A Multi-objective Scheduling Optimization for Crude Oil Operations

doi:10.3969/j.issn.1007-7375.2020.04.017

Abstract

Abstract: To detail the schedules for crude oil operations, it is necessary to optimize multiple objectives. A dual population algorithm is proposed,being called bare bone particle swarm optimization and NSGA-II algorithm, BPGA, which is based on the improved bare bone particle swarm optimization algorithm (I-BBPSO) and non-dominated sorting genetic algorithm II (NSGA-II). It controls population exchange through Pareto difference entropy, which optimizes all the costs of the charging tanks, switching the charging tanks, mixing crude oil in the pipeline and mixing crude oil in the bottom of the tanks. The proposed algorithm is applied to an industrial example and compared with several representative evolutionary multi-objective algorithms and shows its feasibility and validity .

Key words: crude oil operations, high-dimensional multi-objective optimization, bare bone particle swarm optimization algorithm, NSGA-II, Pareto difference entropy

CLC Number:

TP301

HOU Yan, HUANG Kanghuan, ZHANG Yixian, WU Naiqi. A Multi-objective Scheduling Optimization for Crude Oil Operations[J]. Industrial Engineering Journal, 2020, 23(4): 131-139.

References

[1] FLOUDAS C A, LIN X X. Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review[J]. Computers & Chemical Engineering, 2004, 28(11): 2109-2129
[2] WU N Q, LI Z W, QU T. Energy efficiency optimization in scheduling crude oil operations of refinery based on linear programming[J]. Journal of Cleaner Production, 2017, 166: 49-57
[3] HAMISU A A, KABANTIOK S, WANG M. Refinery sche- duling of crude oil unloading with tank inventory management[J]. Computers & Chemical Engineering, 2013, 55: 134-147
[4] ZHANG S J, XU Q. Refinery continuous-time crude scheduling with consideration of long-distance pipeline transportation[J]. Computers & Chemical Engineering, 2015, 75: 74-94
[5] 陈旋. 离散和连续时间模型在原油调度问题中的应用研究[D]. 北京: 清华大学, 2012.
CHEN Xuan. Research of discrete and continuous time models for the crude oil scheduling problem[D]. Beijing: Tsinghua University, 2012.
[6] WU N Q, ZHOU M C, CHU F. A petri net-based heuristic algorithm for realizability of target refining schedule for oil refinery[J]. IEEE Transactions on Automation Science and Engineering, 2008, 5(4): 661-676
[7] 侯艳, 伍乃骐, 白丽平. 原油处理过程短期生产计划优化[J]. 工业工程, 2014, 17(3): 79-85
HOU Yan, WU Naiqi, BAI Liping. Optimization of short-term scheduling for crude oil operations[J]. Industrial Engineering Journal, 2014, 17(3): 79-85
[8] 李明. 炼油过程生产调度建模方法研究[D]. 济南: 山东大学, 2011.
LI Ming. Modeling method research on refinery process production scheduling[D]. Jinan: Shandong University, 2011.
[9] WU N Q, CHU F, CHU C B, et al. Short-term schedulability analysis of multiple distiller crude oil operations in refinery with oil residency time constraint[J]. IEEE Transactions on Systems Man & Cybernetics Part C (Applications & Reviews), 2009, 39(1): 1-16
[10] WU N Q, CHU C B, CHU F, et al. Schedulability analysis of short-term scheduling for crude oil operations in refinery with oil residency time and charging-tank-switch-overlap constraints[J]. IEEE Transactionson Automation Science and Engineering, 2011, 8(1): 190-204
[11] WU N Q, CHU F, CHU C B, et al. Short-term schedulability analysis of crude oil operations in refinery with oil residency time constraint using petri nets[J]. IEEE Transactions on Systems Man & Cybernetics Part C (Applications & Reviews), 2008, 38(6): 765-778
[12] 成华, 伍乃骐. 双管道输送原油短期生产计划初态达成分析[J]. 机电工程技术, 2014, 43(7): 37-43
CHEN Hua, WU Naiqi. Reachability analysis of initial state of short-term scheduling for crude oil operations in refinery with two transportation pipelines[J]. Mechanical & Electrical Engineering Technology, 2014, 43(7): 37-43
[13] HOU Y, WU N Q, ZHOU M C, et al. Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(3): 517-530
[14] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197
[15] 陈祥, 朱传军, 张超勇. 基于文化基因算法的开放车间调度问题研究[J]. 工业工程, 2018, 21(6): 16- 22
CHEN Xiang, ZHU Chuanjun, ZHANG Chaoyong. An open shop scheduling problem based on memetic algorithm[J]. Industrial Engineering Journal, 2018, 21(6): 16- 22
[16] JIANG H, LIU C Y. Scheduling optimization of cloud re- source supply chain through multi-objective particle swarm optimization[J]. International Journal of Simulation Modelling, 2019, 18(1): 163-174
[17] KENNEDY J. Bare bones particle swarms[M/OL]. Institute of Electrical and Electronics Engineers, 2003. (2003-06-05)[2019-05-14]. https://ieeexplore.ieee.org/document/1202251.
[18] 胡旺, YEN G G, 张鑫. 基于Pareto熵的多目标粒子群优化算法[J]. 软件学报, 2014, 25(5): 1025-1050
HU Wang, YEN Gary G, ZHANG Xin. Multi-objective particle swarm optimization based on pareto entropy[J]. Journal of Software, 2014, 25(5): 1025-1050
[19] ZHANG Y, GONG D W, DING Z H. A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch[J]. Information Sciences, 2012, 192: 213-227
[20] COELLO C A C, LECHUGA M. MOPSO: a proposal for multiple objective particle swarm optimization[C/OL]. Proceedings of the 2002 Congress on Evolutionary Computation. (2002-08-07)[2019-05-14].http://ieeexplore.ieee.org/document/1004388.
[21] DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601
[22] TIAN Y, CHENG R, ZHANG X Y, et al. PlatEMO: a matlab platform for evolutionary multi-objective optimization[J]. IEEE Computational Intelligence Magazine, 2017, 12(4): 73-87
[23] ZITZLER E, THIELE L. Multi-objective evolutionary al- gorithms: a comparative case study and the strength pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271
[24] ZHANG Q F, LI H. MOEA/D: a multiobjective evolutonary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731