一类离散制造业生产过程中的优化问题研究
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摘要
生产调度和下料问题广泛存在于离散制造业的生产过程中,采用优化的生产调度方案安排生产是提高企业制造资源利用率的有效手段;而根据优化的配切方案进行下料是降低原材料消耗,提高企业竞争力的重要途径。当今离散制造业多品种小批量的生产方式决定了其生产调度和下料问题多为NP难题,通常无法在有效的时间内求得最优解,因此寻求近优解的方法被广泛地应用到实际的生产过程中。文章研究了一类离散制造业生产过程中的半flow shop等生产调度问题和多型材变截面一维下料问题,分别提出了改进的自适应混合遗传算法和两级分组启发式算法对问题进行求解,并将优化算法与信息系统的设计相结合,开发出了一个生产优化与执行系统。文章的主要贡献和创新点如下:
1.在对马钢车轮公司火车车轮生产线的生产过程深入研究后,提炼出了一类离散制造业生产线生产方式中广泛存在的“半flow shop”生产调度问题,该调度问题类似于flow shop但又与flow shop有差别,它允许生产线上加工的产品可以跳过一些工序。文章根据实际生产情况建立了该调度问题的数学模型,并针对该调度问题的特点提出了一种用自适应遗传算法与FCFS调度规则结合求解的混合算法。
2.提出了一种具有序依赖setup time的单机器批量生产调度问题,以总的setup time最小为目标建立了该生产调度问题的数学模型,设计了一种改进的自适应混合遗传算法求解该生产调度问题,对遗传算法的交叉和变异算子均按照该调度问题的特点进行了改进,提出一种“紧后任务选优重组交叉算子”,该算子能将调度排序上的优良性状很好地遗传到下一代群体中。用一种局部优化算法代替变异算子以提高算法的爬山能力。
3.将一类使用多种型号圆台形原料的下料问题定义为“多型材变截面一维下料问题”。建立的数学模型考虑了刀缝宽度对切割计算的影响,考虑了企业生产的实际约束,并提出一种两级分组启发式算法对问题进行求解。
4.在自适应遗传算法交叉和变异概率的计算中,引入基因信息熵的概念来计算个体差异度,克服了传统自适应遗传算法靠个体适应度来计算的不足,从而改善算法的性能。
5.在小生境遗传算法中,对个体在群体中共享程度的评价方面,引入信息熵的概念,创造出了基于信息熵理论共享机制的小生境进化环境,维护了群体的多样性,改善了遗传算法的性能。
6.将优化算法的研究成果应用到信息系统的设计中,设计开发了一类离散制造业生产优化与执行系统,并实现了与企业现有信息系统(如MES和ERP等)的无缝集成。并提出了一个基于MAS的生产调度与下料I3DSS结构框架。
Scheduling problems and cutting stock problems exist in the production process of discrete manufacturing industry widely. It is an effective measure of increasing the utilize rate of manufacture resource that the production is arranged by the optimization scheduling. The important approach of reducing the waste of raw material, enhancing the competition ability of the enterprise is that cutting stock by the optimization scheme. Now most scheduling problems and cutting stock problems are NP-hard problems because of the production fashion of multi-varietal and small batch in discrete manufacturing industry. So it is impossible to discover the exact optimal solution in a valid time, therefore people have to find a near-optimal solution to some extent instead in practice. This paper research the specific scheduilng problems and cutting stock problems existing in the production process of a discrete manufacturing industry. The improved adaptive hybrid genetic algorithms are proposed to solve the scheduling problems, and a two-phase heuristic algorithm is proposed to solve the cutting stock problem. The fruits of the research have been employed to design the production system. A optimization production and execution system is made. Concrete research the contents is as follows:
1. A semi-flow shop scheduling problem is proposed after the research on manufacturing process of the production line of train wheel in Masteel Wheel Company. This scheduling problem is similar to flow shop scheduling problem, but it is different from the flow shop scheduling problem. The jobs are allowed to pass some working procedures in the production line. The math model of this scheduling problem is upbuilt according to the real production case. A novel hybrid algorithm that combines the improved adaptive genetic algorithm with FCFS scheduling rule is proposed for solving this scheduling problem.
2. A single machine scheduling problem with batchs setup time depending on sequence is proposed. The math model to minimize setup times is upbuilt. An improved adaptive hybrid genetic algorithm is brought up for solving this scheduling problem. Several operators are redesigned according to the characteristic of this scheduling problem. Follow job optimization recombination crossover is advanced for transmitting the good character to the offspring. To improve the mountain climbing ability of the genetic algorithm, the mutation is replaced by a local optimization algorithm.
3. The shape of the material used for cutting is frustum of a cone in Masteel Wheel Company. And the material has various types. This cutting stock problem is defined as one dimensional, multi-type and variable-section material cutting stock problem. In this paper, the influence of cutting slot on calculation is considered. Optimization model will be discussed in real production constraints, and propose a two-phase heuristic algorithm to solve the problem.
4. The concept of gene entropy is used for calculation of adaptive probabilities of crossover and mutation, making the measures of the diversity of the population more accurate, so as to improve the performance of the algorithm.
5. In the niche genetic algorithm, the concept of entropy is employed to measure the extent of individuals sharing. The niche evolution entironment is set up to suppress the similar individuals to maintain large diversity of the population, improve the capability of the algorithm .
6. In Masteel Wheel Company the production optimization and execution system is designed through combining optimization decision theory, optimization algorithm with information system. This system is integrated seamlessly with existent information systems (such as MES and ERP). In addition, a structure of optimization scheduling and cutting stock I3DSS is designed based on MAS.
