Weight balance problem modeling and two-stage Benders decomposition heuristic algorithm design of non-ULDs
-
摘要: 为探索民航货机装载非集装器的潜力,研究了货机装载非集装器的载重平衡问题;剖析了非集装器与集装器在载重平衡上的不同,将飞机货舱视为矩形板,非集装器视为矩形块,建立了两阶段的非集装器载重平衡优化模型;在第1阶段的二维几何位置模型中,考虑了非集装器不重叠、不超出货舱边界、可正交旋转等约束,以飞机货舱面积利用率最大为目标函数;在第2阶段的配载模型中,考虑了多种飞机质量和稳定性约束,以装载量最大、重心偏差最小为多目标函数;设计使用了基于逻辑分解的Benders算法,将非集装器的载重平衡问题分解为主问题和子问题;主问题采用改进的遗传模拟算法和最低水平线算法确定非集装器放置顺序和位置,子问题采用y-check算法对各种质量和稳定性等约束检查,并给出了Benders' cut约束模型;设计了非集装器面积大于、小于货舱面积的2种场景,基于本文提出的算法、Gurobi*、Gurobi和专家配载针对2种不同的装载约束模型进行仿真验证和对比分析。分析结果表明:在货舱左右平衡的二维几何位置分配算例中,Gurobi*的解质量和求解速度较好,平均装载量、货舱面积利用率、重心偏差、求解时间分别为19 872 kg、65.88%、2.08%MAC、61.18 s;专家配载结果相对较差,平均装载量、货舱面积利用率、重心偏差、求解时间分别为18 494 kg、65.21%、2.79% MAC、986.98 s;提出的算法作为一种启发式方法,平均装载量为18 874 kg,略低于Gurobi*和Gurobi的优化结果,但平均货舱面积利用率和重心偏差分别为71.87%、2.76%MAC,且平均求解速度为175.97 s,明显快于Gurobi的1 082.92 s。建立的两阶段载重平衡优化模型和算法能够为非集装器装载位置和方向的确定提供参考。Abstract: To explore the potential of loading non-unit load devices (non-ULDs) in civil aviation cargo aircraft, the weight balance problem (WBP) of loading non-ULDs in cargo aircraft was studied. The differences between loading non-ULDs and ULDs in WBP were compared. The cargo hold was regarded as a rectangular plane and the non-ULDs as rectangular items, a two-stage weight balance optimization model was constructed for loading non-ULDs. In the two-dimensional geometric constraint model in the first stage, constraints of non-ULDs were considered such as no overlapping, not exceeding the cargo hold boundaries, and orthogonal rotation. The maximum utilization of the aircraft cargo hold plane area was taken as the objective function. In the weight balance model in the second stage, constraints of various aircraft were considered such as weight and stability. The maximum payload and minimum center of gravity (CG) deviation were selected as the multi-objective function. The Benders algorithm was designed and used based on logical decomposition. The WBP of cargo aircraft loading non-ULDs was decomposed into a master problem and a sub-problem. The master problem used improved genetic simulation and the lowest horizontal line algorithm to determine the loading sequence and position of non-ULDs. The sub-problem employed the y-check algorithm to check various constraints, such as weight and stability. The Benders' cut constraint model was provided. The two scenarios where the area of non-ULDs was greater than and less than the cargo hold area were designed. The model was verified and compared using four methods: the proposed algorithm, Gurobi*, Gurobi, and expert stowing for two different loading constraint requirements. Experimental results show that, when testing a two-dimensional geometric position allocation model for left-right balance in the cargo hold, Gurobi* achieves the best solution quality and speed. The average payload, cargo hold area utilization rate, CG deviation, and solution time are 19 872 kg, 65.88%, 2.08%MAC and 61.18 s, respectively. The expert stowing method is the worst, with its average payload, cargo hold area utilization rate, CG deviation, and solution time reaching 18 494 kg, 65.21%, 2.79%MAC, and 986.98 s, respectively. As a heuristic algorithm, the proposed algorithm gets an average payload of 18 874 kg, slightly worse than the optimized solutions of Gurobi* and Gurobi. The average cargo hold area utilization rate and CG deviation are 71.87% and 2.76%MAC, respectively. The average solution time is 175.97 s, much faster than Gurobi's of 1 082.92 s. The proposed two-stage weight balance optimization model and algorithm can provide a reference for determining the loading positions and directions of non-ULDs.
