Diffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling
This is an incremental improvement for refinery operations, addressing a specific domain problem with enhanced optimization efficiency.
The paper tackles the complex gasoline blending scheduling problem with nonlinearity and integer constraints by introducing a diffusion model-based multiobjective optimization approach (DMO), which outperforms state-of-the-art evolutionary algorithms in efficiency across various scales.
Gasoline blending scheduling uses resource allocation and operation sequencing to meet a refinery's production requirements. The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms. This paper introduces a novel multiobjective optimization approach driven by a diffusion model (named DMO), which is designed specifically for gasoline blending scheduling. To address integer constraints and generate feasible schedules, the diffusion model creates multiple intermediate distributions between Gaussian noise and the feasible domain. Through iterative processes, the solutions transition from Gaussian noise to feasible schedules while optimizing the objectives using the gradient descent method. DMO achieves simultaneous objective optimization and constraint adherence. Comparative tests are conducted to evaluate DMO's performance across various scales. The experimental results demonstrate that DMO surpasses state-of-the-art multiobjective evolutionary algorithms in terms of efficiency when solving gasoline blending scheduling problems.