1.在对马钢车轮公司火车车轮生产线的生产过程深入研究后,提炼出了一类离散制造业生产线生产方式中广泛存在的“半flow shop”生产调度问题,该调度问题类似于flow shop但又与flow shop有差别,它允许生产线上加工的产品可以跳过一些工序。文章根据实际生产情况建立了该调度问题的数学模型,并针对该调度问题的特点提出了一种用自适应遗传算法与FCFS调度规则结合求解的混合算法。
2.提出了一种具有序依赖setup time的单机器批量生产调度问题,以总的setup time最小为目标建立了该生产调度问题的数学模型,设计了一种改进的自适应混合遗传算法求解该生产调度问题,对遗传算法的交叉和变异算子均按照该调度问题的特点进行了改进,提出一种“紧后任务选优重组交叉算子”,该算子能将调度排序上的优良性状很好地遗传到下一代群体中。用一种局部优化算法代替变异算子以提高算法的爬山能力。
3.将一类使用多种型号圆台形原料的下料问题定义为“多型材变截面一维下料问题”。建立的数学模型考虑了刀缝宽度对切割计算的影响,考虑了企业生产的实际约束,并提出一种两级分组启发式算法对问题进行求解。
4.在自适应遗传算法交叉和变异概率的计算中,引入基因信息熵的概念来计算个体差异度,克服了传统自适应遗传算法靠个体适应度来计算的不足,从而改善算法的性能。
5.在小生境遗传算法中,对个体在群体中共享程度的评价方面,引入信息熵的概念,创造出了基于信息熵理论共享机制的小生境进化环境,维护了群体的多样性,改善了遗传算法的性能。
6.将优化算法的研究成果应用到信息系统的设计中,设计开发了一类离散制造业生产优化与执行系统,并实现了与企业现有信息系统(如MES和ERP等)的无缝集成。并提出了一个基于MAS的生产调度与下料I3DSS结构框架。
Scheduling problems and cutting stock problems exist in the production process of discrete manufacturing industry widely. It is an effective measure of increasing the utilize rate of manufacture resource that the production is arranged by the optimization scheduling. The important approach of reducing the waste of raw material, enhancing the competition ability of the enterprise is that cutting stock by the optimization scheme. Now most scheduling problems and cutting stock problems are NP-hard problems because of the production fashion of multi-varietal and small batch in discrete manufacturing industry. So it is impossible to discover the exact optimal solution in a valid time, therefore people have to find a near-optimal solution to some extent instead in practice. This paper research the specific scheduilng problems and cutting stock problems existing in the production process of a discrete manufacturing industry. The improved adaptive hybrid genetic algorithms are proposed to solve the scheduling problems, and a two-phase heuristic algorithm is proposed to solve the cutting stock problem. The fruits of the research have been employed to design the production system. A optimization production and execution system is made. Concrete research the contents is as follows:
1. A semi-flow shop scheduling problem is proposed after the research on manufacturing process of the production line of train wheel in Masteel Wheel Company. This scheduling problem is similar to flow shop scheduling problem, but it is different from the flow shop scheduling problem. The jobs are allowed to pass some working procedures in the production line. The math model of this scheduling problem is upbuilt according to the real production case. A novel hybrid algorithm that combines the improved adaptive genetic algorithm with FCFS scheduling rule is proposed for solving this scheduling problem.
2. A single machine scheduling problem with batchs setup time depending on sequence is proposed. The math model to minimize setup times is upbuilt. An improved adaptive hybrid genetic algorithm is brought up for solving this scheduling problem. Several operators are redesigned according to the characteristic of this scheduling problem. Follow job optimization recombination crossover is advanced for transmitting the good character to the offspring. To improve the mountain climbing ability of the genetic algorithm, the mutation is replaced by a local optimization algorithm.
3. The shape of the material used for cutting is frustum of a cone in Masteel Wheel Company. And the material has various types. This cutting stock problem is defined as one dimensional, multi-type and variable-section material cutting stock problem. In this paper, the influence of cutting slot on calculation is considered. Optimization model will be discussed in real production constraints, and propose a two-phase heuristic algorithm to solve the problem.
4. The concept of gene entropy is used for calculation of adaptive probabilities of crossover and mutation, making the measures of the diversity of the population more accurate, so as to improve the performance of the algorithm.
5. In the niche genetic algorithm, the concept of entropy is employed to measure the extent of individuals sharing. The niche evolution entironment is set up to suppress the similar individuals to maintain large diversity of the population, improve the capability of the algorithm .
6. In Masteel Wheel Company the production optimization and execution system is designed through combining optimization decision theory, optimization algorithm with information system. This system is integrated seamlessly with existent information systems (such as MES and ERP). In addition, a structure of optimization scheduling and cutting stock I3DSS is designed based on MAS.
引文
[1] Graves S C.A review of production scheduling[J]. Operations Research, 1981, 29(4): 646-675.
[2] Conway. R. W., W. L Maxwell and L. M. Miller. Theory of Scheduling[M]. Addison-Wesley, Reading, Massachusetts, 1967.
[3] Graham, R. L, E. L. Lawler, J. K. Lenstra add A. H. G. Rinnooy Kan. Optimization and Approximation in Deterministic Sequencing and Scheduling[J]. A Survey, Annols of Discrete Mathematics, 1979,5: 287-326.
[4] K N McKay, V C S Wiers. Unifying the theory and practice of production scheduling[J]. Journal of Manufacturing System, 1999, 18(4): 241-255.
[5]徐俊刚,戴国忠,王宏安.生产调度理论和方法研究综述[J].计算机研究与发展, 2004, 41(2). 257-267.
[6] P.Mellor, A review of job-shop scheduling[J], Operations Research Quarterly, 1966, 17: 161-171.
[7] JoséFernando Gon?alves, Jorge Joséde Magalh?es Mendes, Maurício G.C. Resende. A hybrid genetic algorithm for the job shop scheduling problem[J]. European Journal of Operational Research, 2005, 167: 77-95.
[8] Leonardo Bedoya Valencia, Ghaitb Rabadi. A Multiagents Approach for the Job Shop Scheduling Problem with Earliness and Tardiness[C]// 1rd IEEE International Conference on Industrial Informatics (INDIN), 2003: 1217-1222.
[9] Tatsunobu Kawai, Yasutaka Fujimoto. An Efficient Combination of Dispatch Rules for Job-shop Scheduling Problem[C]// 3rd IEEE International Conference on Industrial Informatics (INDIN), 2005: 484-488.
[10] Mascis, Alessandro and Pacciarelli, Dario. Job-shop scheduling with blocking and no-wait constraints[J]. European Journal of Operational Research, 2002, 143(3): 498-517.
[11]陈伟达,达庆利.工艺路线可变的Job-Shop准时生产调度研究[J].管理工程学报, 2003, 17(1): 61–64.
[12]张万礼. Job-Shop型车间计划与调度优化[J].中国制造业信息化, 2002, 32(7): 106-108.
[13]李郝林.一种实用的JOB-SHOP生产调度算法[J].上海理工大学学报, 2002, 24(4): 333-336.
[14] Seyed Hessameddin Zegordi, Kenji Itoh, Takao Enkawa. Minimizing makespan for flow shop scheduling by combining simulated annealing with sequencing knowledge[J]. European Journal of Operational Research, 1995, 85: 515-531.
[15] Iravani, S.M.R. and Teo, C.P. Asymptotically optimal schedules for single-server flow shop problems with setup costs and times[J]. Operations Research Letters, 2005, 33(4): 421-430.
[16] Damodaran, P. and Srihari, K. Mixed integer formulation to minimize makespan in a flow shop with batch processing machines[J]. Mathematical and Computer Modelling, 2004, 40 (13): 1465-1472.
[17]庞哈利,郑秉霖,徐心和.多目标Flow Shop调度问题的改进TA求解算法[J].系统工程理论与实践, 2000, 5: 62-65.
[18]王吉波,唐恒永. Flow Shop排序问题F2|prmu|∑wj(1-e-rCj)的一个启发式算法[J].系统工程理论方法应用, 2001, 10(4): 311-314.