-
表 1 非集装器数据
Table 1. Non-ULD data
编号 长/m 宽/m 质量/kg 编号 长/m 宽/m 质量/kg 1 3.00 2.44 4 500 16 4.22 2.11 1 090 2 6.88 1.87 4 440 17 4.22 2.11 1 073 3 6.00 2.44 4 077 18 3.73 2.32 770 4 5.69 2.04 3 472 19 3.68 1.88 670 5 5.61 2.13 3 313 20 4.04 1.98 670 6 5.61 2.04 3 296 21 6.11 3.41 3 532 7 5.69 2.01 3 270 22 5.92 3.69 2 056 8 5.61 2.01 3 270 23 5.39 2.12 1 668 9 5.61 2.13 3 270 24 5.21 3.60 3 931 10 5.61 2.04 3 270 25 5.94 2.34 3 477 11 5.69 2.03 3 200 26 7.32 3.46 5 368 12 2.74 2.24 2 850 27 7.18 3.24 5 456 13 4.11 2.11 2 100 28 5.30 3.26 4 599 14 4.41 2.41 1 845 29 6.73 3.24 3 358 15 4.14 2.11 1 700 30 6.17 3.01 2 687 表 2 飞机基本参数
Table 2. Basic parameters of aircraft
参数 数值 参数 数值 IO 31.3 R 70 000 PS/%MAC 23 D/m 26.36 QO/kg 52 752 BL/m 25.19 QM/kg 30 708 BM/m 5.07 IT, F 4.5 L/m 47.3 IL, F 0.4 W/m 3.7 表 3 模型1装载量
Table 3. Payloads of model 1
类型 数量 算例 装载量/kg 本文算法 Gurobi* Gurobi 专家 1 20 1-1 27 185 27 994 27 994 26 678 1-2 26 235 27 388 27 388 25 112 1-3 28 220 30 379 30 379 27 543 10 1-4 27 493 28 819 28 819 26 362 1-5 24 305 20 189 20 189 21 876 1-6 23 832 20 723 20 723 19 109 1-7 26 750 28 481 28 481 24 098 5 1-8 10 735 12 998 12 998 9 798 1-9 6 965 7 010 7 010 10 032 1-10 4 286 2 065 2 065 3 298 2 20 2-1 30 181 30 573 30 573 29 177 2-2 29 639 30 414 30 414 27 843 2-3 30 115 30 648 30 648 28 754 10 2-4 23 628 23 222 23 222 19 803 2-5 12 359 11 376 11 376 10 873 2-6 20 051 21 410 21 410 22 892 2-7 14 227 15 859 15 859 12 565 5 2-8 6 617 5 283 5 283 4 695 2-9 18 238 18 238 18 238 15 987 2-10 8 584 8 584 8 584 6 003 均值 19 982 20 083 20 083 18 625 表 4 模型1货舱面积利用率
Table 4. Cargo hold area utilization rates of model 1
类型 数量 算例 货舱面积利用率/% 本文算法 Gurobi* Gurobi 专家 1 20 1-1 76.46 80.53 80.53 68.33 1-2 75.88 74.51 74.51 77.98 1-3 58.19 70.77 70.77 64.09 10 1-4 82.15 67.15 67.15 79.03 1-5 65.45 76.57 76.57 67.09 1-6 70.62 78.40 78.40 66.76 1-7 89.90 79.21 79.21 73.09 5 1-8 79.09 76.84 76.84 67.89 1-9 79.14 40.00 40.00 54.89 1-10 60.72 17.52 17.52 43.12 2 20 2-1 63.49 70.26 70.26 67.32 2-2 64.52 77.00 77.00 74.01 2-3 69.85 72.45 72.45 68.32 10 2-4 74.63 83.20 83.20 75.07 2-5 72.45 57.91 57.91 64.06 2-6 84.02 79.59 79.59 72.98 2-7 71.43 63.99 63.99 58.23 5 2-8 48.28 46.79 46.79 40.77 2-9 73.24 73.24 73.24 73.24 2-10 72.99 72.99 72.99 72.99 均值 71.62 67.95 67.95 66.46 表 5 模型1重心偏差
Table 5. CG deviations of model 1
类型 数量 算例 重心偏差/%MAC 本文算法 Gurobi* Gurobi 专家 1 20 1-1 1.99 1.78 1.07 2.32 1-2 2.35 1.89 1.46 2.65 1-3 2.01 1.36 1.89 2.95 10 1-4 2.36 1.83 1.51 2.66 1-5 1.96 0.65 1.24 2.39 1-6 1.36 0.53 1.84 1.99 1-7 1.69 0.96 0.62 1.85 5 1-8 0.97 1.86 1.29 2.31 1-9 2.32 2.45 2.94 2.88 1-10 1.67 2.77 3.97 2.93 2 20 2-1 2.56 1.72 1.37 2.97 2-2 2.31 1.45 1.36 3.11 2-3 1.96 0.50 0.43 2.75 10 2-4 1.32 1.59 0.69 2.39 2-5 1.66 2.35 1.48 1.85 2-6 1.25 1.87 1.70 1.69 2-7 2.31 0.31 1.63 2.63 5 2-8 1.98 3.12 2.97 2.85 2-9 1.66 1.51 1.14 1.76 2-10 0.99 0.68 1.34 1.99 均值 1.83 1.56 1.60 2.45 表 6 模型1求解时间
