航空乘务员机型执照规划与签派员生产规划研究
|
摘要
近年来,随着国民经济的迅速发展及航空市场的逐步开放,航空运输量和飞行范围日益扩大,民航运输飞行安全管理的工作越来越重要。影响航空运行安全的因素涉及飞行器/飞行辅助设备等硬件设备的可靠性技术因素、人员培训体制/各种规章和程序等人为因素、组织和管理因素等,但其中最关键的因素是：“人”。加强对“人”的管理,其关键在于对飞行安全链条上所涉及的关键技术人才的管理。航空公司飞行安全链条上涉及的主要运行业务环节有飞行员、客舱服务、地面保障、机务工程和维修、运行控制。本文以客舱服务及运行控制部门的关键技术人才：客舱乘务员和飞行签派员为研究对象,从安全角度出发,研究客舱乘务员的机型执照规划问题和飞行签派员生产规划相关的一系列问题。本文的主要研究内容如下：
一、针对航空乘务员,分别基于生产保障度最大与乘务员人均收益均衡目标,在满足生产任务需求、乘务员服务机型数量限制的条件下,研究乘务员机型执照规划问题,确定每个机型执照组合的乘务员人数。这里,机型执照组合指乘务员持有的机型执照种类的集合。本文将乘务员机型执照规划问题分解为乘务员需求估计及乘务员机型执照配置优化两个问题,并主要研究乘务员机型执照优化配置问题,该问题细分为下面两个研究内容：
(1)生产保障度最大目标下,乘务员服务机型数量限制的乘务员机型执照配置优化问题。生产保障度是机型执照方案对生产运行保障度的评价指标,生产保障度越大,该机型执照规划方案越能应对未来生产的波动性。
(2)乘务员人均收益均衡目标下,满足生产保障度和乘务员服务机型数量限制约束的乘务员机型执照配置优化问题。乘务员人均收益均衡是机型执照方案对乘务员个人收益差异的影响指标。
二、针对飞行签派员,研究的主要内容有：
(1)对飞行签派员的工作负荷进行刻画,得到签派工作负荷的高峰时段、分析具有较高峰值负荷签派席位处理的航班组成状况,为合理分配航线提供支持。
(2)将签派放行席位的工作劳动强度用该席位单位时段的工作负荷来体现,研究基于峰值负荷最小的签派放行席位任务调度问题,得到放行席位的峰值负荷最小的工作安排,为合理分配席位间的放行任务提供决策支持。
(3)给定签派放行的的席位集合、放行任务集合和任务随时间的工作负荷分布下,研究峰值负荷最小及总负荷量最小的双目标下的签派放行席位间的任务分配问题。
本文采用的方法和主要结论如下：
第一、在生产保障度目标下的乘务员机型执照配置优化问题中,本文提出一个解的构造算法,该算法对某一类问题可直接构造出最优解,对其他类问题得到与最优解差异最大为1/n0的可行解,这里n0是不同类型机型生产的需求人数的最小值。
第二、在生产保障度目标最大,人均收益均衡的客舱乘务员机型执照配置优化问题中,本文将问题抽象为每个箱子装相同数量的物品,但物品的种类各不相同的一维装箱问题,并提出一个基于LPT算法思想的贪婪算法,并得出k≤2,该算法给出最优解；k>2,最坏情况下的性能比为2-M-1的结论。这里,k是乘务员可持有的机型执照数量,M是乘务员总人数。在k>2下,又分析了k的具体取值对最坏情况性能比的影响。
第三、在基于峰值负荷最小的签派放行席位任务调度的研究问题中,本文中将问题描述为有优先序任务排序的单机排序问题,每个任务都有一个到达时间,截止期限和处理时间,目标是在不延误完成任务的前提下,使机器的峰值负荷最小,并提出了一个有效算法,且证明出该算法给出的任务安排计划是最优安排。
第四、在签派放行岗位间的任务分配问题中,本中采用两种VNS算法-VND和RVNS来解决这个问题。在求解过程中,两种领域选择方式-插入和交换类型工作被定义并用在领域搜索算法中；Vazirani提出的修正的平行机调度启发式算法被用来求解初始解。最后采用某航空公司的航班放行数据来评估我们算法的效能,案例的结果表明,VND与RVNS均能提供稳定且较好的结果。
本文的主要创新点有,第一,首次对客舱乘务员机型执照配置问题进行建模,并设计出有效算法(解构造算法和基于LPT算法思想的贪婪算法)求解；第二,首次对飞行签派员的任务配置问题进行建模,并设计最优算法求解。
In recent years, with the rapid development of economic and the gradual liberalization of the aviation market, airline traffic volume and fight area will rapidly grow; the topic of safety management for civil aviation is more and more important. Factors which affect the safety of aviation operations involve the physical properties of the aircraft and auxiliary equipment, flight/monitoring/maintenance techniques, operating environment, weather and other uncontrollable factors and other aspects, but one of the most critical factors is:'human'. For strengthening the management of 'human', the key lies in the management of key technical personnel involved in flight safety chain. The main operational elements involved in flight safety chain are pilots, cabin services, ground support, maintenance engineering and maintenance, operational control. In this paper, we choose cabin-crew in cabin service and flight dispatchers in operational control as the research objects, and study the license planning for cabin-crew and some production planning for flight dispatchers from a security perspective. The main contents are as follows:
1. The problem on license planning for equal rank cabin-crew is:how do employees choose proper skill combinations to attain some goals, under constraintion of maximal license amout holded by one person and production demands? Here, skill combination is the set of skill types holded by one cabin-crew. Goals considered in this paper contain maximum production security level and equilibrium for individual average earnings. In this paper, we divided the problem on license planning into two problems:demand forcast and the license allocation optimization problem. Moreover, we primary study the last one, which mainly contains two contents:
(1) The license allocation optimization problem with maximum production security level based on satisfied production demand. Production security level is the evaluation of license planning on the protection degree of the production, the higer the production security level, the more possibility to cope with the volatility of future.
(2) The license allocation optimization problem on a single objective of cabin-crew personal profit equilibrium based on the feasible region of the optimal solution on problem (1). Cabin-crew personal profit equilibrium is indicators for crew personal income's differences.
2. For airline dispatchers, we primary study the following contens:
(1) Workload analysis for dispatchers. We depict release workload of one flight by the average procession time unifing the subjective workload and objective load. Then we provide some indicators for depicting the work load fluctuations of a dispatch post and the total workload degree of this post. We can get the required peak hours needed to be caused of one post and provide decision support of proper task allocation by anlysising the flights composition of that post.
(2) Using hourly-load to reflect the workload intensity thythm of one post and study task scheduling with objective of mimimum peak-load on a dispatch release post.
