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摘要: 針對煉鋼?連鑄過程的生產調度問題，首先，對生產調度問題的研究方法進行了總結和評述，梳理了各類方法的特點及適用范圍；其次，介紹了當前國內外鋼廠典型計算機輔助調度系統的一些案例，并討論分析其特點；最后，在以往研究的基礎上，對未來煉鋼?連鑄過程生產調度問題的研究思路和方法提出了建議，針對靜態調度提出了“規則+算法”的研究思路，以國內某特鋼廠為例，提出了基于鋼廠生產模式優化的調度模型構建方法；針對動態調度提出“多工序協同”的研究思路，提出了基于多智能體的煉鋼?連鑄過程多工序工藝、質量與調度的協同控制的研究方法。優化高效的建模及求解方法是解決生產調度問題的重要手段之一，旨在改善當前鋼廠生產計劃編制水平、提高生產計劃的可執行性、加強現場實時調控，對實現煉鋼?連鑄過程穩定化、有序化、連續化運行具有重要意義。Abstract: Production scheduling is one of the key technologies in steel manufacturing process. It plays a significant role in reducing the production cost and improving the production efficiency of iron and steel enterprises. In recent years, with the rapid development of intelligent steel-manufacturing technology, production scheduling has attracted increasing attention and become a research hotspot in the field of iron and steel metallurgy. The process of production scheduling in the process of steelmaking?continuous casting was summarized and discussed through reviews of previous researches, and the characteristics and application scopes of various methods were compared and classified. Typical cases of the computer-aided scheduling system in domestic and overseas steelmaking plants were discussed, and the characteristics of each system were studied and analyzed comparatively. On the basis of the previous studies, forward-looking strategies and a methodology of production scheduling were proposed for the future study of steelmaking?continuous casting process. For static scheduling, a new concept “rules + algorithm” was proposed, and a scheduling model construction method based on the production mode optimization of a steel mill was developed for a domestic special-steelmaking plant as a case study. For dynamic scheduling, multi-process collaboration was suggested, and a collaborative control method based on multi-agent was proposed. This method was developed for multi-process control, quality control, and scheduling control of the steelmaking-continuous casting process. An optimized and effective method for modeling and solving is one of the important means to solve the production-scheduling problem, aiming at improving the levels of setting up production plan and the feasibility of production planning. Meanwhile proposed method for modeling and solving can strengthen the on-site real-time control in steel mills and is of great significance to realize stable, orderly, and continuous operation in steelmaking-continuous casting process.
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圖 3 四爐?三機生產模式（3號連鑄機停機）
