https://github.com/pegah-ardehkhani/multi-objective-energy-aware-job-shop-scheduling-with-nsga-ii
Multi-objective job shop scheduling system using NSGA-II algorithm to optimize makespan, energy costs, and tardiness simultaneously. Features energy-aware scheduling with time-of-use tariffs and Pareto front visualization
https://github.com/pegah-ardehkhani/multi-objective-energy-aware-job-shop-scheduling-with-nsga-ii
energy-aware energy-aware-scheduler energy-efficiency energyefficiency evolutionary-algorithm gantt-chart job-shop-scheduling-problem jobshop-scheduling manufacturing metaheuristic multiobjective-optimization nsga-ii operations-research optimization pareto-front production production-planning scheduling-algorithm sustainability sustainable-manufacturing
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Multi-objective job shop scheduling system using NSGA-II algorithm to optimize makespan, energy costs, and tardiness simultaneously. Features energy-aware scheduling with time-of-use tariffs and Pareto front visualization
- Host: GitHub
- URL: https://github.com/pegah-ardehkhani/multi-objective-energy-aware-job-shop-scheduling-with-nsga-ii
- Owner: Pegah-Ardehkhani
- License: mit
- Created: 2025-07-03T11:12:15.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-03T12:26:12.000Z (3 months ago)
- Last Synced: 2025-07-03T13:19:46.555Z (3 months ago)
- Topics: energy-aware, energy-aware-scheduler, energy-efficiency, energyefficiency, evolutionary-algorithm, gantt-chart, job-shop-scheduling-problem, jobshop-scheduling, manufacturing, metaheuristic, multiobjective-optimization, nsga-ii, operations-research, optimization, pareto-front, production, production-planning, scheduling-algorithm, sustainability, sustainable-manufacturing
- Language: Jupyter Notebook
- Homepage:
- Size: 273 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Energy-Aware Job Shop Scheduling with NSGA-II 🏭  
[](https://nbviewer.org/github/Pegah-Ardehkhani/Multi-Objective-Energy-Aware-Job-Shop-Scheduling-with-NSGA-II/blob/main/Multi-Objective%20Energy-Aware%20Job%20Shop%20Scheduling%20with%20NSGA-II.ipynb)
A multi-objective optimization system for job shop scheduling that simultaneously minimizes makespan, energy costs, and tardiness using the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm.
## 🚀 Features
- **Multi-objective optimization** with three objectives:
- **Makespan**: Total completion time
- **Energy Cost**: Time-of-use electricity costs
- **Tardiness**: Penalty for late job completion
- **Energy-aware scheduling** with configurable tariff periods
- **Machine-specific energy profiles** (idle vs. working power consumption)
- **Flexible job shop configuration** with precedence constraints
- **Pareto front visualization** in 2D and 3D
- **Gantt chart generation** for schedule visualization
## 📋 Requirements
```
numpy
matplotlib
```
## 🔧 Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/energy-aware-job-shop-scheduling.git
cd energy-aware-job-shop-scheduling
```
2. Install required dependencies:
```bash
pip install numpy matplotlib
```
## 🏃♂️ Quick Start
```python
from nsga2_scheduler import *
# Define machines with energy profiles
machines = [
Machine("M1", idle_power=0.1, working_power=1.0),
Machine("M2", idle_power=0.15, working_power=1.5),
Machine("M3", idle_power=0.08, working_power=0.8),
]
# Define operations and jobs
op1_1 = Operation("Op1.1", "J1", "M1", 5, 1)
op1_2 = Operation("Op1.2", "J1", "M2", 3, 2)
job1 = Job("J1", [op1_1, op1_2], due_date=10)
# Define energy cost model
energy_tariffs = {
"off_peak": (0, 7, 0.10),
"peak": (7, 17, 0.25),
"off_peak_evening": (17, 24, 0.15),
}
energy_model = EnergyCostModel(energy_tariffs)
# Create and run scheduler
scheduler = NSGA2Scheduler(
jobs=[job1],
machines=machines,
energy_model=energy_model,
population_size=100,
generations=300
)
results = scheduler.run()