(3) Task allocation for dispatch release posts with two-objective:minimum peak load and total load by given post numbers, dispatch jobs and workload distribution over time.
The methodology and key findings in this paper are as follows:
1) On license allocation optimization problem with objective of maximum production security level, we propose a solution construction method to directly construct a feasible solution and analyze its performance. The results show that the method can get the optimal solution for a class of problem S, else get an approximate solution and the absolute difference between it and the optimal solution is at most1/no, where n0is the smallest person demand of aircafte types.
2) On license allocation optimization problem with objective of cabin-crew personal profit equilibrium, we consider this problem as the problem of balanced packing problem with items'type restriction, here, balanced packing means each bins contain equal number of items and items'type restriction means each item included in one bin has different type. This problem is an extended balanced number partitioning problem, and has wide application in the industry where one person can hold multi-skill licenses. We give a greedy algorithm based on LPT algorithm's idea and have the following conclusions:k<2, greedy algorithm get optimal solution; k>2, the performance ratio is2-M-1, here, M denotes the amount of bins.
3) On the problem of task scheduling with objective of mimimum peak-load on a dispatch release post, we use hourly-load to reflect the workload intensity thythm of one post. We descriptions this problem as a one-machine scheduling problem with n jobs. Each task has a release time, deadline and its processing time. Our goal is getting the minimum peak load of the machine, on the conditions of accomplishing it before its deadline. Under the goal, we propose an effective algorithm and give the proof of its accuracy.
4) On problem of task allocation for dispatch release posts with multi-objectives. Two types of variable neighborhood search algorithm (VNS) are used to solve the multi-objective parallel machine scheduling with classified jobs. Through computational experiments by using our VNS algorithm, good performance was displayed in generating a variety of Pareto-optimal schedules in terms of solution quality and computational time.
The main innovations of this paper are as follows:mathematic model for cabin-crew license planning is first proposed and effective algorithms are designed for solving this problem (solution construction algorithm and a greedy algorithm based on the LPT algorithm); mathematic model of task allocation for dispathcers is first proposed and effective algorithms are designed for solving it.
一、针对航空乘务员,分别基于生产保障度最大与乘务员人均收益均衡目标,在满足生产任务需求、乘务员服务机型数量限制的条件下,研究乘务员机型执照规划问题,确定每个机型执照组合的乘务员人数。这里,机型执照组合指乘务员持有的机型执照种类的集合。本文将乘务员机型执照规划问题分解为乘务员需求估计及乘务员机型执照配置优化两个问题,并主要研究乘务员机型执照优化配置问题,该问题细分为下面两个研究内容：
(1)生产保障度最大目标下,乘务员服务机型数量限制的乘务员机型执照配置优化问题。生产保障度是机型执照方案对生产运行保障度的评价指标,生产保障度越大,该机型执照规划方案越能应对未来生产的波动性。
(2)乘务员人均收益均衡目标下,满足生产保障度和乘务员服务机型数量限制约束的乘务员机型执照配置优化问题。乘务员人均收益均衡是机型执照方案对乘务员个人收益差异的影响指标。
二、针对飞行签派员,研究的主要内容有：
(1)对飞行签派员的工作负荷进行刻画,得到签派工作负荷的高峰时段、分析具有较高峰值负荷签派席位处理的航班组成状况,为合理分配航线提供支持。
(2)将签派放行席位的工作劳动强度用该席位单位时段的工作负荷来体现,研究基于峰值负荷最小的签派放行席位任务调度问题,得到放行席位的峰值负荷最小的工作安排,为合理分配席位间的放行任务提供决策支持。
(3)给定签派放行的的席位集合、放行任务集合和任务随时间的工作负荷分布下,研究峰值负荷最小及总负荷量最小的双目标下的签派放行席位间的任务分配问题。
本文采用的方法和主要结论如下：
第一、在生产保障度目标下的乘务员机型执照配置优化问题中,本文提出一个解的构造算法,该算法对某一类问题可直接构造出最优解,对其他类问题得到与最优解差异最大为1/n0的可行解,这里n0是不同类型机型生产的需求人数的最小值。
第二、在生产保障度目标最大,人均收益均衡的客舱乘务员机型执照配置优化问题中,本文将问题抽象为每个箱子装相同数量的物品,但物品的种类各不相同的一维装箱问题,并提出一个基于LPT算法思想的贪婪算法,并得出k≤2,该算法给出最优解；k>2,最坏情况下的性能比为2-M-1的结论。这里,k是乘务员可持有的机型执照数量,M是乘务员总人数。在k>2下,又分析了k的具体取值对最坏情况性能比的影响。
第三、在基于峰值负荷最小的签派放行席位任务调度的研究问题中,本文中将问题描述为有优先序任务排序的单机排序问题,每个任务都有一个到达时间,截止期限和处理时间,目标是在不延误完成任务的前提下,使机器的峰值负荷最小,并提出了一个有效算法,且证明出该算法给出的任务安排计划是最优安排。
第四、在签派放行岗位间的任务分配问题中,本中采用两种VNS算法-VND和RVNS来解决这个问题。在求解过程中,两种领域选择方式-插入和交换类型工作被定义并用在领域搜索算法中；Vazirani提出的修正的平行机调度启发式算法被用来求解初始解。最后采用某航空公司的航班放行数据来评估我们算法的效能,案例的结果表明,VND与RVNS均能提供稳定且较好的结果。
本文的主要创新点有,第一,首次对客舱乘务员机型执照配置问题进行建模,并设计出有效算法(解构造算法和基于LPT算法思想的贪婪算法)求解；第二,首次对飞行签派员的任务配置问题进行建模,并设计最优算法求解。
In recent years, with the rapid development of economic and the gradual liberalization of the aviation market, airline traffic volume and fight area will rapidly grow; the topic of safety management for civil aviation is more and more important. Factors which affect the safety of aviation operations involve the physical properties of the aircraft and auxiliary equipment, flight/monitoring/maintenance techniques, operating environment, weather and other uncontrollable factors and other aspects, but one of the most critical factors is:'human'. For strengthening the management of 'human', the key lies in the management of key technical personnel involved in flight safety chain. The main operational elements involved in flight safety chain are pilots, cabin services, ground support, maintenance engineering and maintenance, operational control. In this paper, we choose cabin-crew in cabin service and flight dispatchers in operational control as the research objects, and study the license planning for cabin-crew and some production planning for flight dispatchers from a security perspective. The main contents are as follows:
1. The problem on license planning for equal rank cabin-crew is:how do employees choose proper skill combinations to attain some goals, under constraintion of maximal license amout holded by one person and production demands? Here, skill combination is the set of skill types holded by one cabin-crew. Goals considered in this paper contain maximum production security level and equilibrium for individual average earnings. In this paper, we divided the problem on license planning into two problems:demand forcast and the license allocation optimization problem. Moreover, we primary study the last one, which mainly contains two contents:
(1) The license allocation optimization problem with maximum production security level based on satisfied production demand. Production security level is the evaluation of license planning on the protection degree of the production, the higer the production security level, the more possibility to cope with the volatility of future.