Table 6. Solution times of model 1
类型 数量 算例 时间/s 本文算法 Gurobi* Gurobi 专家 1 20 1-1 634.74 466.02 3 376.02 1 932.36 1-2 656.11 256.75 3 601.94 1 853.44 1-3 495.68 254.05 3 601.39 1 736.66 10 1-4 84.32 2.56 4.13 1 803.65 1-5 60.74 3.65 7.32 1 855.32 1-6 71.66 2.68 4.22 1 400.93 1-7 56.98 1.63 3.33 1 362.57 5 1-8 20.14 1.06 0.35 1 001.44 1-9 24.96 1.23 0.78 957.32 1-10 20.12 0.67 0.11 838.53 2 20 2-1 556.65 1.67 2.07 2 100.65 2-2 514.20 230.45 3 600.84 1 988.32 2-3 537.78 357.36 3 601.44 2 045.66 10 2-4 95.31 3.43 5.71 1 700.63 2-5 84.69 0.78 0.44 1 756.92 2-6 74.87 19.69 205.05 1 823.99 2-7 63.98 0.39 0.81 1 423.55 5 2-8 32.01 1.98 0.43 639.75 2-9 19.36 0.69 0.94 800.54 2-10 22.54 0.67 0.86 755.44 均值 206.34 80.37 900.91 1 488.89 表 7 模型2装载量
Table 7. Payloads of model 2
类型 数量 算例 装载量/kg 本文算法 Gurobi* Gurobi 专家 1 20 1-1 22 135 23 924 23 924 25 536 1-2 27 235 28 740 28 740 24 262 1-3 29 022 30 697 30 697 28 635 10 1-4 27 493 28 819 28 819 26 543 1-5 21 208 22 532 22 532 18 552 1-6 22 412 22 365 22 365 18 435 1-7 27 452 29 874 29 874 26 792 5 1-8 10 078 11 963 11 963 9 654 1-9 11 189 8 563 8 563 6 065 1-10 4 630 3 560 3 560 4 120 2 20 2-1 28 742 30 022 30 022 28 541 2-2 20 023 22 434 22 434 28 455 2-3 26 354 29 977 29 977 28 436 10 2-4 23 301 23 222 23 222 20 013 2-5 10 014 11 379 11 379 9 325 2-6 19 783 21 410 21 410 18 652 2-7 13 620 15 859 15 859 13 204 5 2-8 5 961 5 283 5 283 7 836 2-9 18 238 18 238 18 238 18 238 2-10 8 584 8 584 8 584 8 584 均值 18 874 19 872 19 872 18 494 表 8 模型2货舱面积利用率
Table 8. Cargo hold area utilization rates of model 2
类型 数量 算例 货舱面积利用率/% 本文算法 Gurobi* Gurobi 专家 1 20 1-1 75.63 80.00 80.00 70.64 1-2 72.63 74.75 74.75 67.15 1-3 65.32 67.07 67.07 66.35 10 1-4 72.36 67.15 67.15 63.26 1-5 63.25 70.63 70.63 60.32 1-6 71.36 72.54 72.54 68.58 1-7 83.45 74.34 74.34 75.65 5 1-8 64.98 70.71 70.71 74.55 1-9 81.58 42.63 42.63 37.32 1-10 69.77 25.75 25.75 58.64 2 20 2-1 73.69 69.35 69.35 58.95 2-2 64.88 58.91 58.91 60.78 2-3 60.16 66.11 66.11 65.94 10 2-4 76.58 83.20 83.20 75.12 2-5 77.66 57.91 57.91 66.91 2-6 83.46 79.59 79.59 74.32 2-7 74.64 63.99 63.99 60.88 5 2-8 59.78 46.79 46.79 52.65 2-9 73.24 73.24 73.24 73.24 2-10 72.99 72.99 72.99 72.99 均值 71.87 65.88 65.88 65.21 表 9 模型2重心偏差
Table 9. CG deviations of model 2
类型 数量 算例 重心偏差/%MAC 本文算法 Gurobi* Gurobi 专家 1 20 1-1 2.01 3.99 1.07 2.66 1-2 2.77 1.53 1.46 3.51 1-3 2.64 1.89 1.89 2.77 10 1-4 2.02 1.86 1.51 2.44 1-5 2.77 1.36 1.58 2.57 1-6 3.72 1.87 2.21 2.01 1-7 1.69 1.23 1.75 2.47 5 1-8 3.70 0.89 1.32 2.78 1-9 1.51 2.36 1.85 2.97 1-10 3.64 1.85 1.27 3.54 2 20 2-1 2.99 1.87 1.34 3.65 2-2 3.73 3.95 3.25 2.78 2-3 3.62 1.98 2.78 2.96 10 2-4 3.37 1.23 1.45 2.53 2-5 2.97 3.45 2.97 2.85 2-6 3.86 1.34 1.66 2.99 2-7 3.72 2.03 1.65 2.52 5 2-8 1.45 2.01 2.34 3.63 2-9 1.82 1.93 1.07 2.35 2-10 1.12 2.93 2.85 1.87 均值 2.76 2.08 1.86 2.79 表 10 模型2求解时间
Table 10. Solution times of model 2
