文献类型：期刊文献

英文题名：Large-Scale Continuous-Time Crude Oil Scheduling: A Variable-Length Evolutionary Optimization Approach

作者：Zhang, Wanting[1];Du, Wenli[1];Du, Wei[1];He, Renchu[1];Jin, Yaochu[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Westlake Univ, Sch Engn, Hangzhou 310030, Peoples R China

年份：2025

卷号：22

起止页码：2526

外文期刊名：IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

收录：;EI(收录号：20241515881435);WOS:【SCI-EXPANDED(收录号:WOS:001415310800007)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Basic Science Center Program) under Grant 61988101, in part by the National Natural Science Foundation of China (Key Program) under Grant 62136003, in part by the National Natural Science Foundation of China under Grant 62173144, in part by the Shanghai Rising-Star Program under Grant 22QA1402400, and in part by the Major Science and Technology Projects of Longmen Laboratory under Grant LMZDXM202206.

语种：英文

外文关键词：Large-scale crude oil scheduling; continuous-time model; variable-length representation; evolutionary algorithm

摘要：Evolutionary algorithms (EAs) have significantly contributed to addressing large-scale crude oil scheduling problems (COSPs) that exceed the capabilities of mathematical programming. However, current research using EAs to resolve COSPs is limited to discrete-time models that assume uniform operation durations. While this simplification transforms COSPs into fixed-length optimization problems suitable for conventional EAs, it fails to adequately tackle the challenge posed by variable operation durations in COSPs. This limitation results in a compromised solution quality for EAs. To bridge the gap between large-scale COSPs and real-world refineries, this paper introduces the formulation of a continuous-time model for a large-scale COSP, accounting for varying operating durations and practical requirements. In an effort to enhance the effectiveness of EAs in optimizing COSPs represented in continuous time, we propose a variable-length constrained evolutionary algorithm (VLCEA). The variable-length encoding in VLCEA, tailored to the specific problem, facilitates the generation of high-quality solutions with variable lengths. This feature addresses the challenge of determining solution length in advance based on a priori knowledge using conventional methods. Additionally, VLCEA incorporates a sorting strategy and a niching-based selection to expedite the search for promising regions, aiming to achieve better solutions in less time. Experimental results on practical cases not only verify that the continuous-time model produces higher quality solutions compared to the discrete-time model but also demonstrate that VLCEA significantly outperforms five state-of-the-art large-scale EAs with fixed length when applied to the continuous-time model. Note to Practitioners-Crude oil scheduling is one of the most challenging problems in the refinery scheduling process. With the increasing size of scheduling tasks, traditional methods have become inefficient and inaccurate. To address this, evolutionary algorithms (EAs) have emerged as powerful tools for solving large-scale crude oil scheduling problems (COSPs). However, existing EA-based approaches often rely on a fixed number of decisions, resulting in suboptimal solutions when confronted with insufficient prior knowledge. To overcome these obstacles, we have developed a comprehensive continuous-time model specifically designed to address COSPs in real-world refineries. Additionally, we propose a variable-length constrained evolutionary algorithm (VLCEA) as an effective approach for resolving these complex scheduling problems. Computational results demonstrate that the VLCEA achieves superior performance compared to three state-of-the-art EAs in practical large-scale cases. Consequently, the proposed VLCEA exhibits significant potential for solving real-world refinery scheduling problems and can be extended to tackle other scheduling problems with an unfixed number of decisions.