Hands-On: Optimization Modeling for Maintenance Scheduling in the Electric Power Industry

Efficient maintenance scheduling is a pivotal challenge across various industries, from electric power to manufacturing and transportation. It involves scheduling maintenance activities to keep a high level of operational availability. Good maintenance schedules match maintenance windows with times of reduced demand to ensure that systems continue to run smoothly.

It seems counterintuitive, but taking assets offline for maintenance actually ensures they stay online longer. Scheduling maintenance during low-demand periods prevents breakdowns. This approach can be applied to your industry as well.

How does Optimization help with Maintenance Scheduling?

Consider yourself in charge of managing 15 power plants that operate continuously throughout the year. Each generating unit can be in one of three states during a particular week: ON, OFF, or MAINTENANCE. The challenge is to schedule maintenance activities in a way that minimizes total operating costs while meeting power demand and adhering to maintenance constraints.

• Active Power Plants: Generate power and incur operational costs.
• Maintenance Units: Undergo preventive maintenance and incur maintenance costs.
• Inactive Plants: Not in use and incur no costs.

The objective is to create an optimal maintenance schedule that meets the following requirements:

• Satisfy the power demand for each week (Capacity Planning)
• Adhere to maintenance duration and timing constraints (Maintenance Window)
• Not all combinations of units can be maintained simultaneously (Operational Constraints)
• Minimize total operational and maintenance costs (Cost Optimization)

This tutorial equips power plant managers, operation planners, and industry professionals to use Quantagonia’s HybridSolver for optimal maintenance scheduling.

How to Implement Mixed Integer Linear Programming in Python

Implementing this optimization problem in Python involves several steps:

1. Define Problem Parameters (e.g., Costs and Capacities)
2. Define Objective Function (e.g., Minimize Operating Costs)
3. Define Constraint Functions (e.g., Demand and Maintenance Constraints)
4. Run the Model (with HybridSolver)
5. Process the Results

Following this structured approach ensures clarity and makes it easier to understand and modify the code in the future.

Code Implementation: Optimizing Maintenance Scheduling for Power Plants

At its core, we tackle a maintenance scheduling challenge using principles from Operations Research. This involves optimizing the schedule for maintenance activities while ensuring the power demand is met. The goal is to minimize total costs and ensure a stable power supply by solving several well-known optimization problems:

• Maintenance Scheduling (Scheduling Problem)
• Power Demand Fulfillment (Resource Allocation Problem)
• Maintenance Constraints (Constraint Satisfaction Problem)

In combination, the algorithm solves for an optimal maintenance plan that meets demand while minimizing costs within a Mixed-Integer Linear Programming (MILP) framework.

Next, we utilize the HybridSolver API to determine the most efficient maintenance schedule.

The solution ensures that power demand is met, and maintenance activities are optimally scheduled to minimize costs. This approach is scalable and can handle more complex scenarios with additional constraints and a larger number of units condition-based CBM and additional FMEA insights from effect analysis. Try it out for yourself. Here is the code implementation of the tutorial.