类型 数量 算例 时间/s 本文算法 Gurobi* Gurobi 专家 1 20 1-1 736.56 200.02 3 602.61 1 935.36 1-2 591.44 107.42 3 601.94 1 960.79 1-3 391.83 478.54 3 601.59 1 803.41 10 1-4 88.45 3.60 1.20 1 432.84 1-5 74.21 1.47 3.61 823.16 1-6 60.92 3.90 3.74 635.53 1-7 53.29 2.56 3.12 721.54 5 1-8 43.64 1.65 2.00 536.99 1-9 14.83 2.06 2.71 588.47 1-10 40.08 2.69 2.50 698.12 2 20 2-1 347.20 134.86 3 604.73 765.64 2-2 636.04 18.44 3 601.30 2 003.69 2-3 107.90 96.71 3 601.71 2 108.74 10 2-4 103.49 6.13 3.38 753.51 2-5 44.14 0.06 1.76 798.35 2-6 14.80 161.81 11.66 678.35 2-7 38.35 0.16 1.97 699.42 5 2-8 31.88 0.84 2.43 206.58 2-9 27.24 0.31 2.89 325.78 2-10 73.15 0.42 1.55 263.41 均值 175.97 61.18 1 082.91 986.98 -
[1] HU Y Z, WANG S R, ZHANG S, et al. Review of optimization problems, models and methods for airline disruption management from 2010 to 2024[J]. Digital Transportation and Safety, 2024, 3(4): 246-263. [2] FENG B, LI Y Z, SHEN Z J M. Air cargo operations: Literature review and comparison with practices[J]. Transportation Research Part C: Emerging Technologies, 2015(56): 263-280. [3] COCHARD D D, YOST K A. Improving utilization cargo aircraft of air force[J]. Interfaces, 1985, 15(1): 53-68. [4] ANDERSON D, ORTIZ C. AALPS A knowledge-based system for aircraft loading[J]. IEEE Expert, 1987, 2(4): 71-79. [5] NG K Y K. A multicriteria optimization approach to aircraft loading[J]. Operations Research, 1992, 40(6): 1200-1205. [6] BAKER S F, MORTON D P, ROSENTHAL R E, et al. Optimizing military airlift[J]. Operations Research, 2002, 50(4): 582-602. [7] KALUZNY B L, DAVID SHAW R H A. Optimal aircraft load balancing[J]. International Transactions in Operational Research, 2009, 16(6): 767-787. [8] GUERET C, JUSSIEN N, LHOMME O. et al. Loading aircraft for military operations[J]. Journal of the Operational Research Society, 2003, 54(5): 458-465. [9] HOMSI G, JORDAN J, MARTELLO S, et al. The assignment and loading transportation problem[J]. European Journal of Operational Research, 2021, 289(3): 999-1007. [10] NANCE R L, ROESENER A G, MOORE J T. An advanced tabu search for solving the mixed payload airlift loading problem[J]. Journal of the Operational Research Society, 2011, 62(2): 337-347. [11] ROESENER A G, BARNES J W. An advanced tabu search approach to the dynamic airlift loading problem[J]. Logistics Research, 2016, 9(1): 1-18. doi: 10.3969/j.issn.1672-8882.2016.01.001 [12] HILLIARD M R, SOLANKI R S, LIU C, et al. Scheduling the operation desert storm airlift: an advanced automated scheduling support system[J]. Interfaces, 1992, 22(1): 131-146. [13] YANG C G, LIU H, GAO Y. Load planning of transport aircraft based on hybrid genetic algorithm[C]//EDP Sciences. 2018 2nd International Conference on Mechanical, Material and Aerospace Engineering, MATEC Web of Conferences. Paris: EDP Sciences, 2018: 1-6. [14] 张兵, 王瑛, 林嘉豪, 等. 混合遗传算法在大型运输机装载问题中的运用[J]. 