Evaluating Optimization Algorithms for Dynamic Workforce Management: A Comparative Approach
Empirical benchmark of GA, PSO, ACO, SA, and Tabu Search in high-constraint emergency healthcare dispatch.
Published in IEEE CICTN '25 Proceedings in collaboration with Amity University & Amazon USA.
Effective workforce management is critical for optimizing operational efficiency in dynamic environments, particularly in healthcare settings. This paper presents a comparative analysis of five prominent optimization algorithms—Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Tabu Search (TS)—specifically applied to the problem of dynamic staffing needs in hospitals. Each algorithm is evaluated based on key performance metrics, including execution time, solution quality, scalability, and robustness. The study reveals that while GA and SA deliver high-quality solutions, ACO and TS are better suited for dynamic environments where conditions frequently change. PSO is recognized for its speed but may compromise on solution quality in more complex scenarios. The findings suggest that healthcare practitioners should consider hybrid approaches that combine the strengths of these algorithms to enhance workforce optimization.
Clinical Dataset: 10-Year Metropolitan Hospital Scope
Simulated 2014–2024 daily patient inflow across 6 departments: General Ward, ICU, ER, Pediatrics, Oncology, Cardiology.
Table I & II: Computational Complexity & Benchmark Metrics
Direct transcription from paper: evaluating algorithm complexity $O(\cdot)$ alongside execution time, solution quality, scalability, and robustness.
| Algorithm | Complexity | Execution Time | Solution Quality | Scalability | Robustness |
|---|---|---|---|---|---|
| Genetic Algorithm (GA) | O(I · N · C) | Moderate to High | High (Near-Optimal) | Moderate (Medium–Large) | High (Consistent) |
| Ant Colony (ACO) | O(I · N · E) | Moderate | Moderate to High | High (Dynamic Contexts) | Moderate (Sensitive) |
| Particle Swarm (PSO) | O(I · N) | Low to Moderate | Moderate to High | High (High-Dim Spaces) | Moderate (Premature) |
| Simulated Annealing (SA) | O(I) | Moderate to High | High (Local Optima Escape) | High (Escapes Optima) | High (Flexible) |
| Tabu Search (TS) | O(I · N) | Moderate | High (Adaptive Memory) | Moderate to High | High (Memory Guided) |
class HybridHospitalScheduler:
"""
Decoupled Metaheuristic Dispatch Engine
GA/SA for Global Initial Baselines + Tabu/ACO for Real-Time Shift Reallocation
"""
def __init__(self, departments, staffing_matrix, constraints):
self.departments = departments
self.matrix = staffing_matrix
self.constraints = constraints
self.tabu_memory = set()
def evaluate_schedule_fitness(self, schedule):
# Multi-objective penalty function:
# Patient safety margin + Overtime cost + Labor law compliance
overtime_penalty = sum(max(0, s.hours - 8) * 1.5 for s in schedule)
understaff_penalty = sum(s.uncovered_beds * 10.0 for s in schedule)
fatigue_penalty = sum(s.consecutive_shifts ** 2 for s in schedule)
return -(overtime_penalty + understaff_penalty + fatigue_penalty)
def reactive_tabu_dispatch(self, surge_event, current_schedule):
# Rapid localized neighborhood search avoiding visited topologies
best_candidate = None
for neighbor in self.generate_neighborhood(surge_event, current_schedule):
if neighbor.signature not in self.tabu_memory:
fitness = self.evaluate_schedule_fitness(neighbor)
if best_candidate is None or fitness > best_candidate.fitness:
best_candidate = neighbor
self.tabu_memory.add(best_candidate.signature)
return best_candidateKey Research Findings & Takeaways
- 01No single metaheuristic algorithm dominates all operational dimensions; hybrid pairing is mathematically essential.
- 02GA and SA excel at long-horizon baseline schedules due to superior global optima discovery.
- 03Tabu Search and ACO demonstrate superior convergence speed for dynamic, real-time shifts under stochastic patient surges.
- 04Public benchmark dataset with 10 years of simulated clinical data released on Kaggle to accelerate healthcare operations research.
Discuss Algorithmic Optimization
Consult with Aabhas on metaheuristics, high-constraint scheduling, and systems modeling.