Industrial Innovations

Industrial Innovations

Integrated Optimization of Routing and Scheduling for Nursing Services Considering Healthcare Operational Constraints: A case study in Kermanshah

Document Type : Original Article

Author
Babak Yousefi Yegane
Department of Industrial Engineering, Faculty of Engineering Management, Kermanshah University of Technology, Kermanshah, Iran.
10.61882/jii.3.1.66
Abstract
Nurse routing is a key challenge in healthcare operations management, requiring optimal patient-to-nurse assignment and precise scheduling of visits within specific time windows during which patients must receive care. Given the combinatorial complexity of the problem and the presence of strict temporal and capacity constraints, developing efficient and reliable solution approaches is of critical importance for real-world applicability.
In this study, a comprehensive mixed-integer programming (MIP) model is developed to simultaneously address both the assignment of patients to available nurses and the detailed scheduling of visits. The model fully respects hard time windows, nurse availability, and care delivery requirements. The exact solution of the model using the open-source solver GLPK within the Pyomo framework demonstrates acceptable performance for small-scale problem instances. However, the computational time escalates significantly as the problem size increases, limiting the practical usability of the exact method for large cases. To overcome this limitation, a learning-based simulated annealing (SA) algorithm is designed and implemented. By incorporating adaptive learning mechanisms, including reinforcement learning strategies such as Q-learning, this approach improves the efficiency and intelligence of the search process. The proposed metaheuristic enables the discovery of high-quality solutions within reasonable computation times, even for large-scale instances. Experimental results demonstrate that the learning-enhanced SA algorithm significantly outperforms exact methods in terms of both solution quality and computational efficiency. This research not only contributes to the advancement of optimization techniques in healthcare logistics but also highlights the practical value of hybrid metaheuristics in handling complex, real-world scenarios. The model was validated through a real case study in Kermanshah, Iran, and results indicate considerable improvements in resource utilization, nurse workload balance, and service timeliness.
Keywords
Nurse Routing Problem
Simulated Annealing
Learning Algorithms
Combinatorial Optimization
Patient Time Windows
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  • Receive Date 27 July 2025
  • Revise Date 04 November 2025
  • Accept Date 15 November 2025