(2) The license allocation optimization problem on a single objective of cabin-crew personal profit equilibrium based on the feasible region of the optimal solution on problem (1). Cabin-crew personal profit equilibrium is indicators for crew personal income's differences.
2. For airline dispatchers, we primary study the following contens:
(1) Workload analysis for dispatchers. We depict release workload of one flight by the average procession time unifing the subjective workload and objective load. Then we provide some indicators for depicting the work load fluctuations of a dispatch post and the total workload degree of this post. We can get the required peak hours needed to be caused of one post and provide decision support of proper task allocation by anlysising the flights composition of that post.
(2) Using hourly-load to reflect the workload intensity thythm of one post and study task scheduling with objective of mimimum peak-load on a dispatch release post.
(3) Task allocation for dispatch release posts with two-objective:minimum peak load and total load by given post numbers, dispatch jobs and workload distribution over time.
The methodology and key findings in this paper are as follows:
1) On license allocation optimization problem with objective of maximum production security level, we propose a solution construction method to directly construct a feasible solution and analyze its performance. The results show that the method can get the optimal solution for a class of problem S, else get an approximate solution and the absolute difference between it and the optimal solution is at most1/no, where n0is the smallest person demand of aircafte types.
2) On license allocation optimization problem with objective of cabin-crew personal profit equilibrium, we consider this problem as the problem of balanced packing problem with items'type restriction, here, balanced packing means each bins contain equal number of items and items'type restriction means each item included in one bin has different type. This problem is an extended balanced number partitioning problem, and has wide application in the industry where one person can hold multi-skill licenses. We give a greedy algorithm based on LPT algorithm's idea and have the following conclusions:k<2, greedy algorithm get optimal solution; k>2, the performance ratio is2-M-1, here, M denotes the amount of bins.
3) On the problem of task scheduling with objective of mimimum peak-load on a dispatch release post, we use hourly-load to reflect the workload intensity thythm of one post. We descriptions this problem as a one-machine scheduling problem with n jobs. Each task has a release time, deadline and its processing time. Our goal is getting the minimum peak load of the machine, on the conditions of accomplishing it before its deadline. Under the goal, we propose an effective algorithm and give the proof of its accuracy.
4) On problem of task allocation for dispatch release posts with multi-objectives. Two types of variable neighborhood search algorithm (VNS) are used to solve the multi-objective parallel machine scheduling with classified jobs. Through computational experiments by using our VNS algorithm, good performance was displayed in generating a variety of Pareto-optimal schedules in terms of solution quality and computational time.
The main innovations of this paper are as follows:mathematic model for cabin-crew license planning is first proposed and effective algorithms are designed for solving this problem (solution construction algorithm and a greedy algorithm based on the LPT algorithm); mathematic model of task allocation for dispathcers is first proposed and effective algorithms are designed for solving it.
引文
[1]中国民用航空局发展计划.2011.2011从统计看民航[M].中国民航出版社.
[2]陈雷廷.2008.航空公司安全危机管理研究.硕士论文.贵州大学.
[3]Wiegmann D A, Shappell S A 2011. Human error analysis of commercial aviation accidents: Application of the human factors analysis and classification system (HFACS) [J]. Aviation Space and Environmental Medicine.72(11):1006-1016
[4]Asa Holm.2008. Manpower Planning in Airlines-Mode ling and optimization [D]. Linkopings Universitet
[5]Chris A Wallace, David M. Ryan.1998. Modelling the International Flight Attendant Tour of Duty Problem [J]. The 33rd Annual Conference of the Operational Research Society, Auckland
[6]Shangyao Yan, "Yu-Ping Tu.2002. A network model for airline cabin crew scheduling [J]. European Journal of Operational Research.140(3):531-540
[7]Jesper Hansen, Tomas Liden.2005. Group Construction for Airline Cabin Crew:Comparing Constraint Programming with Branch and Price [J]. Proceedings of CPAIOR.228-242
[8]J. Konig, C. Strauss.2000. Supplement in Airline Cabin Service [J]. Information and Communication Technologies in Tourism
[9]饶云波.2006.航空乘务员排班系统的优化算法研究及设计实现[D].电子科技大学工程硕士学位论文
[10]吴漫云.2005.航空公司空勤人员需求分析和预测方案[J].航空公司管理.178：21-23
[11]David Gurwitz.1998. Fight attendants, breast cancer, and melatonin [J]. The Lancet. 353(9137):1389-1390
[12]Federal Aviation Administration.2007. Flight Attendant Fatigue, Office of Aerospace Medicine. Washington. DC 20591. DOT/FAA/AM-07/21
[13]Barayan, O S.1991. Cabin crew stress factors examined [J]. ICAO J.46(5):9-11
[14]Ono,Yuichiro; Watanabe, et al.1991. Working hours and fatigue of Japanese flight attendants (FA) [J]. Journal of Human Ergology.20(2):155-164
[15]王晶,静进.2009.女航空乘务员职业压力特点与健康的相关性[J].现代医院10：74-75
[16]高翠翠,朱丽娟,李春燕.2009.空中乘务员心理素质测量评量表的编制[J].中国健康心理学杂志.6：682-685
[17]陈秀瑾.2006.空中乘务员的工作压力管理[J].中国民用航空.11：58-60[18]中国民用航空局飞行标准司.2010.客舱乘务员服务机型数量评审指南.
[19]Agnetis A, Smriglio S.2000. Optimal assignment of high multiplicity flight plans to dispatchers [J]. Publisher:John Wiley & Sons, Inc. Journal:Naval Research Logistics. 47(5)361-376
[20]王玉文,何元清.2009.签派员复训系统的设计与开发[D].电子科技大学.硕士论文
[21]罗凤娥.2002.改进和完善我国飞行签派员培训体系[J].中国民航飞行学院学报.4：2-5
[22]戴维东,谢进一.2005.完善飞行签派员的培养和激励机制[J].中国民用航空.12：30-31
[23]付令.2007.谈淡签派员的职业训练[J].港航论坛.2：18-21
[24]张鑫,赵培会.2004.浅谈航空公司的飞行签派[J].航空知识.10：32-37
[25]王丰.2008.签派安全管理之我见[J].中国民用航空.12：62-63
[26]牟延宝.2008.谈谈签派资源管理[J].技术与管理论坛.3：32-37
[27]刘志森.2001.航空公司的飞行签派与运行成本[J].中国民航飞行学院学报.12(1)：2-3
[28]谢春生,赵煌,韩红蓉.2007.签派工作中的人为因素分析[J].中国民航飞行学院学报.18(5)：18-20
[29]滕凤云,伍文钢.2007.论如何加强企业“一专多能”人才培养[J].宝钢科技.33(1)：85-87
[30]李瑛.2003.建设“一专多能”干部队伍推行多岗资格认证[J].税收经纬.2：92-93
[31]左文卫,陈长松.2008.全业务模式下运维人员要“一专多能”[J],通信企业管理.49
[32]方准,裘定心.2006.医学设备需要“一专多能”复合型人才[J].6：48-49
[33]Chandrika B.2003. The impact of multi-skilling training on the paintshop production environment and its employees at Volkswagen South Africa (VWSA) [D]. Dissertation, Port Elizabeth Technikon.
