How porhao/harnessing-genetic-algorithms-to-optimize-scheduling-challenges-in-workforce-management works
This is a small command-line workforce scheduler. The user provides employee names and genders; the program creates a week of shifts, evolves candidate schedules using a genetic algorithm, prints the best schedule, and visualizes employee shift counts. The implementation is contained in project.py; the graph separates its main workflow, optimization stages, and reporting. The README documents the same capabilities, but no additional source modules are present.
Main entry points: project.py Main Workflow
Components 14
User Workflow
- Main Workflow
project.py - Collect Inputs
project.py
Genetic Optimization
- Workforce Scheduler
project.py - Initial Population
project.py - Fitness Constraints
project.py - Population Ranking
project.py - Parent Selection
project.py - Population Breeding
project.py - Crossover and Mutation
project.py
Schedule Reporting
- Shift Counting
project.py - Shift Visualization
project.py
Other components
- User
- Terminal
- Matplotlib
Connections 15
- User → Collect Inputs: provides details
- Main Workflow → Collect Inputs: collects inputs
- Collect Inputs → Main Workflow: returns employees and shifts
- Main Workflow → Workforce Scheduler: runs optimization
- Workforce Scheduler → Initial Population: creates population
- Workforce Scheduler → Population Ranking: ranks generations
- Workforce Scheduler → Parent Selection: selects parents
- Workforce Scheduler → Population Breeding: breeds population
- Population Ranking → Fitness Constraints: scores schedules
- Population Breeding → Crossover and Mutation: creates offspring
- Workforce Scheduler → Main Workflow: returns best schedule
- Main Workflow → Terminal: prints schedule
- Main Workflow → Shift Counting: counts assignments
- Main Workflow → Shift Visualization: plots shift counts
- Shift Visualization → Matplotlib: renders chart