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.

中文翻译：

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基于扩散模型的汽油混合调度多目标优化

汽油混合调度使用资源分配和操作顺序来满足炼油厂的生产要求。非线性、整数约束和大量决策变量的存在增加了该问题的复杂性，给传统算法和进化算法带来了挑战。本文介绍了一种由扩散模型（称为 DMO）驱动的新型多目标优化方法，该方法专为汽油混合调度而设计。为了解决整数约束并生成可行的时间表，扩散模型在高斯噪声和可行域之间创建多个中间分布。通过迭代过程，解决方案从高斯噪声过渡到可行的计划，同时使用梯度下降法优化目标。 DMO 同时实现目标优化和约束遵守。进行比较测试以评估 DMO 在不同尺度上的性能。实验结果表明，DMO 在解决汽油混合调度问题时在效率方面超越了最先进的多目标进化算法。