Constrained multi-objective optimization of short-term crude oil scheduling with dual pipelines and charging tank maintenance requirement

For the short-term crude oil scheduling problem, it is difficult to guarantee the feasibility of a schedule due to complicated constraints. Meanwhile, uncertainty is a very important concern in refineries, such as unexpected breakdown of charging tanks. Therefore, it is a great challenge to make a s...

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Vydáno v:Information sciences Ročník 588; s. 381 - 404
Hlavní autoři: Hou, Yan, Zhang, YiXian, Wu, NaiQi, Zhu, QingHua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.04.2022
Témata:
Constrained multi-objective optimization
Crude oil operations
Heuristic strategy
Short-term scheduling
Crude oil operations
Constrained multi-objective optimization
Short-term scheduling
Heuristic strategy
ISSN:0020-0255, 1872-6291
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Shrnutí:For the short-term crude oil scheduling problem, it is difficult to guarantee the feasibility of a schedule due to complicated constraints. Meanwhile, uncertainty is a very important concern in refineries, such as unexpected breakdown of charging tanks. Therefore, it is a great challenge to make a schedule feasible. Most existing works on multi-objective optimization of short-term crude oil scheduling are developed for refineries processing low-fusion-point oil (L-oil) only and little is done for the case with dual pipelines for processing both L-oil and high-fusion-point oil (H-oil). With five objectives and many constraints, it is challenging for a metaheuristic algorithm to find a feasible schedule. To solve this problem, in this work, constraint violation is used to describe the degree of constraint violation. Thus, an adaptive enhanced selection pressure algorithm based on NSGA-II-CDP (NSGA-II-APE) is proposed to efficiently solve the problem for processing both L-oil and H-oil. This algorithm can effectively enhance the selection pressure in the later iterations. Industrial case problems are used to test the proposed method and compare its performance with 11 state-of-the-art constrained multi-objective evolution algorithms (CMOEAs). Results show its superiority over the existing ones in terms of convergence, solution diversity, and time efficiency.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.12.067