# Matheuristics to optimize refueling and maintenance planning of nuclear power plants

## TL;DR

This paper develops and tests matheuristics combining MILP techniques and local search to optimize maintenance and refueling schedules for nuclear power plants over five-year periods, improving solution quality for large instances.

## Contribution

It introduces effective matheuristics for large-scale nuclear maintenance planning, demonstrating their ability to handle complex constraints and reduce over-costs associated with time window restrictions.

## Key findings

- Matheuristics are effective for medium and large instances.
- Time window restrictions cause significant over-costs.
- Matheuristics enable extension to bi-objective optimization.

## Abstract

Planning the maintenance of nuclear power plants is a complex optimization problem, involving a joint optimization of maintenance dates, fuel constraints and power production decisions. This paper investigates Mixed Integer Linear Programming (MILP) matheuristics for this problem, to tackle large size instances used in operations with a time scope of five years, and few restrictions with time window constraints for the latest maintenance operations. Several constructive matheuristics and a Variable Neighborhood Descent local search are designed. The matheuristics are shown to be accurately effective for medium and large size instances. The matheuristics give also results on the design of MILP formulations and neighborhoods for the problem. Contributions for the operational applications are also discussed. It is shown that the restriction of time windows, which was used to ease computations, induces large over-costs and that this restriction is not required anymore with the capabilities of matheuristics or local search to solve such size of instances. Our matheuristics can be extended to a bi-objective optimization extension with stability costs, for the monthly re-optimization of the maintenance planning in the real-life application.