A Multi-Objective Genetic Algorithm for Healthcare Workforce Scheduling

2025-08-28

AI Summary

Healthcare workforce scheduling must jointly optimize labor costs, patient coverage, and staff satisfaction—challenged by conflicting objectives and dynamic demand fluctuations. This paper proposes an intelligent scheduling model based on a multi-objective genetic algorithm (MOO-GA), which innovatively integrates hourly appointment demand forecasting with a modular, multi-skill shift-rotation mechanism. A composite objective function is formulated to simultaneously minimize labor cost, satisfy coverage constraints, and maximize staff preference satisfaction, yielding a Pareto-optimal solution set. Evaluated on real-world clinical department data, the model achieves a 66% average improvement in综合 performance over manual scheduling baselines. It significantly enhances schedule robustness, fairness, and implementability. The framework provides a scalable, intelligent decision-support tool for lean healthcare resource allocation.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Workforce scheduling in the healthcare sector is a significant operational challenge, characterized by fluctuating patient loads, diverse clinical skills, and the critical need to control labor costs while upholding high standards of patient care. This problem is inherently multi-objective, demanding a delicate balance between competing goals: minimizing payroll, ensuring adequate staffing for patient needs, and accommodating staff preferences to mitigate burnout. We propose a Multi-objective Genetic Algorithm (MOO-GA) that models the hospital unit workforce scheduling problem as a multi-objective optimization task. Our model incorporates real-world complexities, including hourly appointment-driven demand and the use of modular shifts for a multi-skilled workforce. By defining objective functions for cost, patient care coverage, and staff satisfaction, the GA navigates the vast search space to identify a set of high-quality, non-dominated solutions. Demonstrated on datasets representing a typical hospital unit, the results show that our MOO-GA generates robust and balanced schedules. On average, the schedules produced by our algorithm showed a 66% performance improvement over a baseline that simulates a conventional, manual scheduling process. This approach effectively manages trade-offs between critical operational and staff-centric objectives, providing a practical decision support tool for nurse managers and hospital administrators.

Problem

• Optimizing healthcare workforce scheduling with conflicting objectives
• Balancing labor costs, patient care coverage, and staff satisfaction
• Addressing fluctuating demand and multi-skilled staff constraints

Innovation

• Multi-objective Genetic Algorithm for scheduling
• Incorporates hourly demand and modular shifts
• Optimizes cost, coverage, and staff satisfaction