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Turkish Journal of Electrical Engineering and Computer Sciences

Author ORCID Identifier

KEREM MANALP: 0009-0003-8399-7546

ANSEL EROL: 0009-0000-3149-075X

KUTLUHAN EROL: 0000-0003-1816-5877

CEM EVRENDİLEK: 0000-0003-1204-2817

Abstract

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that the best-performing algorithm varies across problem instances, we propose an algorithm selection approach based on a gradient boosting classifier that achieves 89\% of the performance gap between the virtual best solver and the single best solver.

DOI

10.55730/1300-0632.4191

Keywords

Combinatorial optimization, routing, metaheuristics, algorithm selection, machine learning

First Page

561

Last Page

583

Publisher

The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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