[19]赵传立,唐恒永. Flow Shop排序问题Fm|prmu|∑ωjCj的分枝定界法[J].应用数学与计算数学学报, 1999, 13(2): 30-36
[20] Masuda T, Ishii H, Nishida T. The mixed shop scheduling problem[J]. Discrete Appl Math, 1985, 11:175-86.
[21] Liaee MM, Emmons H. Scheduling families of jobs with setup times[J]. International Journal of Production Economics, 1997, 51:165-76.
[22] Garey M R, Graham R L, Johnson D S. Performance guarantees for scheduling algorithms[J]. Operations Research, 1978,26: 3-21.
[23] Sarin S C, Ahn S, Bishop A B. An improved branching scheme for the branch and bound procedure of scheduling n jobs on m machines to minimize total weighted flowtime[J]. Internat J Production Res, 1988, 26:1183-1191.
[24] Potts C N, van Wassenhove L N. A branch and bound algorithm for the total weighted tardiness problem[J]. Operation Research, 1985,33: 363-377.
[25] Panwalkar S S, Iskander W. A survey of scheduling rules[J]. Operations Research, 1977, 25(1): 45-61.
[26] Montazeri M, van Wassehove L N. Analysis of scheduling rules of an FMS[J]. Int J Prod Res, 1990, 28(4): 785-802.
[27] Adams J, Balas E, Zawack D. The shifting bottleneck procedure for Job Shopscheduling[J]. Management Science, 1988, 34(3): 391-401.
[28] Federico Della Croce, Roberto Tadei, Giuseppe Volta. A Genetic Algorithm for the Job Shop Problem[J]. Computers Ops Res, 1995, 22(1): 15-24.
[29] Lee C Y, Kim S J. Parallel genetic algorithms for the earliness-tardiness job scheduling problem with general penalty weights[J]. Comput Indus Engng, 1995, 28(2): 231-243.
[30]田澎,杨自厚,张嗣瀛.同顺序排序问题的模拟退火求解[J].信息与控制, 1994, 23(3): 133-139.
[31] Hisao I, Shinta M, Hideo T. Modified simulated annealing algorithms for the flow shop sequencing problem[J]. Euro J of Operation Research, 1995, 81: 388-398.
[32] Glover F. Future paths for integer programming and links to artificial intelligence[J]. Computer Operation Res, 1986, 13(5): 533-549.
[33] Taillard E. Some efficient heuristic methods for the flow shop sequencing problem[J]. Euro Operation Res, 1990, 47(1): 65-74.
[34] Manuel L, Barnes J W, Glover F. Intelligent scheduling with tabu search: An application to jobs with linear delay penalties and sequence-dependent setup costs and times[J]. Applied Intelligence, 1993, 3: 159-172.
[35] Zhou Pin, Li Xiao-ping, Zhang Hong-fang. An Ant Colony Algorithm for Job Shop Scheduling Problem[C]// Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, 2004.167-170.
[36] Vincent T’kindt.et al. An Ant Colony Optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem[J]. European Journal of Operational Research, 2002, 142: 250-257.
[37] R..et al. Hopfield Neural Network Approach for Single Machine Scheduling Problem[C]// Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 2004: 850-854.
[38] H. Wang, V. Jacob and E. Rolland. Design of efficient hybrid neural networks for flexible flow shop scheduling[J]. Expert Systems, 2003, 20(4): 208-231.
[39]杨建国,丁慧敏,李蓓智.解决多目标Flow-shop问题的生物免疫调度算法[J].机械设计与研究, 2002, 18(4):28-30.
[40]徐震浩,顾幸生.不确定条件下具有零等待的流水车间免疫调度算法[J].计算机集成制造系统, 2004, 10(10): 1247-1251
[41] Orhan Engin, Alper D?yen. A new approach to solve hybrid flow shopscheduling problems by artificial immune system[J]. Future Generation Computer Systems, 2004, 20: 1083–1095.
[42]姚建初等人.连续过程工业生产调度专家系统[J].交通与计算机, 1996, 14(1): 20– 23.
[43]陈世权等人.模糊排序专家系统及其在科研管理中的应用[J].模糊系统与数学, 2000, 14(1). 94-99.
[44]李国富,叶飞帆.基于动态阈值神经网络模型的FlowShop排序研究[J].机电工程, 2001, 18(2): 54-57.
[45] Zhanjie Wang, Yanbo Liu. A Multi-Agent Agile Scheduling System for Job-Shop Problem[C] //Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), 2006: 45-53
[46] Nathalie GRNGEON. et al. Generic Simulation Model for Hybrid Flow-shop[J]. Computers & Industrial Engineering, 1999, 37: 207-210
[47]何鸿声,张建林,刘国威.制造业车间生产调度方案的仿真[J].管理工程学报. 1997, 11(3): 162-166
[48]王宏安.实时智能技术及其在流程工业中的应用研究[D].中国科学院软件研究所,北京, 1999
[49] C L Liu, J W Layland. Scheduling algorithm for multi-programming in a hard-real-time environment[J]. Journal of the ACM. 1973, 20(1): 46-61
[50] J Stankovic, M Spuri, M D Nataleetal. Implications of classical scheduling results for real-time systems[J]. IEEE Computer, 1995, 28(6): 16-25
[51] M Dertouzos, A K Mok. Multi-processor online scheduling of hard-real-time tasks[J]. IEEE Transon Software Engineering, 1989, 15(12): 1497-1506
[52]乔颖.实时异构系统的集成动态调度算法研究[D].中国科学院软件研究所,北京, 2001
[53] C Y Lu, J Stankovic, G Tao. Design and evaluation of a feedback control EDF scheduling[C] // The 20th IEEE Real-Time Systems Symposium, Phoenix, Arizona, 1999: 56-67
[54] J Stankovic, T He,T Abdelzaher et al. Feedback control scheduling in distributed real-time systems[C]// The 22nd IEEE Real-Time SystemsSymposium, London, England, 2001: 59-70.
[55] C Y Lu, J Stankovic,G Tao et al. Feedback control real-time scheduling: Framework, modeling, and algorithms[J]. Real-Time Systems Journal, Special Issue on Control Theoretical Approaches to Real-Time Computing, 2002,23(1/2): 85-126
[56] KHOO L P, LEE S G, YIN X F. A prototype genetic algorithm: enhanced multi-objective scheduler for manufacturing systems[J]. International Journal of Advance Manufacturing Technology, 2000, 16(2): 131-138
[57] Lin Kuo-ching, Fang Ming-jhy, Tien Yi-lin, et al. The Dynamic Producing Scheduling System of # 2 Steelmaking Plant[J]. Technology & Training, 2000, 25(5): 134-146
[58]王万良,宋毅,吴启迪.求解作业车间调度问题的双倍体遗传算法与软件实现[J].计算机集成制造系统. 2004, 10(1): 65-69
[59] Dyckhoff H. A typology of cutting and packing problems[J]. European Journal of Operational Research, 1990, 44: 145-159.
[60] Gilmore P. C., Gomory R. E.. A linear programming approach to the cutting stock problem[J]. Operations Research, 1961, 91: 849-859.