[34]Milliman, J.,Von Glinow, M. A., Nathan, M.1991. Organizational life cycles and strategic international human resource management in multinational companies [J]. Academy of Management Review.16:318-339.
[35]Gupta, Y P.& Somers, T. M.1996. Business strategy, manufacturing flexibility, and organizational performance relationships:a path analysis approach [J]. Production and Operations Management.5 (3):204-233.
[36]Christian.C., Philippe, A.V, Georges, D.,Evelyne, V, Subjective aspects of mental workload in air-traffic control [J], pii.tls.cena.fr/docs/NR04-538.pdf
[37]Sandra, G. H., Lowell, E.S.1988. Development of NASA-TLX(Task Load Index):Results of Empirical and Theoretiacal Research [J]. In P. Hancock & N. Meshkati (Eds.), Human mental workload.239-250
[38]Donmez, B., Brzezinski, A.S., Graham, H., Cummings, M.L.,2008. Modified Cooper Harper Scales for Assessing Unmanned Vehicle Displays. MIT Humans and Automation Laboratory[J]. Citable URI:http://hdl.handle.net/1721.1/46745
[39]Ameers ing, L., Ravindra, S.G.2001. Simplified subjective workload assessment technique [J]. ERGONMICS.44(3):229-243
[40]李丹阳.2007.空中交通管制员工作负荷的队列模型分析方法[J].空中交通管制.12：21-24
[41]陈治怀.2000.空中交通管制环境中的工作负荷与能力估计[J].空中交通管理.6：25-27
[42]Arnab, M., Washington, O., John, P.,2002. Estimation of European Airspace Capacity from a Model of Controller workload [J]. THE JOURALNAVIGATION.55:381-403
[43]唐恒永,赵传立.2002.排序引论[M].科学出版社
[44]Lukasz, K, John, L., Kavita, R., Steven, S.2011. Heavy traffic analysis for EDF queues with reneging [J]. The Annals of Applied Probability.21(2):484-545
[45]Coffman, E.G.,1976. Computer and Job-Shopseheduling Theory [M]. John Wiley & Sons, Inc
[46]Edwin, S.H., Nirwan, A, Hong, R.1994. A Genetic Algorithm for MultiProeessor Scheduling [J]. IEEE Transactions On Parallel and Distributed Systems.5(2):113-120
[47]Imtiaz Ahmad, Dhodhi, M.K.,1996. MultiProcessor Scheduling in a Genetic Paradigm [J]. Parallel Computing.22(3):395-406
[48]Wang, L., Siegel, H.J.,1997. Roychowdhury, V.P., Maciejewski, A A, Task Matching and Scheduling in Heterogogeneous Computing Environments using a Genetic-Algorithm-Based Approach [J]. Journal of Parallel Computing.47:8-22
[49]Kwok, YK., Ishfaq Ahmad.1997. Efficient Schduling of Arbitrary Task Graphs to Multiprocessors using a Parallel Genetic Algorithm [J], Journal of Parallel Computing.47(1): 58-77
[50]Wang, J.C, Lu, X.D., Zeng, G.S, Huang, W.M.,1999. Heterogeneous Unified Resource Scheduling via a Fast Genetie-Annealing-Based-Algorithm [J]. In Proceedings of the International Symposiumon Future Software Technology, Nanjing, China.440-445
[51]Schoneveld, A, de Ronde, J.F., Sloot, P.M.A,1997. Task Allocation by Parallel Evolutionary Computing [J]. Journal of Parallel Computing.47(1):91-97
[52]Mansour, N., Fox, G.C.,1991. A Hybird Genetie Algorithm for Task Allocation in Multicomputers [J]. In Proceedings of the Fourth International Conference on Genetic Algor ithms.466-473
[53]Srinivous, M., Patnaik, L.M.,1994. Genetic Algorithms:A survey [J]. IEEE Computer Society.27(6):17-26
[54]Holland, J.H.,1975 Adaptation in Natural and artificial systems[M], Ann Arbor:the university of Michigan press
[55]Metropolis, N., Rosenbluth, A.,1953. Equation of state calculations by fast computing machines [J]. Journal of Chemical Physis.21:1087-1092
[56]Kirkpatrick S., Gelatt Jr C D.,Vecchi, M.P,1983. Optimization by simulated annealing [J]. Science.220:671-680
[57]Aarts, E.H.L., van Laarhoven, P.J.M.,1987. Simulated Annealing:Theory and Application [M]. Dordrecht:D Reidel Publishing Company
[58]Nourani, Y, Andresen, B.,1998. Acomparision of simulated annealing cooling strategies [J], J. Phys. A:Math. Gen.31:8373-8385
[59]Ingber L.,1993.Simulated annealing:practice versus theory [J], Mathl. Comput. Modelling. 18(11):29-57
[60]Stummer C., Sun M.,2005. New Multi-objective Metaheuristics Solution Procedures for Capital Investment Planning [J]. Journal of Heuristics.11(3):183-199.