Figure 3. Production mode of four furnaces to three continuous casters(No.3 continuous caster shut down)
圖 4 煉鋼?連鑄過程多工序協同控制
Figure 4. Collaborative control of multiple processes in steelmaking?continuous casting process
圖 5 基于案例推理和規則推理的煉鋼?連鑄動態調度策略
Figure 5. Dynamic scheduling strategy in steelmaking?continuous casting based on case-based reasoning and rule-based reasoning
表 1 鋼鐵生產調度的研究方法
Table 1. Research methods of iron and steel production scheduling
算法 特點 案例 運籌學方法 1）計算結果準確，使用有效性高，在理論上能獲得問題的全局最優解；2）適用于產品品種單一、脈絡較為清晰的生產流程，對較為簡單的調度問題，能快速得到最優調度方案；3）隨著生產規模或者約束條件的復雜化，計算時間將呈指數化增長；4）不能涵蓋所有的影響因素，實際應用范圍較小，且計算難度較大。 數學規劃[7?8]、拉格朗日松弛
法[9?10]、分支定界法[11?12]啟發式算法 1）操作簡單、求解速度快，解的質量較高；2）求解規模較大的問題時搜索效率低，評估解的質量的手段較少；3）在性能上并不要求精確解，只希望盡可能接近“最優解”。 多種規則的啟發式算法[13-15] 遺傳算法[16] 1）計算時間短，具有很好的收斂性、魯棒性強；2）個體數量較多時，搜索空間大且搜索時間較長；3）算法對新空間的探索能力有限，往往會出現早熟，收斂到局部最優解的情況；由于該算法的進化過程存在隨機性，解的可靠性及穩定性較差；4）對初始種群很敏感，初始種群選擇不好會影響解的質量和算法效率。 混合遺傳算法[17?18]、
改進遺傳算法[19?20]蟻群算法[21] 1）具有較強的魯棒性和搜索較好解的能力；2）對初始路線要求不高，求解結果不依賴于初始路線的選擇，而且在搜索過程中不需要進行人工的調整；3）在解決大型優化問題時，存在搜索空間和時間性能上的矛盾，易出現過早收斂于非全局最優解以及計算時間過長等問題。 其他算法與蟻群算法結合[22?23] 粒子群算法 1）算法結構簡單、需要調節的參數少、實現方式容易；2）對于離散的優化問題處理不佳，容易陷入局部最優；3）廣泛應用于函數優化、多目標優化、求解整數約束和混合整數約束優化問題、神經網絡訓練、信號處理等實際問題中。 離散粒子群算法[24?25]、其他算法與粒子群算法結合[26] 人工蜂群算法 1）控制參數少、收斂速度快、穩定性好和效率高；2）后期收斂速度慢、
易陷入局部最優、搜索精度不高。改進人工蜂群算法[27?28] 禁忌搜索[29] 1）收斂性好，快速而高概率地向好的方向移動；2）需要調整不同的參數，參數的選取對最后得到的解有著直接的影響，沒有很好的魯棒性；3）禁忌長度和禁忌表不宜太大，禁忌長度太小容易循環搜索，禁忌表太長容易陷入“局部解”。 求解最優爐次計劃[30]、
與其他算法結合[31]模擬退火[32] 1）它的解是隨機近似解，所求解的質量有賴于大量實驗；2）突破局域搜索的限制，
因此其全局搜索性較強；3）各個參數選擇比較困難，如果選擇不得當，就會使得計算時間很長，而且可能得不到好的結果。模擬退火與遺傳算法結合[33]、混合排序免疫模擬退火[34] 系統仿真 1）通過建立仿真模型來模擬實際生產情況，對實際系統全局性進行分析；2）設計、建立和運行仿真模型進行生產調度在時間、費用上成本很高；3）仿真的準確性受操作人員的判斷技巧的限制，甚至很高精度的仿真模型也無法保證能找到最優或次優的調度。 仿真優化[35]、Petri網模型[36] 專家系統 1）應用大量的專家知識和推理方法求解復雜問題；2）專家系統能解決特定領域的一些具體問題，在煉鋼?連鑄過程中多用于建立生產調度系統。 專家系統[37?38] 多智能體系統 1）特別適用于解決具有大量交互作用的復雜問題；2）將復雜大系統分解成結構簡單、且彼此相互通訊及相互協調的、易于管理的多個簡單智能體(agent)組成的復雜系統。 與其他算法結合[39?40] 
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表 2 國內外一些典型鋼廠的計算機輔助調度系統使用情況
Table 2. Application of computer-aided scheduling system in steel plants in China and overseas
地區 鋼廠 名稱 特點 國內 河鋼唐鋼 唐鋼APS（advanced planning and
scheduling）系統智能化優化算法結合人工計劃排產知識和經驗，實現資源整合、統一管理，提高排產效率及靈活性。 首鋼京唐 計算機輔助調度系統 主要作用是過程監控，通過人工實現計劃排產。 太原鋼鐵 鋼軋作業排產計劃 結合工藝限制條件的自動優化算法結合人工計劃，
側重人機交互，由計劃員進行最終調整確認。上海寶鋼 煉鋼?連鑄調度系統 通過專家推理建立調度模型，結合人機交互的方式，
對復雜事件做出快速響應。首鋼遷鋼[41] 煉鋼?連鑄生產調度系統 通過Gantt圖編輯器實現系統和人工相結合的決策方式，
增強系統的可控性。方大特鋼 煉鋼?連鑄計算機輔助生產調度系統 運用調度規則，計算初始調度方案，再根據實時數據反饋，
調整調度方案。國外 印度JSPL[42] 高級計劃與排程(APS)和制造執行系統(MES) MES系統將來自訂單的指令發布給排產系統、二級系統后，再運行來自接口的新的訂單項，訂單設計是交互式或者自動化。 日本新日鐵[43] production planning and scheduling系統 采用基于約束的規則運算和優化算法，提供了從煉鋼到熱軋的生產作業計劃和排程能力。 Bethlehem steel[43] steel planner 分為計劃模塊和優化模塊，可分析得到生產計劃。 德國曼內斯曼鋼鐵公司[44] 計算機生產調度管理系統 采用模糊優化方法，以Petri網理論為基礎，做到動態模擬和
動態優化調整調度系統。韓國浦項光陽鋼鐵廠[44] HIPASS系統 編制高爐到冷軋的綜合生產時刻表，并進行
狀態監視和緊急情況的支持。日本NKK京濱鋼鐵廠 Scheplan系統[45] 采用人工智能和人機交互技術生成調度計劃，系統包括高爐?
轉爐、轉爐?連鑄、連鑄?軋機的調度。
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參考文獻
[1] Yin R Y. A discussion on “smart” steel plant—view from physical system side. Iron Steel, 2017, 52(6): 1殷瑞鈺. 關于智能化鋼廠的討論——從物理系統一側出發討論鋼廠智能化. 鋼鐵, 2017, 52(6):1 [2] Zhang C X, Wang H F, Zhang S R, et al. Strategic study on green development of Chinese steel industry. Iron Steel, 2015, 50(10): 1張春霞, 王海風, 張壽榮, 等. 中國鋼鐵工業綠色發展工程科技戰略及對策. 鋼鐵, 2015, 50(10):1 [3] Wu X L, Sun S D, Yang Z, et al. Survey on multi-objective flexible Job Shop scheduling problem. Appl Res Comput, 2007, 24(3): 1 doi: 10.3969/j.issn.1001-3695.2007.03.001吳秀麗, 孫樹棟, 楊展, 等. 多目標柔性Job Shop調度問題的技術現狀和發展趨勢. 計算機應用研究, 2007, 24(3):1 doi: 10.3969/j.issn.1001-3695.2007.03.001 [4] Zheng Z, Long J Y, Gao X Q, et al. Present situation and prospect of production control technology focusing on planning and scheduling in iron and steel enterprise. Comput Integr Manuf Syst, 2014, 20(11): 2660鄭忠, 龍建宇, 高小強, 等. 鋼鐵企業以計劃調度為核心的生產運行控制技術現狀與展望. 計算機集成制造系統, 2014, 20(11):2660 [5] Wang B, Wang C, Wang B, et al. Comprehensive optimization of production planning and scheduling for steelmaking-continuous casting process. J Wuhan Univ Sci Technol, 2014, 37(3): 161王彬, 王闖, 王寶, 等. 煉鋼?連鑄過程生產計劃與調度問題綜合優化研究. 武漢科技大學學報, 2014, 37(3):161 [6] Tang L X, Liu J Y, Rong A Y, et al. A review of planning and scheduling systems and methods for integrated steel production. Eur J Oper Res, 2001, 133(1): 1 doi: 10.1016/S0377-2217(00)00240-X [7] Touil A, Echchtabi A, Bellabdaoui A, et al. A hybrid metaheuristic method to optimize the order of the sequences in continuous-casting. Int J Ind Eng Comput, 2016, 7(3): 385 [8] Tang L X, Liu J Y, Rong A Y, et al. A mathematical programming model for scheduling steelmaking-continuous casting production. Eur J Oper Res, 2000, 120(2): 423 doi: 10.1016/S0377-2217(99)00041-7 [9] Pang X F, Gao L, Pan Q K, et al. A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production. J Cent South Univ, 2017, 24(2): 467 doi: 10.1007/s11771-017-3449-1 [10] Cui H J, Luo X C. An improved Lagrangian relaxation approach to scheduling steelmaking-continuous casting process. Comput Chem Eng, 2017, 106: 133 doi: 10.1016/j.compchemeng.2017.05.026 [11] An Y W, Yan H S. Integrated optimization for the production planning and scheduling in a two-stage production system. Comput Integr Manuf Syst, 2012, 18(4): 796安玉偉, 嚴洪森. 一類兩階段生產系統生產計劃與調度的集成優化. 計算機集成制造系統, 2012, 18(4):796 [12] Xu W J, Tang L X, Pistikopoulos E N. Modeling and solution for steelmaking scheduling with batching decisions and energy constraints. Comput Chem Eng, 2018, 116: 368 doi: 10.1016/j.compchemeng.2018.03.010 [13] Liu G H, Li T K. A steelmaking-continuous casting production scheduling model and its heuristic algorithm. Syst Eng, 2002, 20(6): 44 doi: 10.3969/j.issn.1001-4098.2002.06.009劉光航, 李鐵克. 煉鋼?連鑄生產調度模型及啟發式算法. 系統工程, 2002, 20(6):44 doi: 10.3969/j.issn.1001-4098.2002.06.009 [14] Feng Z J, Yang G K, Du B, et al. Two stage optimization algorithms based on heuristic and linear programming in continuous casting scheduling. Metall Ind Autom, 2005(4): 18 doi: 10.3969/j.issn.1000-7059.2005.04.005馮振軍, 楊根科, 杜斌, 等. 煉鋼連鑄調度的啟發式和線性規劃兩步優化算法. 冶金自動化, 2005(4):18 doi: 10.3969/j.issn.1000-7059.2005.04.005 [15] Yu S P, Chai T Y. Heuristic scheduling method for steelmaking and continuous casting production process. Control Theory Appl, 2016, 33(11): 1413俞勝平, 柴天佑. 煉鋼?連鑄生產啟發式調度方法. 控制理論與應用, 2016, 33(11):1413 [16] Sampson J R. Adaptation in natural and artificial systems (John H. Holland). SIAM Rev, 1976, 18(3): 2 [17] Fujii S, Tanimoto S, Tomiyama S, et al. Genetic algorithm with reduction of search space using operational constraints and its application to scheduling system for steelmaking process. Tetsu-to-Hagané, 2003, 89(12): 1220 doi: 10.2355/tetsutohagane1955.89.12_1220 [18] Xu Z J, Zheng Z, Gao X Q. HGA combined with priority strategy for production planning of steelmaking-continuous casting. Control Decision, 2016, 31(8): 1394徐兆俊, 鄭忠, 高小強. 煉鋼連鑄生產調度的優先級策略混合遺傳算法. 控制與決策, 2016, 31(8):1394 [19] Yuan S P, Li T K, Wang B L. Improved fast elitist non-dominated sorting genetic algorithm for multi-objective steelmaking-continuous casting production scheduling. Comput Integr Manuf Syst, 2019, 25(1): 115袁帥鵬, 李鐵克, 王柏琳. 多目標煉鋼?連鑄生產調度的改進帶精英策略的快速非支配排序遺傳算法. 計算機集成制造系統, 2019, 25(1):115 [20] Li T K, Su Z X. Two-stage genetic algorithm for SM-CC production scheduling. Chin J Manage Sci, 2009, 17(5): 68 doi: 10.3321/j.issn:1003-207X.2009.05.010李鐵克, 蘇志雄. 煉鋼連鑄生產調度問題的兩階段遺傳算法. 中國管理科學, 2009, 17(5):68 doi: 10.3321/j.issn:1003-207X.2009.05.010 [21] Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cyberne Part B Cybern, 1996, 26(1): 29 doi: 10.1109/3477.484436 [22] Zhu D F, Zheng Z, Gao X Q. Hybrid genetic algorithm optimization-based production planning and simulation analysis for steelmaking and continuous casting. Comput Eng Appl, 2010, 46(1): 241 doi: 10.3778/j.issn.1002-8331.2010.01.072朱道飛, 鄭忠, 高小強. 煉鋼連鑄作業計劃的混合遺傳優化與仿真分析. 計算機工程與應用, 2010, 46(1):241 doi: 10.3778/j.issn.1002-8331.2010.01.072 [23] Fang Y D, Wang F, Wang H. Research of multi-objective optimization study for job shop scheduling problem based on grey ant colony algorithm. Adv Mater Res, 2011, 308-311: 1033 [24] Xue Y C, Zheng D L, Yang Q W. Optimum charge plan of steelmaking continuous casting based on the modified discrete particle swarm optimization algorithm. Comput Integr Manuf Syst, 2011, 17(7): 1509薛云燦, 鄭東亮, 楊啟文. 基于改進離散粒子群算法的煉鋼連鑄最優爐次計劃. 計算機集成制造系統, 2011, 17(7):1509 [25] Zheng P, Tang Q H, Zhang Q M, et al. Discrete particle swarm optimization for steelmaking and continuous casting production. Mach Des Manuf, 2016(7): 49 doi: 10.3969/j.issn.1001-3997.2016.07.013鄭鵬, 唐秋華, 張啟敏, 等. 基于離散粒子群算法的煉鋼連鑄生產調度. 機械設計與制造, 2016(7):49 doi: 10.3969/j.issn.1001-3997.2016.07.013 [26] Zarandi M H F, Dorry F. A hybrid fuzzy PSO algorithm for solving steelmaking-continuous casting scheduling problem. Int J Fuzzy Syst, 2018, 20(1): 219 doi: 10.1007/s40815-017-0331-0 [27] Pan Q K. An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling. Eur J Oper Res, 2016, 250(3): 702 doi: 10.1016/j.ejor.2015.10.007 [28] Peng K K, Pan Q K, Zhang B. An improved artificial bee colony algorithm for steelmaking-refining-continuous casting scheduling problem. Chin J Chem Eng, 2018, 26(8): 1727 doi: 10.1016/j.cjche.2018.06.008 [29] Glover F, Greenberg H J. New approaches for heuristic search: a bilateral linkage with artificial intelligence. Eur J Oper Res, 1989, 39(2): 119 doi: 10.1016/0377-2217(89)90185-9 [30] Yu G, Tian N Y, Xu A J. Computer-aided simulation of steelmaking-continuous casting production scheduling. J Univ Sci Technol Beijing, 2009, 31(9): 1183于港, 田乃媛, 徐安軍. 煉鋼?連鑄區段生產調度與計算機仿真. 北京科技大學學報, 2009, 31(9):1183 [31] Zhang Q, Manier H, Manier M A. A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times. Comput Oper Res, 2012, 39(7): 1713 doi: 10.1016/j.cor.2011.10.007 [32] Kirkpatrick S, Gellatt C D, Vecchi M P. Optimisation by simulated annealing. Science, 1982, 220(4598): 671 [33] Zhao W. Job-shop scheduling based on genetic algorithm and simulated annealing algorithm. Comput Simul, 2011, 28(7): 361 doi: 10.3969/j.issn.1006-9348.2011.07.090趙衛. 模擬退火遺傳算法在車間作業調度中的應用. 計算機仿真, 2011, 28(7):361 doi: 10.3969/j.issn.1006-9348.2011.07.090 [34] Shivasankaran N, Kumar P S, Raja K V. Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling. Int J Comput Intell Syst, 2015, 8(3): 455 doi: 10.1080/18756891.2015.1017383 [35] Yang Z, Qiu H L, Luo X W, et al. Simulating schedule optimization problem in steelmaking continuous casting process. Int J Simul Model, 2015, 14(4): 710 doi: 10.2507/IJSIMM14(4)CO17 [36] Wu Y L, Zeng J C, Wei J H, et al. Dynamic scheduling algorithm for batch process systems based on hybrid Petri nets. J Xi'an Jiaotong Univ, 2002, 36(2): 147 doi: 10.3321/j.issn:0253-987X.2002.02.010吳亞麗, 曾建潮, 衛軍胡, 等. 一種基于混合Petri網的批過程動態調度算法. 西安交通大學學報, 2002, 36(2):147 doi: 10.3321/j.issn:0253-987X.2002.02.010 [37] Liao S H. Expert system methodologies and applications-a decade review from 1995 to 2004. Expert Syst Appl, 2005, 28(1): 93 doi: 10.1016/j.eswa.2004.08.003 [38] Sun F Q, Tang L X, Zheng B L. The steelmaking- continuous casting production schedulingbased on expert system. Metall Ind Autom, 1998(6): 31孫福權, 唐立新, 鄭秉霖. 煉鋼?連鑄生產調度專家系統. 冶金自動化, 1998(6):31 [39] Ma C B. Research on the Simulation Methods of Crane Scheduling in Steel Plants Based on Multi-Agent [Dissertation]. Kunming: Kunming University of Science and Technology, 2011馬長波. 基于多智能體的煉鋼廠車間天車調度仿真方法研究[學位論文]. 昆明: 昆明理工大學, 2011 [40] Gu X J, Zhu Z Y, Zhang J, et al. Multi agent ant colony optimization for dynamic scheduling of steelmaking and continuous casting. Sci Technol Eng, 2016, 16(27): 50 doi: 10.3969/j.issn.1671-1815.2016.27.009谷學靜, 朱朝月, 張建, 等. 多Agent蟻群算法求解煉鋼-連鑄動態調度問題. 科學技術與工程, 2016, 16(27):50 doi: 10.3969/j.issn.1671-1815.2016.27.009 [41] Sun L, Liu J C. Steelmaking−continuous casting production scheduling system and application // Proceedings of 2013 Annual Meeting of Metallurgical Branch of China Metrology Association. Jinan, 2013: 304孫玲, 劉繼超. 煉鋼−連鑄生產調度系統及應用// 中國計量協會冶金分會2013年會論文集. 濟南, 2013: 304 [42] Broner. Broner將向印度JSPL提供煉鋼廠最優化解決方案[EB/OL]. 新聞通訊[2019-04-30] https://max.book118.com/html/2017/0328/97503178.shtm [43] Liu X Q, Gu J C, Sun Y G, et al. Planning and scheduling system of MES in iron and steel industry. Metall Ind Autom, 2004(1): 22 doi: 10.3969/j.issn.1000-7059.2004.01.006劉曉強, 顧佳晨, 孫彥廣, 等. 鋼鐵企業MES中的計劃調度系統. 冶金自動化, 2004(1):22 doi: 10.3969/j.issn.1000-7059.2004.01.006 [44] Pang X F, Yu S P, Liu W, et al. Research and development of steelmaking and continuous casting dynamic intelligence scheduling system. Control Eng China, 2005, 12(6): 553 doi: 10.3969/j.issn.1671-7848.2005.06.013龐新富, 俞勝平, 劉煒, 等. 煉鋼連鑄動態智能調度系統的研究與開發. 控制工程, 2005, 12(6):553 doi: 10.3969/j.issn.1671-7848.2005.06.013 [45] Numao M, Morishita S. Scheplan-a scheduling expert for steel-making process // Proceedings of theInternational Workshop on Artificial Intelligence for Industrial Applications. Hitachi City, 1988: 467 [46] Wang C, Liu Q, Wang B, et al. Scheduling model of steelmaking-continuous casting processes in special steel plants. J Univ Sci Technol Beijing, 2013, 35(3): 371王闖, 劉青, 王彬, 等. 