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Motivated by a real-world application, we model and solve a complex staffscheduling problem. Tasks are to be assigned to workers for supervision.Multiple tasks can be covered in parallel by a single worker, with workershifts being flexible within availabilities. Each worker has a different skillset, enabling them to cover different tasks. Tasks require assignment accordingto priority and skill requirements. The objective is to maximize the number ofassigned tasks weighted by their priorities, while minimizing assignmentpenalties. We develop an adaptive large neighborhood search (ALNS) algorithm,relying on tailored destroy and repair operators. It is tested on benchmarkinstances derived from real-world data and compared to optimal results obtainedby means of a commercial MIP-solver. Furthermore, we analyze the impact ofconsidering three additional alternative objective functions. When applied tolarge-scale company data, the developed ALNS outperforms the previously appliedsolution approach.

Adaptive large neighborhood search for a personnel task scheduling problem with task selection and parallel task assignments