[61] Gilmore P. C., Gomory R. E.. A linear programming approach to the cutting stock problem-partⅡ[J]. Operations Research, 1963, 11: 863-888.
[62] Vance, P.. Branch-and-price algorithms for the one-dimensional cutting stock problem[J]. Computational Optimization and Applications, 1998, 9: 211-228.
[63] de Carvalho J. M.V.. Exact Solution of Cuting Stock Problems Using Column Generation and Branch-and-Bound[J]. Int.Trans.Opt Res, 1998, 5: 35-44.
[64] Vanderbeck, F.. Computational study of a column generation algorithm for bin packing and cutting stock problems[J]. Mathematical Programming Series A, 1999, 86: 565-594.
[65] Monaci. M.. Algorithms for packing and scheduling problems[D]. Universita` di Bologna, 2002.
[66] Vanderbeck, F.. Exact algorithm for minimizing the number of setups in the one-dimensional cutting stock problem[J]. Operations Research, 2000, 48: 915-926.
[67] Harjunkoski I., Porn R., Westerlund T., Skrifvars H.. Different strategies for solving bilinear integer non-linear programming problems with convex transformations[J]. Computers and Chemical Engineering, 1997, 21: 487-492.
[68] Schilling G., Georgiadis M.C.. An algorithm for the determination of optimal cutting patterns[J]. Computers and Operations Research, 2002, 29: 1041-1058.
[69] Degraeve, Z., Gochet, W., Jans, R.. Alternative formulations for a layoutproblem in the fashion industry[J]. European Journal of Operational Research, 2002, 143: 80-93.
[70] W?scher G., HauBner H., Schumann H.. An improved typology of cutting and packing problems[J]. European Journal of Operational Research, 2007, 183(3): 1109-1130.
[71] Hajizadeh I., Lee C.G.. Alternative configurations for cutting machines in a tube cutting mill[J]. European Journal of Operational Research, 2007, 183(3): 1385-1396.
[72] Armbruster M.. A solution procedure for a pattern sequencing problem as part of a one-dimensional cutting stock problem in the steel industry[J]. European Journal of Operational Research, 2002, 141: 328-340.
[73] Alfieri A., van de Velde S.. Woeginger G.J.. Roll cutting in the curtain industry, or:A well-solvable allocation problem[J]. European Journal of Operational Research, 2007, 183(3): 1397-1404.
[74] Trkman P., Gradisar M.. One-dimensional cutting stock optimization in consecutive time periods[J]. European Journal of Operational Research, 2007, 179(2): 291-301.
[75] de Carvalho V.J.. Metal ALP-based approach to a two-stage cutting stock problem[J]. European Journal of operational Research, 1995, 84: 580-589.
[76] Carvalho J. M. V., Rodrigues A. J. G.. An LP-based approach to a two-stage cutting stock problem[J]. European Journal of Operational Research, 1995, 84: 580-589.
[77] Haessler R. W.. A heuristic solution to a nonlinear cutting stock problem[J]. Management Science, 1971, 17: 793-803.
[78] Haessler R.W.. Controlling cutting pattern changes in one-dimensional trim problems[J]. Operations Reasearch, 1975,23: 483-493.
[79] Sweeney E., Haessler R.W.. One-dimensional cutting stock decisions for rolls with multiple quality grades[J]. European Journal of Operational Research, 1990,44: 224-231.
[80] Gradisar M., Kljajic M., Resinovic G., Jesenko J.. A sequential heuristic procedure for one-dimensional cutting[J]. European Journal of Operational Research, 1999, 114: 557-568.
[81] Goulimis C.. Optimal solutions for the cutting stock problem[J]. European Journal of Operational Research, 1990, 44: 197-208.
[82] Yanasse H. H., Limeira M.S.. A hybrid heuristic to reduce the number of different patterns in cutting stock problems[J]. Computers & Operations Research, 2006, 33: 2744-2756.
[83] Stadtler H.. A one-dimensional cutting stock problem in the aluminium industry and its solution[J]. European Journal of Operational Reasearch, 1990, 44: 209-223.
[84] Haessler R.W., Sweeney P.E.. Cutting stock problems and solution procedures[J]. European Journal of Operation Research, 1991, 54: 141-150.
[85] Gradisar M., Resinovic G., Kljajic M.. A hybrid approach for optimization of one-dimensional cutting[J]. European Journal of Operational Reasearch, 1999, 119: 719-728.
[86] Gradisar M., Trkman. P.. A combined approach to the solution to the general one-dimensional cutting stock problem[J]. Computers & Operations Reseach, 2005, 32: 1793-1807.
[87] Sung T.C., Weng W.C., Yang C.T.. A two-stage optimization of piece arrangement for the cutting problem in shipbuilding[J]. J Marine Sci. Technol, 2004, 12(3): 175-82.
[88] Antonio J., Chauvet F., Chu C.B., Proth J.M.. The cutting stock problem with mixed objectives: Two heuristics based on dynamic programming[J]. European Journal of Operational Research, 1999, 114: 395-402.
[89] Chen Chuen-Lung S, Hart Stephen M., Tham Wai Mui. A simulated annealing heuristic for the one dimensional cutting stock problem[J]. European Journal of Operation Research, 1995, 93: 522-535.
[90] Vahrenkamp R.. Random search in the one-dimensional cutting stock problem[J]. European Journal of Operational Reasearch, 1996, 95: 191-200.
[91] Wagner,B.J.. A genetic algotithm solution for one-dimensional bundled stock cutting[J]. European Journal of Opetational Research, 1999, 117: 368-381.
[92] Liang KH, Yao X, Newton Y, Hoffman D.. A new evolutionary approach to cutting stock problems with and without contiguity[J]. Computers and Operations Research, 2002, 29(12): 1641–1659.
[93] Yang C.T., Sung T.C., Weng W.C.. An improved tabu search approach with mixed objective function for one-dimensional cutting stock problems [J]. Advances in Engineering Software, 2006, 37(8): 502–513.
[94] Umetani, S., Yagiura, M., Ibaraki, T.. One-dimensional cutting stock problemto minimize the number of different patterns[J]. European Journal of Operational Research, 2003, 146: 388–402.
[95]米洁. CAPP系统中优化下料问题的研究[J].机械, 2001, 28: 30-32.
[96]王小东,李刚,欧宗瑛.一维下料优化的一种新算法[J].大连理工大学学报, 2004, 4: 407-501.
[97]李元香,张进波,徐静雯,王琳.基于变长编码求解一维下料问题的演化算法[J].武汉大学学报(理学版), 2001, 6: 289-293.
[98]贾志欣,殷国富,胡晓兵,舒斌.一维下料方案的遗传算法优化[J].西安交通大学学报, 2002, 9: 967-970.
[99]李培勇.多规格一维型材优化下料[J].机械科学与技术, 2003, 22(11): 82-86.
