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，并受到许多限制和相互依存关系。轮班工作被广泛应用于从NHS到零售，军事，政府和许多其他部门的行业。当前的解决方案仅提供在日历上安排班次的工具;他们不直接解决复杂性，而是依靠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旅行的距离，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，该软件结合了公正的数学方法，消除了潜意识的偏见，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AI-该项目将使软件能够取代Excel和其他基本的直觉轮值表，成千上万的人人工智能的参与，使软件流程能够进行交互，以请求轮班变化，更新小时数，增加或减少可用小时数，切换班次，并部署最佳员工组合，以实现最佳业务成果。用人工智能生成的解决方案取代负责轮班安排的人员将类似于取消出租车调度员，就像优步利用技术所做的那样。该项目的预期效益包括：提高效率和业绩;减少管理费用;减少压力和焦虑;改善对客户的服务，从而提高声誉;增加公平性（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).