; improved service for customers and therefore improvements in reputation; and increased fairness (eliminating subconscious bias, for example).