[100]李培勇,王呈方,茅云生.基于基因群体的一维优化下料[J].上海交通大学学报, 2006, 6: 1015-1019.
[101]陈炼,马永生,刘光明.一维下料方案的贪心算法优化[J].南昌大学学报(工科版), 2005, 12: 71-74.
[102]王辉,朱珠,张志敏.有交货时间限制的大规模实用下料问题[J].数学的实践与认识, 2005, 7: 64-69.
[103]江盛树,杨春节,李平.基于遗传算法的造纸排产优化系统设计与实现[J].化工自动化及仪表, 2003, 30(5): 22-24.
[104]夏永胜,夏金兵,祁方.铁塔加工中角钢零件优化下料管理系统[J].电力建设, 2004, 7: 64-66.
[105]黄献清,刘建瓴,刘桂雄.面向精密机械制造的一维下料计算机辅助系统设计[J].机电产品开发与创新, 2005, 1: 64-66.
[106]饶运清,郭军,宋志刚,蔡力刚.集成钣金优化排料系统[J].制造技术与机床, 1999, 4: 35-37.
[107]蔡力刚,饶运清,郭军,吕文林.面向集中下料的钣金排样编程系统[J].华中理工大学学报, 1999, 27(6): 66-68.
[108]刘弘,曾广周,林宗楷.具有类比学习机制的优化排料系统[J].计算机辅助设计与图形学学报, 1997, 9: 436-441.
[109]曹炬.实用异型件优化排样系统的研究与开发[J].计算机工程与应用, 1999, 10: 37-40.
[110]潘叶青,吴崇峰,高雨茁.冲裁模优化排样CAD系统[J].电加工与模具, 2000, 5: 35-38.
[111]崔耀东,于洪方.钢板数控下料排样的一种优化算法[J].南京航空航天大学学报, 1999, 31: 712-715.
[112]崔耀东,雷卫宁.同尺寸矩形毛坯排样与板材尺寸的合理选择[J].航天工艺, 2000, 4: 24-27.
[113]陈锦昌,韩锷春.计算机辅助板材排料系统的研究[J].华南理工大学学报(自然科学版), 2001, 5: 42-44.
[114]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社, 1996.
[115] De Jong K A. An Analysis of the Behavior of a Class of Genetic Adaptive Systems[D]. Dissertation, University of Michigan, 1975.
[116]王凌.车间调度及其遗传算法.北京:清华大学出版社, 2003.
[117] Goldberg D E. Genetic algorithms in search, optimization, and machine learning[D]. Addison-Wesley. 1989.
[118] Davis L. Hand book of genetic algorithms[D]. New York:Van Nostrand Reinhold. 1991.
[119] Reeves CR. A genetic algorithms for flow shop sequencing[J]. Computers and Operations Research, 1995, 22(1): 5-13.
[120] Croce F D, Tadei R, Volta G. A genetic algorithms for the job-shop problem[J]. Computers and Operations Research, 1995, 22(1): 15-24.
[121] Whitley D, et. al. Scheduling problems and traveling salesman. The Genetic Edge Recombination Operator[C]//In: Proc. Of 3rd Int. Conf. on Genetic Algorithms. Morgan Kaufmann, 1989.
[122] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetical gorithm[C]// IEEE Trans Systems Man and Cybernetics, 1994: 656-667.
[123] Q. H. Wu, Y. J. Cao, J. Y. Wen, Optimal reactive power dispatch using an adaptive genetic algorithm[J]. Electrical Power & Energy Systems, 1998, 20 (8): 563–569.
[124]刘冀成,胡雅毅,带基因修复策略的自适应遗传算法[J].计算机应用, 2006, 26(6): 1041-1043.
[125] Glodberg D E. Richardson J. Genetic Algorithms with Sharing for Multimodal Function Optimization[C]//In: Proc. Of 2nd Int. Conf. on Genetic Algoriths, Lawrence Erlbaum Associates, 1987: 41-49.
[126] Michael Pinedo. Scheduling-theory, algorithms and systems[M]. PreticeHall, Englewood Cliffs, New Jersey, 1995.
[127] Rubin P A, Ragatz G L. Scheduling in a sequence dependent setupenvironment with generic search[J]. Computers & Operations Research, 1995, 22(1): 85-99
[128] Gagne C, Price W L, Gravel M. Scheduling a single machine with sequence dependent setup times using ant colony optimization[R]. Quebec: University of Quebec, 2003.
[129]高洪深.决策支持系统(DSS)理论方法案例[M].清华大学出版社, 1996.
[130]黄梯云.管理信息系统(第三版)[M].高等教育出版社, 2005.
[131] E Osawa. A Scheme for Agent Collaboration in Open Multi Agent Environment[C] //Proceeding of IJCAI’s 93, 1993, 8: 352-358.
[132] Y. Shoham. Agent-Oriented Programming[J]. Artificial Intelligence, 1993, 50: 51-92.
[133] HS Nwana. Software Agent an over view[J], Knowledge Engineering Review, 1997, 58(2): 205-245.
[134] Wen-Guo Ai, Jie Sun, Hui Li, A Distributed Marketing Decision Support System Based On Multi-intelligen-agent[C]//Proceedings of the Third International Conference on Machine Learning and Cybernetics, 2004, 8: 26-29.
[2] Conway. R. W., W. L Maxwell and L. M. Miller. Theory of Scheduling[M]. Addison-Wesley, Reading, Massachusetts, 1967.
[3] Graham, R. L, E. L. Lawler, J. K. Lenstra add A. H. G. Rinnooy Kan. Optimization and Approximation in Deterministic Sequencing and Scheduling[J]. A Survey, Annols of Discrete Mathematics, 1979,5: 287-326.
[4] K N McKay, V C S Wiers. Unifying the theory and practice of production scheduling[J]. Journal of Manufacturing System, 1999, 18(4): 241-255.
[5]徐俊刚,戴国忠,王宏安.生产调度理论和方法研究综述[J].计算机研究与发展, 2004, 41(2). 257-267.
[6] P.Mellor, A review of job-shop scheduling[J], Operations Research Quarterly, 1966, 17: 161-171.
[7] JoséFernando Gon?alves, Jorge Joséde Magalh?es Mendes, Maurício G.C. Resende. A hybrid genetic algorithm for the job shop scheduling problem[J]. European Journal of Operational Research, 2005, 167: 77-95.
[8] Leonardo Bedoya Valencia, Ghaitb Rabadi. A Multiagents Approach for the Job Shop Scheduling Problem with Earliness and Tardiness[C]// 1rd IEEE International Conference on Industrial Informatics (INDIN), 2003: 1217-1222.
[9] Tatsunobu Kawai, Yasutaka Fujimoto. An Efficient Combination of Dispatch Rules for Job-shop Scheduling Problem[C]// 3rd IEEE International Conference on Industrial Informatics (INDIN), 2005: 484-488.
