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

TL;DR

This paper presents an adaptive large neighborhood search algorithm for a complex personnel scheduling problem involving task selection, skill matching, and parallel task assignments, aiming to maximize task coverage and minimize penalties.

Contribution

The paper introduces a novel ALNS algorithm tailored for a multi-faceted staff scheduling problem with task prioritization and skill considerations, outperforming previous methods on real-world data.

Findings

ALNS outperforms previous solution approaches on large-scale data.

The algorithm effectively handles multiple objectives and constraints.

Benchmark tests show near-optimal solutions with improved efficiency.

Abstract

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.