[61]Gagne C., Gravel M., Price W.L,2005. Using Metaheuristics Compromise Programming for the Solution of Multiple-objective Scheduling Problems [J]. Journal of the Operational Research Society.56:687-698
[62]Hansen P., Mladenovia N.,1997. Variable Neighborhood Search for the P-median [J]. Location Science.5:207-226
[63]Geiger M.J,2004. Randomized Variable Neighborhood Search for Multi Objective Optimization [J]. In Proceedings of EU/ME Workshop:Design and Evaluation of Advanced Hybrid Meta-Heuristics.34-42
[64]Stein, E.S.,1985. Air traffic controller workload:An examination of workload probe. (Report No. DOT/FAA/CT-TN84/24). Atlantic City, NJ:Federal Aviation Administration Technical Center 2),1985, http://acb220.tc.faa.gov/products/bibliographic/tn8424.htm
[65]张学文.2008.基于管制员工作负荷的空域容量评估[J].空中交通管制.11：17-19
[66]Shumway, R.H., Stoffer, D.S.2011. Time Series Analysis and Its Application [M], Springer, 3rd ed
[67]潘红宇.2006.时间序列分析[M].北京：对外经济贸易大学出版社
[68]Embrey, D.,2000. TASK ANALYSIS TECHNIQUES [M]. Human Reliablity Associates Ltd
[69]JohnT., Scott, J.R.,1966. FACTOR ANALYSIS AND REGRESSION [J]. Econometrica. 34(3):552-562
[70]Kosfeld R., Lauridsen J.,2008. Factor Analysis Regression [J], Staticstical papers.49: 653-667. http://www.springerlink.com/content/j7vk1456g8486h81/fulltext.pdf
[71]Charalambous, C.,1979. Acceleration of the least pth algorithm for minimax optimization with engineering applications [J], Math. Programming,19:270-297
[72]Polyak, RA.,1988. Smooth optimization methods for minimax problems [J], SIAM J. Control and Optimization.26:1274-1286
[73]Polyak, R.A.,2000. Nonlinear rescating in discrete minimax, Nonsmooth/Nonconvex Mechanics:Modeling, Analysis, Numerical Methods [M], Gao, D., Ogden, R., Stavroulakis, G. (eds.), Kluwer Academic Publisher,2000 (with Griva, I., Sobieski, J.)
[74]Xingsi, Li.1992. An entropy-based aggregate method for minimax optimization [J], Engineering Optimimization.18:277-285
[75]Dipillo, G., Grippo, L. and Lucidi, S.1993. A smooth method for the finite minimax problem [J], Math. Programming,60:187-214
[76]L.W.Zhang and H.W.Tang.1997. A maximum entropy algorithm with parameters for solving minimax problem [J]. Archives of Control Sciences.6:47-59
[77]S.X. He.2005. A dual algorithm for minimax problems [J], J.Appl. Math & Comuputing.17: 401-418
[78]Rustem, B. and Nguyen, Q.,1998. An algorithm for the inequality-constrained discrete min-max problem [J], SIAM J. Optim,8:265-283
[79]Edward, S.P.,1976. A Lagrange multiplier method for certain constrained min-max problems [J], Operations Research,24:70-91
[80]Charnsethikul, P.,2000. The constrained minimax linear assignment problem [J], Optimization Meth.& Soft,14:37-48
[81]DUTTA, S.R.K.,1977. New algorithm for constrained minimax optimization [J], Mathemat ic al Programming 13,140-155
[82]Liuzzi, G., Lucidi, S. and Sciandrone, M.2006. A derivative-free algorithm for linearly constrained finite minimax problems [J], SIAM J. OPTIM,16:1054-1075
[88]Dell Amico M, Martello S.2001. Bounds for the cardinality constrained p||cmax problem [J]. Journal of Scheduling.4:123-138
[89]Baker K R.1974. Introduction to Sequencing and Scheduling [M]. Wiley, New York, ISBN: 0471045551
[90]Graham R L,1969. Bounds on Multiprocessing Timing Anomalies [J]. SIAM Journal on Applied Mathematics.17(2):416-429.
[91]Kellerer H, Woeginger G.1993. A tight bound for 3-partitioning [J]. Discrete Applied Mathematics,45:249-259.
[92]Michiel W, Korst J, Aarts E, Leeuwen J.2003. Performance ratios for the differencing method applied to balanced number partition problem [J]. In Symposium on Theoretical Aspects of Computer Science,583-595
[93]Jilian Zhang, Kyrialos M, HweeHwa P.2011 Heuristic Algorithms for Balanced Multi-Way
Number Partitioning [J], Proceeding of the Twenty-Second International Joint Conference on Artificial Intelligence.693-698
[94]张佑昌.1998.签派放行飞机的点滴的认识[J]. Air Traffic Management. (05):18-19
[95]Bratley P, Florian M,1971. Robillard P. Scheduling with earliest start and due date constraints[J]. Naval Res. Logist. Quart. 18(4):511-519.
[96]Garey M R, Johnson D S,1981. Simons B B, Tarjan R E. Scheduling unit-time tasKs with arbitrary release times and deadlines [J], SIAM J. Comput.10(2):256-269.
[97]Sadfi C, Penz B, Rapine C,2005. Blazewicz J, Formanowicz P. An improved approximation algorithm for the single machine total completion time scheduling problem with availability constraints [J], Eur. J. Oper. Res.16(1):3-10.
[98]Horn W A.1974. Some simple scheduling algorithms [J]. Naval Res. Logist. Quart. 21(1):177-185.
[99]McMahon G B, Florian M.1975. On scheduling with ready times and due dates to minimize maximum lateness [J], Oper. Res.23(3):475-482.
[100]Lageweg B J, Lenstra J K,1976. Rinnooy Kan A.H.G. Minimizing maximum lateness on one machine:computational experience and some applications [J], Statistical Neerlandica. 30(1):25-41.
[101]Lenstra J K, Rinnooy Kan A H G.1980. Complexity results for scheduling chains on a single machine [J], European J. Oper. Res.4(4):270-275.
[102]Ahmad H. S.Nejib Z,1993. Minimizing the number of tardy jobs in single machine sequencing [J], Discrete Mathematics.117(1-3):215-223.
[103]Sahni S,1976. Algorithms for Scheduling Independent TasKs [J], JACM.23(1):116-127
[104]Koulamas C,1997. Polynomially solvable total tardiness problems:Review and extensions [J]. Omega.25(2):235-239.
[105]Szwarc W,1993. Weighted Tardiness Single Machine Scheduling with Proportional Weights [J]. Management science.39(5):626-632.
[106]Kolahan F., Kayvanfar V.,2009. A Heuristic Algorithm Approach for Scheduling of Multi-criteria Unrelated Parallel Machines [J], World Academy of Science, Engineering and Technology.59:253-256.
[107]Cochran J.K., Horng S.-M., Fowler J.W.,2003. A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines [J], Computers & Operations Research 30.30(03):1087-1102.