[10] Mascis, Alessandro and Pacciarelli, Dario. Job-shop scheduling with blocking and no-wait constraints[J]. European Journal of Operational Research, 2002, 143(3): 498-517.
[11]陈伟达,达庆利.工艺路线可变的Job-Shop准时生产调度研究[J].管理工程学报, 2003, 17(1): 61–64.
[12]张万礼. Job-Shop型车间计划与调度优化[J].中国制造业信息化, 2002, 32(7): 106-108.
[13]李郝林.一种实用的JOB-SHOP生产调度算法[J].上海理工大学学报, 2002, 24(4): 333-336.
[14] Seyed Hessameddin Zegordi, Kenji Itoh, Takao Enkawa. Minimizing makespan for flow shop scheduling by combining simulated annealing with sequencing knowledge[J]. European Journal of Operational Research, 1995, 85: 515-531.
[15] Iravani, S.M.R. and Teo, C.P. Asymptotically optimal schedules for single-server flow shop problems with setup costs and times[J]. Operations Research Letters, 2005, 33(4): 421-430.
[16] Damodaran, P. and Srihari, K. Mixed integer formulation to minimize makespan in a flow shop with batch processing machines[J]. Mathematical and Computer Modelling, 2004, 40 (13): 1465-1472.
[17]庞哈利,郑秉霖,徐心和.多目标Flow Shop调度问题的改进TA求解算法[J].系统工程理论与实践, 2000, 5: 62-65.
[18]王吉波,唐恒永. Flow Shop排序问题F2|prmu|∑wj(1-e-rCj)的一个启发式算法[J].系统工程理论方法应用, 2001, 10(4): 311-314.
[19]赵传立,唐恒永. Flow Shop排序问题Fm|prmu|∑ωjCj的分枝定界法[J].应用数学与计算数学学报, 1999, 13(2): 30-36
[20] Masuda T, Ishii H, Nishida T. The mixed shop scheduling problem[J]. Discrete Appl Math, 1985, 11:175-86.
[21] Liaee MM, Emmons H. Scheduling families of jobs with setup times[J]. International Journal of Production Economics, 1997, 51:165-76.
[22] Garey M R, Graham R L, Johnson D S. Performance guarantees for scheduling algorithms[J]. Operations Research, 1978,26: 3-21.
[23] Sarin S C, Ahn S, Bishop A B. An improved branching scheme for the branch and bound procedure of scheduling n jobs on m machines to minimize total weighted flowtime[J]. Internat J Production Res, 1988, 26:1183-1191.
[24] Potts C N, van Wassenhove L N. A branch and bound algorithm for the total weighted tardiness problem[J]. Operation Research, 1985,33: 363-377.
[25] Panwalkar S S, Iskander W. A survey of scheduling rules[J]. Operations Research, 1977, 25(1): 45-61.
[26] Montazeri M, van Wassehove L N. Analysis of scheduling rules of an FMS[J]. Int J Prod Res, 1990, 28(4): 785-802.
[27] Adams J, Balas E, Zawack D. The shifting bottleneck procedure for Job Shopscheduling[J]. Management Science, 1988, 34(3): 391-401.
[28] Federico Della Croce, Roberto Tadei, Giuseppe Volta. A Genetic Algorithm for the Job Shop Problem[J]. Computers Ops Res, 1995, 22(1): 15-24.
[29] Lee C Y, Kim S J. Parallel genetic algorithms for the earliness-tardiness job scheduling problem with general penalty weights[J]. Comput Indus Engng, 1995, 28(2): 231-243.
[30]田澎,杨自厚,张嗣瀛.同顺序排序问题的模拟退火求解[J].信息与控制, 1994, 23(3): 133-139.
[31] Hisao I, Shinta M, Hideo T. Modified simulated annealing algorithms for the flow shop sequencing problem[J]. Euro J of Operation Research, 1995, 81: 388-398.
[32] Glover F. Future paths for integer programming and links to artificial intelligence[J]. Computer Operation Res, 1986, 13(5): 533-549.
[33] Taillard E. Some efficient heuristic methods for the flow shop sequencing problem[J]. Euro Operation Res, 1990, 47(1): 65-74.
[34] Manuel L, Barnes J W, Glover F. Intelligent scheduling with tabu search: An application to jobs with linear delay penalties and sequence-dependent setup costs and times[J]. Applied Intelligence, 1993, 3: 159-172.
[35] Zhou Pin, Li Xiao-ping, Zhang Hong-fang. An Ant Colony Algorithm for Job Shop Scheduling Problem[C]// Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, 2004.167-170.
[36] Vincent T’kindt.et al. An Ant Colony Optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem[J]. European Journal of Operational Research, 2002, 142: 250-257.
[37] R..et al. Hopfield Neural Network Approach for Single Machine Scheduling Problem[C]// Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 2004: 850-854.
[38] H. Wang, V. Jacob and E. Rolland. Design of efficient hybrid neural networks for flexible flow shop scheduling[J]. Expert Systems, 2003, 20(4): 208-231.
[39]杨建国,丁慧敏,李蓓智.解决多目标Flow-shop问题的生物免疫调度算法[J].机械设计与研究, 2002, 18(4):28-30.
[40]徐震浩,顾幸生.不确定条件下具有零等待的流水车间免疫调度算法[J].计算机集成制造系统, 2004, 10(10): 1247-1251
[41] Orhan Engin, Alper D?yen. A new approach to solve hybrid flow shopscheduling problems by artificial immune system[J]. Future Generation Computer Systems, 2004, 20: 1083–1095.
[42]姚建初等人.连续过程工业生产调度专家系统[J].交通与计算机, 1996, 14(1): 20– 23.
[43]陈世权等人.模糊排序专家系统及其在科研管理中的应用[J].模糊系统与数学, 2000, 14(1). 94-99.
[44]李国富,叶飞帆.基于动态阈值神经网络模型的FlowShop排序研究[J].机电工程, 2001, 18(2): 54-57.
[45] Zhanjie Wang, Yanbo Liu. A Multi-Agent Agile Scheduling System for Job-Shop Problem[C] //Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), 2006: 45-53
[46] Nathalie GRNGEON. et al. Generic Simulation Model for Hybrid Flow-shop[J]. Computers & Industrial Engineering, 1999, 37: 207-210
[47]何鸿声,张建林,刘国威.制造业车间生产调度方案的仿真[J].管理工程学报. 1997, 11(3): 162-166
[48]王宏安.实时智能技术及其在流程工业中的应用研究[D].中国科学院软件研究所,北京, 1999
[49] C L Liu, J W Layland. Scheduling algorithm for multi-programming in a hard-real-time environment[J]. Journal of the ACM. 1973, 20(1): 46-61
[50] J Stankovic, M Spuri, M D Nataleetal. Implications of classical scheduling results for real-time systems[J]. IEEE Computer, 1995, 28(6): 16-25
[51] M Dertouzos, A K Mok. Multi-processor online scheduling of hard-real-time tasks[J]. IEEE Transon Software Engineering, 1989, 15(12): 1497-1506
[52]乔颖.实时异构系统的集成动态调度算法研究[D].中国科学院软件研究所,北京, 2001
[53] C Y Lu, J Stankovic, G Tao. Design and evaluation of a feedback control EDF scheduling[C] // The 20th IEEE Real-Time Systems Symposium, Phoenix, Arizona, 1999: 56-67
[54] J Stankovic, T He,T Abdelzaher et al. Feedback control scheduling in distributed real-time systems[C]// The 22nd IEEE Real-Time SystemsSymposium, London, England, 2001: 59-70.