火力与指挥控制, 2012, 37(5): 115-119.ZHANG Bing, WANG Ying, LIN Jia-hao, et al. Application of hybrid genetic algorithm in load problem of large transport[J]. Fire Control and Command Control, 2012, 37(5): 115-119. [15] 孟冲, 宋华文, 陈柏松. 基于0-1整数线性规划的军事空运装载优化算法[J]. 西南交通大学学报, 2011, 46(3): 500-505.MENG Chong, SONG Hua-wen, CHEN Bai-song. Optimization algorithm of military airlift loading based on 0-1 integer linear programming[J]. Journal of Southwest Jiaotong University, 2011, 46(3): 500-505. [16] 李高西, 陈伟坤, 万仲平, 等. 运输机群装载优化的整数规划模型[J]. 数值计算与计算机应用, 2016, 37(3): 233-244.LI Gao-xi, CHEN Wei-kun, WAN Zhong-ping, et al. Integer programming model for transport aircraft fleet cargo loading[J]. Journal on Numerical Methods and Computer Applications, 2016, 37(3): 233-244. [17] 刘宁君, 王立新, 潘文俊. 运输机群货物装载方案生成方法[J]. 北京航空航天大学学报, 2013, 39(6): 751-755.LIU Ning-jun, WANG Li-xin, PAN Wen-jun. Optimal method of transport aircraft fleet cargo loading[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(6): 751-755. [18] LIMBOURG S, SCHYNS M, LAPORTE G. Automatic aircraft cargo load planning[J]. Journal of the Operational Research Society, 2012, 63(9): 1271-1283. [19] DAHMANI N, KRICHEN S. On solving the bi-objective aircraft cargo loading problem[C]//IEEE. 2013 5th International Conference on Modeling, Simulation and Applied Optimization. New York: IEEE, 2013: 1-6. [20] LURKIN V, SCHYNS M. The airline container loading problem with pickup and delivery[J]. European Journal of Operational Research[J]. 2015, 244(3): 955-965. [21] ZHAO X L, YUAN Y, DONG Y, et al. Optimization approach to the aircraft weight and balance problem with the centre of gravity envelope constraints[J]. IET Intelligent Transport Systems, 2021, 15(10): 1269-1286. [22] ZHAO X L, DONG Y, ZUO L. A combinatorial optimization approach for air cargo palletization and aircraft loading[J]. Mathematics, 2023, 11(13): 1-16. [23] 赵向领, 左蕾. 货运飞机装箱与配载组合优化[J]. 航空动力学报, 2024, 39(11): 482-492.ZHAO Xiang-ling, ZUO Lei. Research on optimization of cargo aircraft packing and stowage combination[J]. Journal of Aerospace Power, 2024, 39(11): 482-492. [24] LODI A, MARTELLO S, MONACI M. Two-dimensional packing problems: a survey[J]. European Journal of Operational Research, 2002, 141(2): 241-252. [25] LODI A, MARTELLO S, VIGO D. Recent advances on two-dimensional bin packing problems[J]. Discrete Applied Mathematics, 2002, 123(1/2/3): 379-396. [26] MARTIN-VEGA L A. Aircraft load planning and the computer description and review[J]. Computers and Industrial Engineering, 1985, 9(4): 357-369. [27] AMIOUNY S V, BARTHOLDI J J, VANDE VATE J H, et al. Balanced loading[J]. Operations Research, 1992, 40(2): 238-246. [28] WODZIAK J R, FADEL G M. Packing and optimizing the center of gravity location using a genetic algorithm[J]. Journal of Computers in Industry, 1994(11): 2-14. [29] LARSEN O, MIKKELSEN G. An interactive system for the loading of cargo aircraft[J]. European Journal of Operational Research, 1980, 4(6): 367-373. [30] MATHUR K. An