A Multi-Objective Decomposition Optimization Method for Refinery Crude Oil Scheduling through Genetic Programming

Pereira, Cristiane Salgado; Dias, Douglas Mota; Martí, Luis; Vellasco, Marley

doi:10.1145/3583133.3596313

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fact that the SCOO problem includes both discrete events and continuous processes makes it an interesting problem in terms of both theoretical research and practical application [3]. The combinatorial structure of this problem causes the high complexity of the problem [4] and allows for each decision to create a different probability tree, and therefore, this makes the problem an NP-Hard problem [5].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Heuristic Optimization for Complex Large-Scale Crude Oil Operation Scheduling

Güleç,

Kabak

2024

Processes

View full text Add to dashboard Cite

This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach.

show abstract

Section: Introductionmentioning

confidence: 99%