[55] C Y Lu, J Stankovic,G Tao et al. Feedback control real-time scheduling: Framework, modeling, and algorithms[J]. Real-Time Systems Journal, Special Issue on Control Theoretical Approaches to Real-Time Computing, 2002,23(1/2): 85-126
[56] KHOO L P, LEE S G, YIN X F. A prototype genetic algorithm: enhanced multi-objective scheduler for manufacturing systems[J]. International Journal of Advance Manufacturing Technology, 2000, 16(2): 131-138
[57] Lin Kuo-ching, Fang Ming-jhy, Tien Yi-lin, et al. The Dynamic Producing Scheduling System of # 2 Steelmaking Plant[J]. Technology & Training, 2000, 25(5): 134-146
[58]王万良,宋毅,吴启迪.求解作业车间调度问题的双倍体遗传算法与软件实现[J].计算机集成制造系统. 2004, 10(1): 65-69
[59] Dyckhoff H. A typology of cutting and packing problems[J]. European Journal of Operational Research, 1990, 44: 145-159.
[60] Gilmore P. C., Gomory R. E.. A linear programming approach to the cutting stock problem[J]. Operations Research, 1961, 91: 849-859.
[61] Gilmore P. C., Gomory R. E.. A linear programming approach to the cutting stock problem-partⅡ[J]. Operations Research, 1963, 11: 863-888.
[62] Vance, P.. Branch-and-price algorithms for the one-dimensional cutting stock problem[J]. Computational Optimization and Applications, 1998, 9: 211-228.
[63] de Carvalho J. M.V.. Exact Solution of Cuting Stock Problems Using Column Generation and Branch-and-Bound[J]. Int.Trans.Opt Res, 1998, 5: 35-44.
[64] Vanderbeck, F.. Computational study of a column generation algorithm for bin packing and cutting stock problems[J]. Mathematical Programming Series A, 1999, 86: 565-594.
[65] Monaci. M.. Algorithms for packing and scheduling problems[D]. Universita` di Bologna, 2002.
[66] Vanderbeck, F.. Exact algorithm for minimizing the number of setups in the one-dimensional cutting stock problem[J]. Operations Research, 2000, 48: 915-926.
[67] Harjunkoski I., Porn R., Westerlund T., Skrifvars H.. Different strategies for solving bilinear integer non-linear programming problems with convex transformations[J]. Computers and Chemical Engineering, 1997, 21: 487-492.
[68] Schilling G., Georgiadis M.C.. An algorithm for the determination of optimal cutting patterns[J]. Computers and Operations Research, 2002, 29: 1041-1058.
[69] Degraeve, Z., Gochet, W., Jans, R.. Alternative formulations for a layoutproblem in the fashion industry[J]. European Journal of Operational Research, 2002, 143: 80-93.
[70] W?scher G., HauBner H., Schumann H.. An improved typology of cutting and packing problems[J]. European Journal of Operational Research, 2007, 183(3): 1109-1130.
[71] Hajizadeh I., Lee C.G.. Alternative configurations for cutting machines in a tube cutting mill[J]. European Journal of Operational Research, 2007, 183(3): 1385-1396.
[72] Armbruster M.. A solution procedure for a pattern sequencing problem as part of a one-dimensional cutting stock problem in the steel industry[J]. European Journal of Operational Research, 2002, 141: 328-340.
[73] Alfieri A., van de Velde S.. Woeginger G.J.. Roll cutting in the curtain industry, or:A well-solvable allocation problem[J]. European Journal of Operational Research, 2007, 183(3): 1397-1404.
[74] Trkman P., Gradisar M.. One-dimensional cutting stock optimization in consecutive time periods[J]. European Journal of Operational Research, 2007, 179(2): 291-301.
[75] de Carvalho V.J.. Metal ALP-based approach to a two-stage cutting stock problem[J]. European Journal of operational Research, 1995, 84: 580-589.
[76] Carvalho J. M. V., Rodrigues A. J. G.. An LP-based approach to a two-stage cutting stock problem[J]. European Journal of Operational Research, 1995, 84: 580-589.
[77] Haessler R. W.. A heuristic solution to a nonlinear cutting stock problem[J]. Management Science, 1971, 17: 793-803.
[78] Haessler R.W.. Controlling cutting pattern changes in one-dimensional trim problems[J]. Operations Reasearch, 1975,23: 483-493.
[79] Sweeney E., Haessler R.W.. One-dimensional cutting stock decisions for rolls with multiple quality grades[J]. European Journal of Operational Research, 1990,44: 224-231.
[80] Gradisar M., Kljajic M., Resinovic G., Jesenko J.. A sequential heuristic procedure for one-dimensional cutting[J]. European Journal of Operational Research, 1999, 114: 557-568.
[81] Goulimis C.. Optimal solutions for the cutting stock problem[J]. European Journal of Operational Research, 1990, 44: 197-208.
[82] Yanasse H. H., Limeira M.S.. A hybrid heuristic to reduce the number of different patterns in cutting stock problems[J]. Computers & Operations Research, 2006, 33: 2744-2756.
[83] Stadtler H.. A one-dimensional cutting stock problem in the aluminium industry and its solution[J]. European Journal of Operational Reasearch, 1990, 44: 209-223.
[84] Haessler R.W., Sweeney P.E.. Cutting stock problems and solution procedures[J]. European Journal of Operation Research, 1991, 54: 141-150.
[85] Gradisar M., Resinovic G., Kljajic M.. A hybrid approach for optimization of one-dimensional cutting[J]. European Journal of Operational Reasearch, 1999, 119: 719-728.
[86] Gradisar M., Trkman. P.. A combined approach to the solution to the general one-dimensional cutting stock problem[J]. Computers & Operations Reseach, 2005, 32: 1793-1807.
[87] Sung T.C., Weng W.C., Yang C.T.. A two-stage optimization of piece arrangement for the cutting problem in shipbuilding[J]. J Marine Sci. Technol, 2004, 12(3): 175-82.
[88] Antonio J., Chauvet F., Chu C.B., Proth J.M.. The cutting stock problem with mixed objectives: Two heuristics based on dynamic programming[J]. European Journal of Operational Research, 1999, 114: 395-402.
[89] Chen Chuen-Lung S, Hart Stephen M., Tham Wai Mui. A simulated annealing heuristic for the one dimensional cutting stock problem[J]. European Journal of Operation Research, 1995, 93: 522-535.
