具有任务选择和并行任务分配的人员任务调度问题的自适应大邻域搜索
Adaptive large neighborhood search for a personnel task scheduling problem with task selection and parallel task assignments
摘要：
出于现实世界的应用程序，我们建模和解决一个复杂的员工调度问题。任务分配给工人进行监督。多个任务可以由一个工人并行完成，工人轮班在可用性范围内灵活。每个工人都有不同的技能，使他们能够完成不同的任务。任务需要根据优先级和技能要求进行分配。其目标是最大化分配任务的数量加权其优先级，同时最小化分配惩罚。我们开发了一个自适应大邻域搜索（ALNS）算法，依赖于定制的破坏和修复算子。它是来自真实世界的数据，并通过商业MIP求解器获得的最佳结果相比，基准实例进行测试。此外，我们分析了考虑三个额外的替代目标函数的影响。当应用到大规模的公司数据，开发的ALNS优于以前应用的解决方案的方法。
英文摘要：
Motivated by a real-world application, we model and solve a complex staff scheduling problem. Tasks are to be assigned to workers for supervision. Multiple tasks can be covered in parallel by a single worker, with worker shifts being flexible within availabilities. Each worker has a different skill set, enabling them to cover different tasks. Tasks require assignment according to priority and skill requirements. The objective is to maximize the number of assigned tasks weighted by their priorities, while minimizing assignment penalties. We develop an adaptive large neighborhood search (ALNS) algorithm, relying on tailored destroy and repair operators. It is tested on benchmark instances derived from real-world data and compared to optimal results obtained by means of a commercial MIP-solver. Furthermore, we analyze the impact of considering three additional alternative objective functions. When applied to large-scale company data, the developed ALNS outperforms the previously applied solution approach.
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