[108]Sun H. and Wang G.,2003. Parallel Machine Earliness and Tardiness Scheduling with Proportional Weights [J]. Computers & Operations Research.30(5):801-808
[109]Sricharoentham C.,2003. Multiple-objective scheduling in parallel machines using memtic algorithm, Master Thesis in industrial engineering [D], Texas Tech University
[110]Huang D., Guo H., Qian N.2005. Hybrid Genetic Algorithm for Minimizing the Range of Lateness and Make-span on Non-identical Parallel Machines [J]. In Proceedings of IEEE International Conference on Neural Networks and Brain.1:150-154,
[111]Piotr Czyzak and Andrzej Jaszkiewicz.1998. Pareto simulated annealing - a metaheuristic technique for multiple-objective combinatorial optimization [J]. Journal of Multi-Criteria Decision Analysis.7:34-47
[112]Thomas Feo, Olivier Goldschmidit and Mallek Khellaf, One-Half Approximation Algorithms for the k-Partition Problem [J]. Operations Research,1992,40(1):S170-S173
[113]Babel L, Kellerer H, Kotov V,1998, The k-partitioning problem[J], Mathematical Methods of Operations Research,47(1):59-82
[114]He Y, Tan Zhiyi, Zhu J, Yao E,2003, K-Partitioning problems for Maximizing the Minimum Load[J], Computers and Mathematics with Applications,46:1671-1681
[115]Wu B, Yao E,2007, K-Partitioning problems with partition matroid constraint [J], Theoretical Computer Science,374:41-48
[116]Dell'Amico M, Lori M, Martello S,2004, Heuristic algorithms and scatter search for the cardinality constrained P|·|Cmax problem [J], Journal of Heuristics,169-204
[117]Chi Z, Wang G, Liu X, Liu J,2009, Approximating scheduling machines with capacity constraints [J], in:Proceedings of the Third International Frontiers of Algorithmic Workshop, 283-292
[118]Saha B, Srinivasan A,2010, A new approximation technique for resource-allocation problems [J], in:Proceedings of the First Annual Symposium on Innovations in Computer Science,342-357
[119]Kellerer H, Kotov V,2011, A 3/2-approximation algorithm for ki-partitioning, Operations Research letters[J],39:359-362
[120]工作负荷,百度百科,http://baike.baidu.com/view/1661512.htm
[121]中国国际航空公司,2008,飞行签派放行手册。
[2]陈雷廷.2008.航空公司安全危机管理研究.硕士论文.贵州大学.
[3]Wiegmann D A, Shappell S A 2011. Human error analysis of commercial aviation accidents: Application of the human factors analysis and classification system (HFACS) [J]. Aviation Space and Environmental Medicine.72(11):1006-1016
[4]Asa Holm.2008. Manpower Planning in Airlines-Mode ling and optimization [D]. Linkopings Universitet
[5]Chris A Wallace, David M. Ryan.1998. Modelling the International Flight Attendant Tour of Duty Problem [J]. The 33rd Annual Conference of the Operational Research Society, Auckland
[6]Shangyao Yan, "Yu-Ping Tu.2002. A network model for airline cabin crew scheduling [J]. European Journal of Operational Research.140(3):531-540
[7]Jesper Hansen, Tomas Liden.2005. Group Construction for Airline Cabin Crew:Comparing Constraint Programming with Branch and Price [J]. Proceedings of CPAIOR.228-242
[8]J. Konig, C. Strauss.2000. Supplement in Airline Cabin Service [J]. Information and Communication Technologies in Tourism
[9]饶云波.2006.航空乘务员排班系统的优化算法研究及设计实现[D].电子科技大学工程硕士学位论文
[10]吴漫云.2005.航空公司空勤人员需求分析和预测方案[J].航空公司管理.178：21-23
[11]David Gurwitz.1998. Fight attendants, breast cancer, and melatonin [J]. The Lancet. 353(9137):1389-1390
[12]Federal Aviation Administration.2007. Flight Attendant Fatigue, Office of Aerospace Medicine. Washington. DC 20591. DOT/FAA/AM-07/21
[13]Barayan, O S.1991. Cabin crew stress factors examined [J]. ICAO J.46(5):9-11
[14]Ono,Yuichiro; Watanabe, et al.1991. Working hours and fatigue of Japanese flight attendants (FA) [J]. Journal of Human Ergology.20(2):155-164
[15]王晶,静进.2009.女航空乘务员职业压力特点与健康的相关性[J].现代医院10：74-75
[16]高翠翠,朱丽娟,李春燕.2009.空中乘务员心理素质测量评量表的编制[J].中国健康心理学杂志.6：682-685
[17]陈秀瑾.2006.空中乘务员的工作压力管理[J].中国民用航空.11：58-60[18]中国民用航空局飞行标准司.2010.客舱乘务员服务机型数量评审指南.
[19]Agnetis A, Smriglio S.2000. Optimal assignment of high multiplicity flight plans to dispatchers [J]. Publisher:John Wiley & Sons, Inc. Journal:Naval Research Logistics. 47(5)361-376
[20]王玉文,何元清.2009.签派员复训系统的设计与开发[D].电子科技大学.硕士论文
[21]罗凤娥.2002.改进和完善我国飞行签派员培训体系[J].中国民航飞行学院学报.4：2-5
[22]戴维东,谢进一.2005.完善飞行签派员的培养和激励机制[J].中国民用航空.12：30-31
[23]付令.2007.谈淡签派员的职业训练[J].港航论坛.2：18-21
[24]张鑫,赵培会.2004.浅谈航空公司的飞行签派[J].航空知识.10：32-37
[25]王丰.2008.签派安全管理之我见[J].中国民用航空.12：62-63
[26]牟延宝.2008.谈谈签派资源管理[J].技术与管理论坛.3：32-37
[27]刘志森.2001.航空公司的飞行签派与运行成本[J].中国民航飞行学院学报.12(1)：2-3
[28]谢春生,赵煌,韩红蓉.2007.签派工作中的人为因素分析[J].中国民航飞行学院学报.18(5)：18-20
[29]滕凤云,伍文钢.2007.论如何加强企业“一专多能”人才培养[J].宝钢科技.33(1)：85-87
[30]李瑛.2003.建设“一专多能”干部队伍推行多岗资格认证[J].税收经纬.2：92-93
[31]左文卫,陈长松.2008.全业务模式下运维人员要“一专多能”[J],通信企业管理.49
[32]方准,裘定心.2006.医学设备需要“一专多能”复合型人才[J].6：48-49
[33]Chandrika B.2003. The impact of multi-skilling training on the paintshop production environment and its employees at Volkswagen South Africa (VWSA) [D]. Dissertation, Port Elizabeth Technikon.
[34]Milliman, J.,Von Glinow, M. A., Nathan, M.1991. Organizational life cycles and strategic international human resource management in multinational companies [J]. Academy of Management Review.16:318-339.
[35]Gupta, Y P.& Somers, T. M.1996. Business strategy, manufacturing flexibility, and organizational performance relationships:a path analysis approach [J]. Production and Operations Management.5 (3):204-233.