# Visualize results
plot_pareto_front(results["pareto_front"])
```
## 📊 Core Components
### 1. Operation
Represents a single manufacturing operation with:
- Processing time and machine assignment
- Energy consumption calculation
- Precedence constraints within jobs
### 2. Job
Collection of operations with:
- Sequential execution requirements
- Due date constraints
- Release time specifications
### 3. Machine
Manufacturing resource with:
- Configurable energy profiles (idle/working power)
- Availability tracking
- Schedule management
### 4. Energy Cost Model
Time-of-use electricity pricing with:
- Multiple tariff periods
- Hourly cost calculation
- Dynamic pricing support
### 5. NSGA-II Scheduler
Multi-objective evolutionary algorithm featuring:
- **Population initialization** with diverse strategies
- **Fast non-dominated sorting** for Pareto ranking
- **Crowding distance** for diversity preservation
- **Tournament selection** with elitism
- **Order crossover (OX1)** for permutation chromosomes
- **Multi-strategy mutation** (swap, insertion, inversion)
## 🎯 Algorithm Overview
The NSGA-II algorithm works by:
1. **Encoding**: Jobs represented as permutation chromosomes
2. **Decoding**: Converting chromosomes to feasible schedules
3. **Evaluation**: Calculating makespan, energy cost, and tardiness
4. **Selection**: Multi-objective tournament selection
5. **Reproduction**: Order crossover and mutation operators
6. **Replacement**: Elitist selection maintaining population diversity
## 📈 Visualization
### Pareto Front Plotting
- 3D scatter plot showing all three objectives
- 2D projections for pairwise objective analysis
- Color-coded solutions for easy interpretation
### Gantt Charts
- Machine-based timeline visualization
- Job-specific color coding
- Operation details and timing
## ⚙️ Configuration
### Population Parameters
```python
NSGA2Scheduler(
population_size=150, # Population size
generations=500, # Number of generations
crossover_rate=0.9, # Crossover probability
mutation_rate=0.2 # Mutation probability
)
```
### Energy Tariffs
```python
energy_tariffs = {
"night": (0, 6, 0.08), # (start_hour, end_hour, cost_per_unit)
"morning": (6, 12, 0.20),
"afternoon": (12, 18, 0.15),
"evening": (18, 24, 0.25),
}
```
## 📋 Example Output
```
--- NSGA-II Optimization Complete ---
Pareto Front Size: 12
--- Pareto Front Solutions ---
Solution 1:
Makespan: 18
Energy Cost: 4.25
Tardiness: 2
Solution 2:
Makespan: 20
Energy Cost: 3.80
Tardiness: 0
Best individual objectives:
Best Makespan: 18
Best Energy Cost: 3.80
Best Tardiness: 0
```
## 🔬 Research Applications
This implementation is suitable for:
- **Manufacturing scheduling** with energy constraints
- **Sustainable production** planning
- **Multi-objective optimization** research
- **Smart factory** implementations
- **Energy cost reduction** studies
## 🛠️ Customization
### Adding New Objectives
Extend the `_decode_chromosome` method to calculate additional objectives:
```python
def _decode_chromosome(self, chromosome):
# ... existing code ...
new_objective = calculate_new_objective(scheduled_operations)
return scheduled_operations, makespan, energy_cost, tardiness, new_objective
```
### Custom Machine Types
Create specialized machine classes:
```python
class AdvancedMachine(Machine):
def __init__(self, machine_id, setup_time=0, maintenance_cost=0):
super().__init__(machine_id)
self.setup_time = setup_time
self.maintenance_cost = maintenance_cost
```
## 📚 References
- Deb, K., et al. (2002). "A fast and elitist multiobjective genetic algorithm: NSGA-II"
- Pinedo, M. (2016). "Scheduling: Theory, Algorithms, and Systems"
- Zhang, R., & Chiong, R. (2016). "Solving the energy-efficient job shop scheduling problem"
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
**Keywords**: Job Shop Scheduling, Multi-objective Optimization, NSGA-II, Energy Efficiency, Manufacturing, Evolutionary Algorithm, Pareto Front, Production Planning