Healthcare staff scheduling with work-life balance constraint using multi objective evolutionary algorithms
Computers and Industrial Engineering, cilt.220, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 220
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.cie.2026.112287
- Dergi Adı: Computers and Industrial Engineering
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, DIALNET, Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: Evolutionary algorithms, Multi-objective scheduling, NSGA-III, Personnel scheduling, Work-life balance
- Süleyman Demirel Üniversitesi Adresli: Evet
Özet
Healthcare staff scheduling is a complex combinatorial optimisation problem involving conflicting operational and workforce sustainability objectives. Traditional models typically enforce meeting managerial requirements with hard constraints while treating employee-related considerations such as fairness, workload balance, and rest regulations as secondary soft constraints. This modelling paradigm often leads to operationally feasible but socially inefficient schedules. This study proposes a Pareto-front multi-objective healthcare personnel scheduling model that integrates controlled capacity relaxation, fairness, contractual hour alignment, and ergonomic rest constraints within a unified optimisation framework. Managerial staffing demands are reformulated as soft constraints allowing ±1 flexibility under predefined conditions while strictly preventing idle shifts. Fairness and workload balance are explicitly enforced through variance-based threshold constraints. The problem is solved using a NSGA-III algorithm to effectively explore the high-dimensional objective space and generate a diverse set of Pareto-optimal schedules. Unlike weighted-sum approaches, the proposed method explicitly represents trade-offs between service quality and workforce sustainability. Computational experiments demonstrate that the proposed framework improves personal happiness level significantly while fairness distribution and workload stability without significantly compromising the quality of service. The results provide healthcare administrators with a structured decision-support tool for balancing operational efficiency and work–life balance objectives.