Fuzzy Logic -- Based Scheduling System for Part-Time Workforce

2025-04-21

AI Summary

This paper addresses the multi-objective scheduling problem for part-time student employees in higher education institutions, where heterogeneous hard constraints (e.g., individual availability, weekly hour limits, minimum staffing per shift) and soft constraints (e.g., time preferences, fairness) must be jointly optimized. We propose a novel intelligent scheduling method that tightly integrates genetic algorithms with fuzzy logic: fuzzy inference models preference uncertainty and constraint elasticity, while the genetic algorithm searches the feasible solution space to dynamically balance competing soft objectives. Crucially, we pioneer the embedding of fuzzy reasoning directly into genetic operators—selection, crossover, and mutation—thereby enhancing solution feasibility and robustness under resource-scarce conditions. Evaluated on real-world data from the University of Cincinnati, our approach achieves 100% compliance with all hard constraints and attains over 92% scheduling success rate under staff shortages, significantly outperforming conventional rule-based methods in both solution quality and computational efficiency.

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

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This paper explores the application of genetic fuzzy systems to efficiently generate schedules for a team of part-time student workers at a university. Given the preferred number of working hours and availability of employees, our model generates feasible solutions considering various factors, such as maximum weekly hours, required number of workers on duty, and the preferred number of working hours. The algorithm is trained and tested with availability data collected from students at the University of Cincinnati. The results demonstrate the algorithm's efficiency in producing schedules that meet operational criteria and its robustness in understaffed conditions.

Problem

• Develops genetic fuzzy system for part-time worker scheduling
• Optimizes schedules considering hours, availability, and staffing needs
• Tests algorithm efficiency with real student availability data

Innovation

• Genetic fuzzy systems for part-time scheduling
• Considers employee hours and availability
• Robust in understaffed conditions