Optimizing nuclear fuel disposal: A multiobjective approach for sustainable solutions

The disposal of nuclear fuel is a critical issue in nuclear energy production, requiring solutions that balance various conflicting objectives such as cost, time, safety, and environmental impact. In this paper, a multi-objective optimization approach is proposed to address these challenges effectively. Specifically, it is utilized the Genetic Algorithm (GA) to optimize the nuclear fuel disposal process by considering multiple criteria simultaneously. The approach aims to find Pareto-optimal solutions, where no objective can be improved without degrading another. It is first presented a mathematical model that incorporates the objectives of minimizing cost, ensuring safety, reducing environmental impact, and optimizing disposal time. The model is then solved using a Non-dominated Sorting Genetic Algorithm II (NSGAII), a widely used evolutionary algorithm for multi-objective problems. The performance of the algorithm is evaluated through several scenarios, including cost minimization, safety and environmental impact prioritization, and a balanced approach where all objectives are considered equally important. Simulation results demonstrate the effectiveness of the GA in finding optimal solutions for different trade-offs between the objectives, with the main finding highlighting the algorithm's ability to balance cost, safety, and environmental concerns in the disposal process. The Pareto front, which represents the set of Pareto-optimal solutions, reveals that solutions with low cost and time can be achieved at the expense of higher environmental impact, while safety can be maintained at an optimal level. The proposed method offers decision-makers valuable insights into the best possible strategies for nuclear fuel disposal while meeting the various regulatory, environmental, and financial constraints. This study provides a novel approach to nuclear fuel disposal optimization, offering a tool for policymakers and industry professionals to make informed decisions that ensure both operational efficiency and sustainability in nuclear energy management.