TY - JOUR
T1 - Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm
AU - Hou, Yan
AU - Wu, Nai Qi
AU - Zhou, Meng Chu
AU - Li, Zhi Wu
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61273036, and in part by the Fundo para o Desenvolvimento das Ciencias e da Tecnologia of Macau under Grants 065/2013/A2 and 066/2013/A2.
Publisher Copyright:
© 2013 IEEE.
PY - 2017/3
Y1 - 2017/3
N2 - With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.
AB - With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.
KW - Crude oil operations
KW - discrete-event and hybrid system
KW - genetic algorithm (GA)
KW - short-term scheduling
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U2 - 10.1109/TSMC.2015.2507161
DO - 10.1109/TSMC.2015.2507161
M3 - Article
AN - SCOPUS:85014639386
SN - 2168-2216
VL - 47
SP - 517
EP - 530
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 3
M1 - 7366610
ER -