[36]Christian.C., Philippe, A.V, Georges, D.,Evelyne, V, Subjective aspects of mental workload in air-traffic control [J], pii.tls.cena.fr/docs/NR04-538.pdf
[37]Sandra, G. H., Lowell, E.S.1988. Development of NASA-TLX(Task Load Index):Results of Empirical and Theoretiacal Research [J]. In P. Hancock & N. Meshkati (Eds.), Human mental workload.239-250
[38]Donmez, B., Brzezinski, A.S., Graham, H., Cummings, M.L.,2008. Modified Cooper Harper Scales for Assessing Unmanned Vehicle Displays. MIT Humans and Automation Laboratory[J]. Citable URI:http://hdl.handle.net/1721.1/46745
[39]Ameers ing, L., Ravindra, S.G.2001. Simplified subjective workload assessment technique [J]. ERGONMICS.44(3):229-243
[40]李丹阳.2007.空中交通管制员工作负荷的队列模型分析方法[J].空中交通管制.12：21-24
[41]陈治怀.2000.空中交通管制环境中的工作负荷与能力估计[J].空中交通管理.6：25-27
[42]Arnab, M., Washington, O., John, P.,2002. Estimation of European Airspace Capacity from a Model of Controller workload [J]. THE JOURALNAVIGATION.55:381-403
[43]唐恒永,赵传立.2002.排序引论[M].科学出版社
[44]Lukasz, K, John, L., Kavita, R., Steven, S.2011. Heavy traffic analysis for EDF queues with reneging [J]. The Annals of Applied Probability.21(2):484-545
[45]Coffman, E.G.,1976. Computer and Job-Shopseheduling Theory [M]. John Wiley & Sons, Inc
[46]Edwin, S.H., Nirwan, A, Hong, R.1994. A Genetic Algorithm for MultiProeessor Scheduling [J]. IEEE Transactions On Parallel and Distributed Systems.5(2):113-120
[47]Imtiaz Ahmad, Dhodhi, M.K.,1996. MultiProcessor Scheduling in a Genetic Paradigm [J]. Parallel Computing.22(3):395-406
[48]Wang, L., Siegel, H.J.,1997. Roychowdhury, V.P., Maciejewski, A A, Task Matching and Scheduling in Heterogogeneous Computing Environments using a Genetic-Algorithm-Based Approach [J]. Journal of Parallel Computing.47:8-22
[49]Kwok, YK., Ishfaq Ahmad.1997. Efficient Schduling of Arbitrary Task Graphs to Multiprocessors using a Parallel Genetic Algorithm [J], Journal of Parallel Computing.47(1): 58-77
[50]Wang, J.C, Lu, X.D., Zeng, G.S, Huang, W.M.,1999. Heterogeneous Unified Resource Scheduling via a Fast Genetie-Annealing-Based-Algorithm [J]. In Proceedings of the International Symposiumon Future Software Technology, Nanjing, China.440-445
[51]Schoneveld, A, de Ronde, J.F., Sloot, P.M.A,1997. Task Allocation by Parallel Evolutionary Computing [J]. Journal of Parallel Computing.47(1):91-97
[52]Mansour, N., Fox, G.C.,1991. A Hybird Genetie Algorithm for Task Allocation in Multicomputers [J]. In Proceedings of the Fourth International Conference on Genetic Algor ithms.466-473
[53]Srinivous, M., Patnaik, L.M.,1994. Genetic Algorithms:A survey [J]. IEEE Computer Society.27(6):17-26
[54]Holland, J.H.,1975 Adaptation in Natural and artificial systems[M], Ann Arbor:the university of Michigan press
[55]Metropolis, N., Rosenbluth, A.,1953. Equation of state calculations by fast computing machines [J]. Journal of Chemical Physis.21:1087-1092
[56]Kirkpatrick S., Gelatt Jr C D.,Vecchi, M.P,1983. Optimization by simulated annealing [J]. Science.220:671-680
[57]Aarts, E.H.L., van Laarhoven, P.J.M.,1987. Simulated Annealing:Theory and Application [M]. Dordrecht:D Reidel Publishing Company
[58]Nourani, Y, Andresen, B.,1998. Acomparision of simulated annealing cooling strategies [J], J. Phys. A:Math. Gen.31:8373-8385
[59]Ingber L.,1993.Simulated annealing:practice versus theory [J], Mathl. Comput. Modelling. 18(11):29-57
[60]Stummer C., Sun M.,2005. New Multi-objective Metaheuristics Solution Procedures for Capital Investment Planning [J]. Journal of Heuristics.11(3):183-199.
[61]Gagne C., Gravel M., Price W.L,2005. Using Metaheuristics Compromise Programming for the Solution of Multiple-objective Scheduling Problems [J]. Journal of the Operational Research Society.56:687-698
[62]Hansen P., Mladenovia N.,1997. Variable Neighborhood Search for the P-median [J]. Location Science.5:207-226
[63]Geiger M.J,2004. Randomized Variable Neighborhood Search for Multi Objective Optimization [J]. In Proceedings of EU/ME Workshop:Design and Evaluation of Advanced Hybrid Meta-Heuristics.34-42
[64]Stein, E.S.,1985. Air traffic controller workload:An examination of workload probe. (Report No. DOT/FAA/CT-TN84/24). Atlantic City, NJ:Federal Aviation Administration Technical Center 2),1985, http://acb220.tc.faa.gov/products/bibliographic/tn8424.htm
[65]张学文.2008.基于管制员工作负荷的空域容量评估[J].空中交通管制.11：17-19
[66]Shumway, R.H., Stoffer, D.S.2011. Time Series Analysis and Its Application [M], Springer, 3rd ed
[67]潘红宇.2006.时间序列分析[M].北京：对外经济贸易大学出版社
[68]Embrey, D.,2000. TASK ANALYSIS TECHNIQUES [M]. Human Reliablity Associates Ltd
[69]JohnT., Scott, J.R.,1966. FACTOR ANALYSIS AND REGRESSION [J]. Econometrica. 34(3):552-562
[70]Kosfeld R., Lauridsen J.,2008. Factor Analysis Regression [J], Staticstical papers.49: 653-667. http://www.springerlink.com/content/j7vk1456g8486h81/fulltext.pdf
[71]Charalambous, C.,1979. Acceleration of the least pth algorithm for minimax optimization with engineering applications [J], Math. Programming,19:270-297
[72]Polyak, RA.,1988. Smooth optimization methods for minimax problems [J], SIAM J. Control and Optimization.26:1274-1286
[73]Polyak, R.A.,2000. Nonlinear rescating in discrete minimax, Nonsmooth/Nonconvex Mechanics:Modeling, Analysis, Numerical Methods [M], Gao, D., Ogden, R., Stavroulakis, G. (eds.), Kluwer Academic Publisher,2000 (with Griva, I., Sobieski, J.)