[90] Vahrenkamp R.. Random search in the one-dimensional cutting stock problem[J]. European Journal of Operational Reasearch, 1996, 95: 191-200.
[91] Wagner,B.J.. A genetic algotithm solution for one-dimensional bundled stock cutting[J]. European Journal of Opetational Research, 1999, 117: 368-381.
[92] Liang KH, Yao X, Newton Y, Hoffman D.. A new evolutionary approach to cutting stock problems with and without contiguity[J]. Computers and Operations Research, 2002, 29(12): 1641–1659.
[93] Yang C.T., Sung T.C., Weng W.C.. An improved tabu search approach with mixed objective function for one-dimensional cutting stock problems [J]. Advances in Engineering Software, 2006, 37(8): 502–513.
[94] Umetani, S., Yagiura, M., Ibaraki, T.. One-dimensional cutting stock problemto minimize the number of different patterns[J]. European Journal of Operational Research, 2003, 146: 388–402.
[95]米洁. CAPP系统中优化下料问题的研究[J].机械, 2001, 28: 30-32.
[96]王小东,李刚,欧宗瑛.一维下料优化的一种新算法[J].大连理工大学学报, 2004, 4: 407-501.
[97]李元香,张进波,徐静雯,王琳.基于变长编码求解一维下料问题的演化算法[J].武汉大学学报(理学版), 2001, 6: 289-293.
[98]贾志欣,殷国富,胡晓兵,舒斌.一维下料方案的遗传算法优化[J].西安交通大学学报, 2002, 9: 967-970.
[99]李培勇.多规格一维型材优化下料[J].机械科学与技术, 2003, 22(11): 82-86.
[100]李培勇,王呈方,茅云生.基于基因群体的一维优化下料[J].上海交通大学学报, 2006, 6: 1015-1019.
[101]陈炼,马永生,刘光明.一维下料方案的贪心算法优化[J].南昌大学学报(工科版), 2005, 12: 71-74.
[102]王辉,朱珠,张志敏.有交货时间限制的大规模实用下料问题[J].数学的实践与认识, 2005, 7: 64-69.
[103]江盛树,杨春节,李平.基于遗传算法的造纸排产优化系统设计与实现[J].化工自动化及仪表, 2003, 30(5): 22-24.
[104]夏永胜,夏金兵,祁方.铁塔加工中角钢零件优化下料管理系统[J].电力建设, 2004, 7: 64-66.
[105]黄献清,刘建瓴,刘桂雄.面向精密机械制造的一维下料计算机辅助系统设计[J].机电产品开发与创新, 2005, 1: 64-66.
[106]饶运清,郭军,宋志刚,蔡力刚.集成钣金优化排料系统[J].制造技术与机床, 1999, 4: 35-37.
[107]蔡力刚,饶运清,郭军,吕文林.面向集中下料的钣金排样编程系统[J].华中理工大学学报, 1999, 27(6): 66-68.
[108]刘弘,曾广周,林宗楷.具有类比学习机制的优化排料系统[J].计算机辅助设计与图形学学报, 1997, 9: 436-441.
[109]曹炬.实用异型件优化排样系统的研究与开发[J].计算机工程与应用, 1999, 10: 37-40.
[110]潘叶青,吴崇峰,高雨茁.冲裁模优化排样CAD系统[J].电加工与模具, 2000, 5: 35-38.
[111]崔耀东,于洪方.钢板数控下料排样的一种优化算法[J].南京航空航天大学学报, 1999, 31: 712-715.
[112]崔耀东,雷卫宁.同尺寸矩形毛坯排样与板材尺寸的合理选择[J].航天工艺, 2000, 4: 24-27.
[113]陈锦昌,韩锷春.计算机辅助板材排料系统的研究[J].华南理工大学学报(自然科学版), 2001, 5: 42-44.
[114]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社, 1996.
[115] De Jong K A. An Analysis of the Behavior of a Class of Genetic Adaptive Systems[D]. Dissertation, University of Michigan, 1975.
[116]王凌.车间调度及其遗传算法.北京:清华大学出版社, 2003.
[117] Goldberg D E. Genetic algorithms in search, optimization, and machine learning[D]. Addison-Wesley. 1989.
[118] Davis L. Hand book of genetic algorithms[D]. New York:Van Nostrand Reinhold. 1991.
[119] Reeves CR. A genetic algorithms for flow shop sequencing[J]. Computers and Operations Research, 1995, 22(1): 5-13.
[120] Croce F D, Tadei R, Volta G. A genetic algorithms for the job-shop problem[J]. Computers and Operations Research, 1995, 22(1): 15-24.
[121] Whitley D, et. al. Scheduling problems and traveling salesman. The Genetic Edge Recombination Operator[C]//In: Proc. Of 3rd Int. Conf. on Genetic Algorithms. Morgan Kaufmann, 1989.
[122] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetical gorithm[C]// IEEE Trans Systems Man and Cybernetics, 1994: 656-667.
[123] Q. H. Wu, Y. J. Cao, J. Y. Wen, Optimal reactive power dispatch using an adaptive genetic algorithm[J]. Electrical Power & Energy Systems, 1998, 20 (8): 563–569.
[124]刘冀成,胡雅毅,带基因修复策略的自适应遗传算法[J].计算机应用, 2006, 26(6): 1041-1043.
[125] Glodberg D E. Richardson J. Genetic Algorithms with Sharing for Multimodal Function Optimization[C]//In: Proc. Of 2nd Int. Conf. on Genetic Algoriths, Lawrence Erlbaum Associates, 1987: 41-49.
[126] Michael Pinedo. Scheduling-theory, algorithms and systems[M]. PreticeHall, Englewood Cliffs, New Jersey, 1995.
[127] Rubin P A, Ragatz G L. Scheduling in a sequence dependent setupenvironment with generic search[J]. Computers & Operations Research, 1995, 22(1): 85-99
[128] Gagne C, Price W L, Gravel M. Scheduling a single machine with sequence dependent setup times using ant colony optimization[R]. Quebec: University of Quebec, 2003.
[129]高洪深.决策支持系统(DSS)理论方法案例[M].清华大学出版社, 1996.
[130]黄梯云.管理信息系统(第三版)[M].高等教育出版社, 2005.
[131] E Osawa. A Scheme for Agent Collaboration in Open Multi Agent Environment[C] //Proceeding of IJCAI’s 93, 1993, 8: 352-358.
[132] Y. Shoham. Agent-Oriented Programming[J]. Artificial Intelligence, 1993, 50: 51-92.
[133] HS Nwana. Software Agent an over view[J], Knowledge Engineering Review, 1997, 58(2): 205-245.
[134] Wen-Guo Ai, Jie Sun, Hui Li, A Distributed Marketing Decision Support System Based On Multi-intelligen-agent[C]//Proceedings of the Third International Conference on Machine Learning and Cybernetics, 2004, 8: 26-29.