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Genetic algorithm for scheduling of laboratory personnel.
1Department of Pathology, University of Virginia Health System, PO Box 800214, Charlottesville, VA 22908, USA. jboyd@virginia.edu
Clinical Chemistry
|January 10, 2001
Summary
A genetic algorithm effectively schedules laboratory personnel, ensuring all workstations are covered and staff skills are maintained through rotation. This approach optimizes staffing and identifies training needs in technical work environments.
Area of Science:
- Laboratory Management
- Operations Research
- Computational Biology
Background:
- Effective staffing of core laboratories requires systematic scheduling to ensure workstation coverage and skill maintenance.
- Periodic exercise of all worker skills is crucial for maintaining staff competence in specialized roles.
Purpose of the Study:
- To develop and evaluate a genetic algorithm for optimizing laboratory personnel scheduling.
- To ensure appropriate workstation coverage and regular utilization of diverse staff skills.
Main Methods:
- A genetic algorithm was developed using Visual Basic 4.0 to schedule laboratory personnel.
- The algorithm maximizes a fitness function evaluating the match between personnel, skills, and work tasks for specific shifts.
- User inputs include work tasks, personnel availability, skills, shift details, and scheduling parameters via an Excel spreadsheet.
Main Results:
- The program successfully matched qualified individuals to tasks and maintained skills through job rotation for over 22 months.
- Generated schedules enabled advance anticipation of staffing limitations, facilitating proactive adjustments.
- Identified specific skills lacking sufficient trained personnel, aiding targeted training initiatives.
- Resulted in an estimated annual saving of approximately $11,000 by reducing supervisory time spent on schedule development.
Conclusions:
- Genetic algorithms provide a valuable tool for scheduling in complex, technical work environments with multiskilled employees.
- The implemented system demonstrated practical utility and staff acceptance in a large university medical center's clinical laboratories.
