Multi-Objective Maintenance Scheduling Optimization

This project implements a multi-objective optimization model using evolutionary algorithms to schedule maintenance of power generation units over multiple time intervals. The goal is to maximize system reserve margins while minimizing total maintenance costs, subject to operational and budgetary constraints.

🗓️ Created: March 2025 | 🎓 6th Semester, "My Genesis in AI: Optimizing Energy Maintenance with Genetic Algorithms"

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Key Features

• Evolutionary Optimization: Uses the NSGA-II algorithm from the pymoo library for solving multi-objective problems.
• Custom Sampling: Ensures that initial populations comply with constraints through multiprocessing-based sampling.
• Constraint Management: Enforces maintenance duration limits, reserve requirements, and cost budgets.
• Visualization: Plots the Pareto front to illustrate the trade-offs between objectives.

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How It Works

Problem Description

You have N generating units, each with:

• A maximum capacity (MW)
• Maintenance costs per time interval
• Constraints on how long they can undergo maintenance

You also have:

• A power reserve demand per time interval
• A limited maintenance budget

The goal is to schedule which units undergo maintenance during which time intervals, such that:

1. Minimum reserve across all intervals is maximized.
2. Total maintenance cost is minimized.
3. Constraints on unit operation and cost are satisfied.

Objectives

1. Maximize: Minimum reserve margin across all time intervals
2. Minimize: Total maintenance cost

Constraints

• Each unit must be in maintenance for 1 or 2 intervals, depending on its type.
• Reserve margin must meet or exceed the demand in each interval.
• The total maintenance cost must stay within the defined budget.

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🔧 Technologies Used

Technology	Purpose
Python	Core programming language
pymoo	Multi-objective optimization library
NumPy	Array manipulation and numerical operations
matplotlib / pymoo.visualization	Visualization of Pareto front
multiprocessing	Parallel sampling of valid solutions

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Algorithm and Components

NSGA-II (Non-dominated Sorting Genetic Algorithm II)

• A robust multi-objective optimization algorithm
• Maintains a diverse Pareto front of optimal solutions
• Uses:
  • Single Point Crossover
  • Bitflip Mutation
  • Custom Sampling to enforce constraint-compliant individuals from the start

Custom Components

• Unit Class: Represents a generating unit and its characteristics
• Constraint Functions:
  • maintenance_duration_violation
  • net_reserve_violation
  • maintenance_cost_violation
• Custom Sampling: Generates only valid solutions using constraint checks before fitness evaluation
• Objective Functions:
  • net_reserve_per_interval
  • total_maintenance_cost

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Output Example

Each solution includes:

• Binary scheduling string (unit × interval)
• Violations summary (duration, reserve, cost)
• Objective values (reserve margin, maintenance cost)

Sample output:

Solution 1: 010001011001...
  Duration Violations: 0, Cost Violations : 0, Reserve Violations: 0
  Reserve: 90, Cost: 745