integer-programming-based heuristic for the balanced loading problem[J]. Operations Research Letters, 1998, 22(1): 19-25. [31] DAHMANI N, KRICHEN S. Solving a load balancing problem with a multi-objective particle swarm optimisation approach: application to aircraft cargo transportation[J]. International Journal of Operational Research, 2016, 27(1/2): 62-84. [32] MACALINTAL J M V, UBANDO A T. Optimal aircraft payload weight and balance using fuzzy linear programming model[J]. Chemical Engineering Transactions, 2023(103): 613-618. [33] WONG E Y, LING K K T. A mixed integer programming approach to air cargo load planning with multiple aircraft configurations and dangerous goods[C]//IEEE. 7th International Conference on Frontiers of Industrial Engineering. New York: IEEE, 2020: 123-130. [34] WONG E Y C, MO D Y, SO S. Closed-loop digital twin system for air cargo load planning operations[J]. International Journal of Computer Integrated Manufacturing, 2021, 34(7/8): 801-813. [35] DESAI J, SRIVATHSAN S, LAI W Y, et al. An optimization- based decision support tool for air cargo loading[J]. Computers and Industrial Engineering, 2023, 175: 1-13. [36] 赵向领, 李云飞, 李鹏飞. 基于改进遗传算法的航空器载重平衡[J]. 科学技术与工程, 2022, 22(33): 14951-14958.ZHAO Xiang-ling, LI Yun-fei, LI Peng-fei. Aircraft load balance based on improved genetic algorithm[J]. Science Technology and Engineering, 2022, 22(33): 14951-14958. [37] 赵向领, 李云飞. 客改货飞机载重平衡问题建模与Benders分解算法设计[J]. 交通运输工程学报, 2023, 23(2): 199-211. doi: 10.19818/j.cnki.1671-1637.2023.02.014ZHAO Xiang-ling, LI Yun-fei. Weight balance problem modeling and Benders decomposition algorithm design of preighter[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 199-211. doi: 10.19818/j.cnki.1671-1637.2023.02.014 [38] 赵向领, 李云飞, 王治宇, 等. 基于装卸顺序的中型机多航段协同配载优化[J]. 北京航空航天大学学报, 2024, 50(4): 1147-1161.ZHAO Xiang-ling, LI Yun-fei, WANG Zhi-yu, et al. Cooperating loading balance optimization for medium-sized aircraft with multiple flight legs based on loading and unloading sequence[J]. Journal of Beijing University of Aeronautics and Astronautics, 2024, 50(4): 1147-1161. [39] MESQUITA A C P, SANCHES C A A. Air cargo load and route planning in pickup and delivery operations[J]. Expert Systems with Applications, 2024, 249: 1-16. [40] HEIDELBERG K R, PARNELL G S, AMES IV J E. Automated air load planning[J]. Naval Research Logistics, 1998, 45(8): 751-768. [41] LIU D S, TAN K C, HUANG S Y, et al. On solving multi objective bin packing problems using evolutionary particle swarm optimization[J]. European Journal of Operational Research, 2008, 190(2): 357-382. [42] THOMAS C, CAMPBELL K, HINES G, et al. Airbus packing at federal express[J]. Interfaces, 1998, 28(4): 21-30. [43] MONGEAU M, BES C. Optimization of aircraft container loading[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(1): 140-150. [44] CHEN C S, LEE S M, SHEN Q S. An analytical model for the container loading problem[J]. European