Title | ||
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Quantum-Inspired Genetic Programming Algorithm for the Crude Oil Scheduling of a Real-World Refinery |
Abstract | ||
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Refinery scheduling comprises a group of decisions that aims to optimize asset allocation, activity sequencing, and the time-related realization of those activities. This scheduling must achieve multiple objectives while considering different types of constraints. Uninterrupted processing unit operation, on-time crude oil batch receipts, and tank switchover minimization coexist in the everyday reasoning of a scheduler. However, it is not usual that works encompassing many operational aspects, such as multiple operational objectives, settling time, and an unlimited number of crudes, to blend in any tank. This article proposes a new algorithm that integrates linear and grammar-guided genetic programming concepts with a quantum-inspired approach to create programs that represent a crude oil refinery scheduling solution. The fitness function comprises four objectives that guide the evolution based on importance predefined by the decision maker. We propose a success ratio to evaluate the algorithm performance considering 50 runs for each case. A final solution is considered a success if two more important objectives are optimized. We assessed our approach with five different scenarios of a real refinery and three of them achieved a 100% success ratio. |
Year | DOI | Venue |
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2020 | 10.1109/JSYST.2020.2968039 | IEEE Systems Journal |
Keywords | DocType | Volume |
Crude oil scheduling,domain-specific language,genetic programming (GP),grammar-guided linear GP,quantum-inspired GP,refinery scheduling | Journal | 14 |
Issue | ISSN | Citations |
3 | 1932-8184 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Cristiane Salgado Pereira | 1 | 0 | 0.34 |
Douglas Mota Dias | 2 | 24 | 7.18 |
Marco Aurélio Cavalcanti Pacheco | 3 | 143 | 22.29 |
Marley M. B. Rebuzzi Vellasco | 4 | 0 | 0.34 |
André Vargas Abs da Cruz | 5 | 29 | 5.91 |
Estefane Horn Hollmann | 6 | 0 | 0.34 |