[74]Xingsi, Li.1992. An entropy-based aggregate method for minimax optimization [J], Engineering Optimimization.18:277-285
[75]Dipillo, G., Grippo, L. and Lucidi, S.1993. A smooth method for the finite minimax problem [J], Math. Programming,60:187-214
[76]L.W.Zhang and H.W.Tang.1997. A maximum entropy algorithm with parameters for solving minimax problem [J]. Archives of Control Sciences.6:47-59
[77]S.X. He.2005. A dual algorithm for minimax problems [J], J.Appl. Math & Comuputing.17: 401-418
[78]Rustem, B. and Nguyen, Q.,1998. An algorithm for the inequality-constrained discrete min-max problem [J], SIAM J. Optim,8:265-283
[79]Edward, S.P.,1976. A Lagrange multiplier method for certain constrained min-max problems [J], Operations Research,24:70-91
[80]Charnsethikul, P.,2000. The constrained minimax linear assignment problem [J], Optimization Meth.& Soft,14:37-48
[81]DUTTA, S.R.K.,1977. New algorithm for constrained minimax optimization [J], Mathemat ic al Programming 13,140-155
[82]Liuzzi, G., Lucidi, S. and Sciandrone, M.2006. A derivative-free algorithm for linearly constrained finite minimax problems [J], SIAM J. OPTIM,16:1054-1075
[88]Dell Amico M, Martello S.2001. Bounds for the cardinality constrained p||cmax problem [J]. Journal of Scheduling.4:123-138
[89]Baker K R.1974. Introduction to Sequencing and Scheduling [M]. Wiley, New York, ISBN: 0471045551
[90]Graham R L,1969. Bounds on Multiprocessing Timing Anomalies [J]. SIAM Journal on Applied Mathematics.17(2):416-429.
[91]Kellerer H, Woeginger G.1993. A tight bound for 3-partitioning [J]. Discrete Applied Mathematics,45:249-259.
[92]Michiel W, Korst J, Aarts E, Leeuwen J.2003. Performance ratios for the differencing method applied to balanced number partition problem [J]. In Symposium on Theoretical Aspects of Computer Science,583-595
[93]Jilian Zhang, Kyrialos M, HweeHwa P.2011 Heuristic Algorithms for Balanced Multi-Way
Number Partitioning [J], Proceeding of the Twenty-Second International Joint Conference on Artificial Intelligence.693-698
[94]张佑昌.1998.签派放行飞机的点滴的认识[J]. Air Traffic Management. (05):18-19
[95]Bratley P, Florian M,1971. Robillard P. Scheduling with earliest start and due date constraints[J]. Naval Res. Logist. Quart. 18(4):511-519.
[96]Garey M R, Johnson D S,1981. Simons B B, Tarjan R E. Scheduling unit-time tasKs with arbitrary release times and deadlines [J], SIAM J. Comput.10(2):256-269.
[97]Sadfi C, Penz B, Rapine C,2005. Blazewicz J, Formanowicz P. An improved approximation algorithm for the single machine total completion time scheduling problem with availability constraints [J], Eur. J. Oper. Res.16(1):3-10.
[98]Horn W A.1974. Some simple scheduling algorithms [J]. Naval Res. Logist. Quart. 21(1):177-185.
[99]McMahon G B, Florian M.1975. On scheduling with ready times and due dates to minimize maximum lateness [J], Oper. Res.23(3):475-482.
[100]Lageweg B J, Lenstra J K,1976. Rinnooy Kan A.H.G. Minimizing maximum lateness on one machine:computational experience and some applications [J], Statistical Neerlandica. 30(1):25-41.
[101]Lenstra J K, Rinnooy Kan A H G.1980. Complexity results for scheduling chains on a single machine [J], European J. Oper. Res.4(4):270-275.
[102]Ahmad H. S.Nejib Z,1993. Minimizing the number of tardy jobs in single machine sequencing [J], Discrete Mathematics.117(1-3):215-223.
[103]Sahni S,1976. Algorithms for Scheduling Independent TasKs [J], JACM.23(1):116-127
[104]Koulamas C,1997. Polynomially solvable total tardiness problems:Review and extensions [J]. Omega.25(2):235-239.
[105]Szwarc W,1993. Weighted Tardiness Single Machine Scheduling with Proportional Weights [J]. Management science.39(5):626-632.
[106]Kolahan F., Kayvanfar V.,2009. A Heuristic Algorithm Approach for Scheduling of Multi-criteria Unrelated Parallel Machines [J], World Academy of Science, Engineering and Technology.59:253-256.
[107]Cochran J.K., Horng S.-M., Fowler J.W.,2003. A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines [J], Computers & Operations Research 30.30(03):1087-1102.
[108]Sun H. and Wang G.,2003. Parallel Machine Earliness and Tardiness Scheduling with Proportional Weights [J]. Computers & Operations Research.30(5):801-808
[109]Sricharoentham C.,2003. Multiple-objective scheduling in parallel machines using memtic algorithm, Master Thesis in industrial engineering [D], Texas Tech University
[110]Huang D., Guo H., Qian N.2005. Hybrid Genetic Algorithm for Minimizing the Range of Lateness and Make-span on Non-identical Parallel Machines [J]. In Proceedings of IEEE International Conference on Neural Networks and Brain.1:150-154,
[111]Piotr Czyzak and Andrzej Jaszkiewicz.1998. Pareto simulated annealing - a metaheuristic technique for multiple-objective combinatorial optimization [J]. Journal of Multi-Criteria Decision Analysis.7:34-47
[112]Thomas Feo, Olivier Goldschmidit and Mallek Khellaf, One-Half Approximation Algorithms for the k-Partition Problem [J]. Operations Research,1992,40(1):S170-S173
[113]Babel L, Kellerer H, Kotov V,1998, The k-partitioning problem[J], Mathematical Methods of Operations Research,47(1):59-82
[114]He Y, Tan Zhiyi, Zhu J, Yao E,2003, K-Partitioning problems for Maximizing the Minimum Load[J], Computers and Mathematics with Applications,46:1671-1681
[115]Wu B, Yao E,2007, K-Partitioning problems with partition matroid constraint [J], Theoretical Computer Science,374:41-48
[116]Dell'Amico M, Lori M, Martello S,2004, Heuristic algorithms and scatter search for the cardinality constrained P|·|Cmax problem [J], Journal of Heuristics,169-204
[117]Chi Z, Wang G, Liu X, Liu J,2009, Approximating scheduling machines with capacity constraints [J], in:Proceedings of the Third International Frontiers of Algorithmic Workshop, 283-292
[118]Saha B, Srinivasan A,2010, A new approximation technique for resource-allocation problems [J], in:Proceedings of the First Annual Symposium on Innovations in Computer Science,342-357
[119]Kellerer H, Kotov V,2011, A 3/2-approximation algorithm for ki-partitioning, Operations Research letters[J],39:359-362
[120]工作负荷,百度百科,http://baike.baidu.com/view/1661512.htm
[121]中国国际航空公司,2008,飞行签派放行手册。