Journal of Operational Research, 1995, 80(1): 68-76. [45] FASANO G. A MIP approach for some practical packing problems: balancing constraints and tetris-like items[J]. Quarterly Journal of the Belgian, French and Italian Operations Research Societies, 2004(2): 161-174. [46] PADBERG M. Packing small boxes into a big box[J]. Mathematical Methods of Operations Research, 2000, 52(1): 1-21. [47] PAQUAY C, SCHYNS M, LIMBOURG S. A mixed integer programming formulation for the three-dimensional bin packing problem deriving from an air cargo application[J]. International Transactions in Operational Research, 2016, 23(1/2): 187-213. [48] PAQUAY C, LIMBOURG S, SCHYNS M, et al. MIP-based constructive heuristics for the three-dimensional bin packing problem with transportation constraints[J]. International Journal of Production Research, 2018, 56(4): 1581-1592. [49] FISCHER V, WØHLK S. A logic-based Benders decomposition solution approach for two covering problems that consider the underlying transportation[J]. Computers and Operations Research, 2023, 160: 1-13. [50] BARZANJEH S, AHMADIZAR F, ARKAT J. Logic-based benders decomposition algorithm for robust parallel drone scheduling problem considering uncertain travel times for drones[J]. Transportation Research Part E: Logistics and Transportation Review, 2025, 193: 1-30. [51] HOOKER J N, OTTOSSON G. Logic-based Benders' decomposition[J]. Mathematical Programming, 2003, 96(1): 33-60. [52] BENDERS J F. Partitioning procedures for solving mixed variables programming problems[J]. Numerische Mathematik, 1962, 4(1): 238-252. [53] COTE J F, DELL'AMICO M, IORI M. Combinatorial Benders' cuts for the strip packing problem[J]. Operations Research, 2014, 62(3): 643-661. [54] 杨卫波, 王万良, 张景玲, 等. 基于遗传模拟退火算法的矩形件优化排样[J]. 计算机工程与应用, 2016, 52(7): 259-263.YANG Wei-bo, WANG Wan-liang, ZHANG Jing-ling, et al. Packing optimization of rectangles based on improved genetic annealing algorithm[J]. Computer Engineering and Applications, 2016, 52(7): 259-263. [55] 夏以冲, 陈秋莲, 宋仁坤. 基于自适应遗传模拟退火算法的矩形件排样[J]. 计算机工程与应用, 2018, 54(22): 229-232, 245.XIA Yi-chong, CHEN Qiu-lian, SONG Ren-kun. Packing of rectangles using adaptive genetic simulated annealing algorithm[J]. Computer Engineering and Applications, 2018, 54(22): 229-232, 245. [56] 牛秦玉, 李博. 基于模拟退火遗传算法的全向AGV路径规划[J]. 计算机集成制造系统, 2024, 30(10): 3730-3741.NIU Qin-yu, LI Bo. Omnidirectional AGV path planning based on simulated annealing genetic algorithm[J]. Computer Integrated Manufacturing Systems, 2024, 30(10): 3730-3741. [57] 郭鑫. 滚装船车辆装载布局优化与调度仿真研究[D]. 哈尔滨: 哈尔滨工程大学, 2023.GUO Xin. Simulation study on vehicle loading layout optimization and scheduling on ro-ro ship[D]. Harbin: Harbin Engineering University, 2023. [58] 宣安峰. 基于机器视觉的石板缺陷检测及排样方案研究[D]. 南昌: 南昌大学, 2023.YI An-feng. Research on stone slabs defect detection and layout scheme based on machine vision[D]. Nanchang: Nanchang University, 2023. -
下载: