[1] FLOUDAS C A, LIN 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, CHU F, CHU C, 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, and Cybernetics, Part C (Applications and Reviews), 2008, 38(6): 765-778 [3] WU N, CHU C, 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 Transactions on Automation Science and Engineering, 2010, 8(1): 190-204 [4] WU N, CHU F, CHU C, et al. Short-term schedulability analysis of multiple distiller crude oil operations in refinery with oil residency time cons traint[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2008, 39(1): 1-16 [5] 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, 2017, 47(3): 517-530 [6] 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 [7] WU N, LI Z, 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 [8] ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength Pareto evolutionary algorithm[R/OL]. TIK-report, 2001, 103: (2001-05).https://doi.org/10.3929/ethz-a-004284029. [9] ZITZLER E, KÜNZLI S. Indicator-based selection in multiobjective search[C]// Proceedings of the 8th International Conference On Parallel Problem Solving from Nature. Berlin: Springer-Verlag, 2004. 832-842. [10] ZHANG Q, LI H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731 [11] 孙宝凤, 任欣欣, 郑再思, 等. 考虑工人负荷的多目标流水车间优化调度[J]. 吉林大学学报(工学版), 2021, 51(3): 900-909 SUN Baofeng, REN Xinxin, ZHENG Zaisi, et al. Multi-objective flow shop optimal scheduling considering worker's load[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 900-909 [12] LARRAÍN S, PRADENAS L, PULKKINEN I, et al. Multiobjective optimization of a continuous kraft pulp digester using SPEA2[J]. Computers & Chemical Engineering, 2020, 143: 107086 [13] LIU X, ZHANG D. An improved SPEA2 algorithm with local search for multi-objective investment decision-making[J]. Applied Sciences, 2019, 9(8): 1675 [14] MIRJALILI S, SAREMI S, MIRJALILI S M, et al. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization[J]. Expert Systems with Applications, 2016, 47: 106-119 [15] 邢怀玺, 吴华, 陈游, 等. 基于多目标灰狼算法的干扰资源多效能优化方法[J]. 北京航空航天大学学报, 2020, 46(10): 1990-1998 XING Huaixi, WU Hua, CHEN You, et al. Multi-efficiency optimization method of jamming resource based on multi-objective grey wolf optimizer[J]. Journal of Beijing University of Aeronatics and Astronautics, 2020, 46(10): 1990-1998 [16] 李长安, 谢宗奎, 吴忠强, 等. 改进灰狼算法及其在港口泊位调度中的应用[J]. 哈尔滨工业大学学报, 2021, 53(1): 101-108 LI Chang'an, XIE Zongkui, WU Zhongqiang et al. Improved grey wolf algorithm and its application in port berth scheduling[J]. Journal of Harbin Institute of Technology, 2021, 53(1): 101-108 [17] ZHANG X, ZHANG Y, MING Z. Improved dynamic grey wolf optimizer[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(6): 877-890
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