  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.