特殊鋼廠煉鋼?連鑄調度模型. 北京科技大學學報, 2013, 35(3):371 [47] Wang G, Wang B, Wang B, et al. Scheduling model for steelmaking-continuous casting process based on “furnace-caster matching” principle. J Univ Sci TechnolBeijing, 2013, 35(8): 1080王剛, 王彬, 王寶, 等. 基于“爐機對應”原則的煉鋼?連鑄調度模型. 北京科技大學學報, 2013, 35(8):1080 [48] Mu Y Q, Yin J, Xie F M, et al. Research on matching between furnaces and casters in special steel plants. J Univ Sci TechnolBeijing, 2013, 35(1): 126穆衍清, 尹佳, 謝飛鳴, 等. 特殊鋼廠爐機匹配研究. 北京科技大學學報, 2013, 35(1):126 [49] Liu Q, Tian N Y, Yin R Y. Operation principle and control strategy for steelmaking workshop system. Chin J Process Eng, 2003, 3(2): 171 doi: 10.3321/j.issn:1009-606X.2003.02.015劉青, 田乃媛, 殷瑞鈺. 煉鋼廠系統的運行原則與調控策略. 過程工程學報, 2003, 3(2):171 doi: 10.3321/j.issn:1009-606X.2003.02.015 [50] Liu Q, Wang B, Wang Z, et al. Lean manufacturing techniques in steel plants. China Sci Technol Achievements, 2014(11): 78 doi: 10.3772/j.issn.1009-5659.2014.11.033劉青, 王彬, 汪宙, 等. 煉鋼廠的精益制造技術. 中國科技成果, 2014(11):78 doi: 10.3772/j.issn.1009-5659.2014.11.033 [51] Wang B. Optimal Planning and Collaborative Scheduling on Steelmaking-Continuous Cast Process in Special Steel Plants of Long Products [Dissertation]. Beijing: University of Science and Technology Beijing, 2015王彬. 長材型特殊鋼廠煉鋼?連鑄過程生產計劃優化與協同調度[學位論文]. 北京: 北京科技大學, 2015 [52] Zhang C S, Li T K, Wang B L, et al. Dynamic CSP-based modeling and algorithm of scheduling problem for steelmaking-continuous casting under steelmaking machine failures. Comput Eng Appl, 2012, 48(22): 226 doi: 10.3778/j.issn.1002-8331.2012.22.046張春生, 李鐵克, 王柏琳, 等. 煉鋼機器故障下煉鋼?連鑄調度DCSP建模與算法. 計算機工程與應用, 2012, 48(22):226 doi: 10.3778/j.issn.1002-8331.2012.22.046 [53] Wang B L, Li T K, Zhang C S, et al. Dynamic CSP based scheduling algorithm for steelmaking and continuous casting with conticaster breakdown. Comput Integr Manuf Syst, 2011, 17(10): 2185王柏琳, 李鐵克, 張春生, 等. 基于動態約束滿足的考慮連鑄機故障的煉鋼連鑄調度算法. 計算機集成制造系統, 2011, 17(10):2185 [54] Wang Q B, Zhang W X, Wang B L, et al. Agent-based dynamic scheduling system for hybrid flow shop. J Comput Appl, 2017, 37(10): 2991 doi: 10.11772/j.issn.1001-9081.2017.10.2991王芊博, 張文新, 王柏琳, 等. 基于Agent的混合流水車間動態調度系統. 計算機應用, 2017, 37(10):2991 doi: 10.11772/j.issn.1001-9081.2017.10.2991 [55] Wang B L, Wang H F, Li T K. Design of a monitoring and rescheduling system for steelmaking-continuous casting production. Int J Performability Eng, 2018, 14(12): 3076 -
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