1 |
丁进良,杨翠娥,陈远东,等.复杂工业过程智能优化决策系统的现状与展望[J].自动化学报,2018,44(11):1931-1943. 10.16383/j.aas.2018.c180550
|
|
DING J L, YANG C E, CHEN Y D, et al. Research progress and prospects of intelligent optimization decision making in complex industrial process[J]. Acta Automatica Sinica, 2018, 44(11): 1931-1943. 10.16383/j.aas.2018.c180550
|
2 |
陈远东,丁进良.炼油生产调度研究现状与挑战[J].控制与决策,2022,37(9):2177-2188.
|
|
CHEN Y D, DING J L. State-of-arts and challenges on production scheduling of refinery[J]. Control and Decision, 2022, 37(9): 2177-2188.
|
3 |
郑万鹏,高小永,朱桂瑶,等.原油作业过程优化的研究进展[J].化工学报,2021,72(11):5481-5501. 10.11949/0438-1157.20210850
|
|
ZHENG W P, GAO X Y, ZHU G Y, et al. Research progress on crude oil operation optimization[J]. CIESC Journal, 2021, 72(11): 5481-5501. 10.11949/0438-1157.20210850
|
4 |
张闻强,邢征,杨卫东.基于多区域采样策略的混合粒子群优化求解多目标柔性作业车间调度问题[J].计算机应用,2021,41(8):2249-2257. 10.11772/j.issn.1001-9081.2020101675
|
|
ZHANG W Q, XING Z, YANG W D. Hybrid particle swarm optimization with multi-region sampling strategy to solve multi-objective flexible job-shop scheduling problem[J]. Journal of Computer Applications, 2021, 41(8): 2249-2257. 10.11772/j.issn.1001-9081.2020101675
|
5 |
闫红超,汤伟,姚斌.求解置换流水车间调度问题的混合鸟群算法[J].计算机应用,2022,42(9):2952-2959. 10.11772/j.issn.1001-9081.2021091650
|
|
YAN H C, TANG W, YAO B. Hybrid bird swarm algorithm for solving permutation flowshop scheduling problem[J]. Journal of Computer Applications, 2022, 42(9): 2952-2959. 10.11772/j.issn.1001-9081.2021091650
|
6 |
DU W, ZHONG W M, TANG Y, et al. High-dimensional robust multi-objective optimization for order scheduling: a decision variable classification approach[J]. IEEE Transactions on Industrial Informatics, 2018, 15(1): 293-304. 10.1109/tii.2018.2836189
|
7 |
LI J, LI W K, KARIMI I, et al. Improving the robustness and efficiency of crude scheduling algorithms[J]. AIChE Journal, 2007, 53(10): 2659-2680. 10.1002/aic.11280
|
8 |
LI W, HUI C W, HUA B, et al. Scheduling crude oil unloading, storage, and processing[J]. Industrial & Engineering Chemistry Research, 2002, 41(26): 6723-6734. 10.1021/ie020130b
|
9 |
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,2012:83-108. 10.1016/j.compchemeng.2012.05.009
|
|
CHEN X. A study on the application of discrete and continuous time models to the crude oil scheduling problem[D]. Beijing: Tsinghua University,2012:83-108. 10.1016/j.compchemeng.2012.05.009
|
10 |
周智菊,周祥,周涵.基于滚动时域分解策略的原油混输调度模型[J].石油学报(石油加工),2021,37(2):320-329.
|
|
ZHOU Z J, ZHOU X, ZHOU H. Scheduling model based on rolling-horizon algorithm for crude oil transportation[J]. Acta Petrolei Sinica (Petroleum Processing Section), 2021, 37(2): 320-329.
|
11 |
PANDA D, RAMTEKE M. Reactive scheduling of crude oil using structure adapted genetic algorithm under multiple uncertainties[J]. Computers & Chemical Engineering, 2018, 116: 333-351. 10.1016/j.compchemeng.2018.04.005
|
12 |
HOU Y, WU N Q, LI Z. A genetic algorithm approach to short-term scheduling of crude oil operations in refinery[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2016, 11 (5): 593-603. 10.1002/tee.22277
|
13 |
WU N, CHU F, CHU C, 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, and Cybernetics, Part C (Applications and Reviews), 2008, 39(1): 1-16. 10.1109/tsmcc.2008.2001709
|
14 |
HOU Y, WU N, ZHOU M, et al. Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 47(3): 517-530.
|
15 |
DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. 10.1109/4235.996017
|
16 |
LIU Z-Z, WANG Y. Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces[J]. IEEE Transactions on Evolutionary Computation, 2019, 23(5): 870-884. 10.1109/tevc.2019.2894743
|
17 |
CHENG R, JIN Y. A competitive swarm optimizer for large scale optimization[J]. IEEE Transactions on Cybernetics, 2014, 45(2): 191-204. 10.1109/tcyb.2014.2322602
|
18 |
ZHAO S-Z, SUGANTHAN P N, DAS S. Self-adaptive differential evolution with multi-trajectory search for large-scale optimization[J]. Soft Computing, 2011, 15: 2175-2185. 10.1007/s00500-010-0645-4
|
19 |
HE X, ZHENG Z, ZHOU Y. MMES: mixture model-based evolution strategy for large-scale optimization[J]. IEEE Transactions on Evolutionary Computation, 2020, 25(2): 320-333. 10.1109/tevc.2020.3034769
|
20 |
MISENER R, FLOUDAS C A. ANTIGONE: algorithms for continuous/integer global optimization of nonlinear equations[J]. Journal of Global Optimization, 2014, 59: 503-526. 10.1007/s10898-014-0166-2
|
21 |
ACHTERBERG T. SCIP: solving constraint integer programs[J]. Mathematical Programming Computation, 2009, 1: 1-41. 10.1007/s12532-008-0001-1
|
22 |
LUNDELL A, KRONQVIST J, WESTERLUND T. The supporting hyperplane optimization toolkit for convex MINLP[J]. Journal of Global Optimization, 2022, 84: 1-41. 10.1007/s10898-022-01128-0
|
23 |
HOOKER J N. Planning and scheduling by logic-based Benders decomposition[J]. Operations Research, 2007, 55(3): 588-602. 10.1287/opre.1060.0371
|
24 |
KRONQVIST J, LUNDELL A, WESTERLUND T. The extended supporting hyperplane algorithm for convex mixed-integer nonlinear programming[J]. Journal of Global Optimization, 2016, 64: 249-272. 10.1007/s10898-015-0322-3
|
25 |
MUTS P, NOWAK I, HENDRIX E M T. The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming[J]. Journal of Global Optimization, 2020, 77: 75-96. 10.1007/s10898-020-00888-x
|