实时系统中最急优先调度策略的二进表示模型

本文提出一个实时系统中最急优先调度策略的二进表示Markov过程模型。以减少软实时系统中任务损失率为衡量标准,最急优先调度策略被证明是一个最佳策略,但其效率却极难分析。本文的模型使这一策略的分析变得可行和简单。基于本文的理论,已得到一系列有关最急优先策略的重要的性能评价分析结果。     

超载实时系统的算法

近年来,超载实时系统ｏｎ－ｌｉｎｅ排序已被许多作者研究过,对单台机问题,Ｓ．Ｂａｒｕａｈ等给出了一个最坏性能比的上界１／（１＋√Ｋ）~２,其中Ｋ为重要性比,Ｇ．Ｋｏｒｅｎ等提出了一个达到此界的最优算法（Ｄ－ｏｖｅｒ算法）,对多台机问题,Ｍ． Ｄｅｒｔｏｕｚｏｓ等证明即使在非超载情况下也不存在最优算法． Ｆ． Ｗａｎｇ等证明不可能存在竞争因子大于１／２的算法,并提出了一个算法,在Ｋ＝１及没有松弛时问的条件下,达到了１／２．对一般情况,他们证明任一Ｏｎ－ｌｉｎｅ算法的竞争因子均不会大于１／ａ,其中．对多台机情况,虽已发现了一些有用的性质,但如何构造一个较好的算法仍有待于进一步的研究．     

两台机器超载实时系统的On-line算法

对超载实时系统的On—line算法中的SR算法作了修改，提出了NSR算法，并证明NSR算法的竞争比至少为2／5，因而它比SR算法更为优异．     

单机单转包商调度与外包联合优化问题的改进启发式算法

针对以总完工时间与总外包费用加权和为优化目标、总外包费用不超过给定上限的单机单转包商调度与外包联合优化问题,设计出一种改进的剔除型启发式算法。该算法通过运用动态规划技术求解新的辅助问题来获取初始外包工件集,并引入判定条件提前从初始外包工件集中剔除特定工件。为满足对总外包费用的上限约束,还利用新型的启发式筛选次序族逐一确定从当前外包工件集中剔除的工件。在仿真实验中,通过生成大量的测试算例,对比分析了改进算法与另2种已报道算法在求解质量、计算时间上的表现情况。实验结果表明所提出的改进算法在解的整体质量上具备显著的比较优势,并且能在5.6秒内完成对工件总数为1500的测试算例的求解。     

基于遗传算子粒子群算法的拖轮动态调度

随着中国港口的发展,进出港口的船舶日益增多,使用拖轮的艘次逐渐增加.而当前极大部分港口所采用的基于人工经验的拖轮调度方案已难以保证船舶的顺利进出港口.如何根据复杂多变的进出港情况来制定合理的拖轮调度方案,已成为当前众多港口迫切需要解决的问题之一.通过分析港口拖轮作业过程与特点,建立了拖轮动态调度的数学模型,采用了基于动态遗传算子的改进粒子群优化算法对该模型进行求解.案例分析表明该拖轮动态调度模型是有效的.通过和传统粒子群算法对比分析,基于遗传算子的粒子群算法不仅在收敛速度上有明显的提高,而且求得的解更优.为港口拖轮动态调度的科学决策提供了依据.     

无线网络中全调度问题的一种随机分布式算法

无线网络中的全调度,要确保网络中每个节点所可能的链路信息和广播信息都能无冲突地进行传输.通过简单的构造方法,证明了多项式时间内,能找到一个长度为$O(\bigtriangleup_{\rm out}^2\bigtriangleup_{\rm in})$的全调度;并且给出了全调度问题的一种随机分布式算法,证明了这种随机分布式算法,对任意的常数$h$,~$0     

基于蝙蝠退火算法的无等待流水线调度问题研究

无等待流水线调度问题(no-wait flow shop scheduling problem,NWFSP)是一类比较重要的复杂生产调度问题,并已经被证明是典型的NP问题.蝙蝠算法(Bat algorithm,BA)是一种较新颖的群体智能算法.本文针对蝙蝠算法在求解无等待流水线调度问题上的不足,提出一种蝙蝠退火算法,它通过采用ROV的编码方式以实现离散问题的连续编码,同时为了避免算法早熟现象引入了模拟退火算法.算法采用基于NEH的局部搜索规则,在很大程度上提高了算法的性能.利用标准Car问题和Rec问题算例进行仿真实验,结果表明了改进算法的可行性和有效性.     

货运列车编组调度问题的模型与算法研究

从双向编组站运输生产实际情况出发,以最大化车站发出车数和最小化车辆在站平均停留时间(中时)为目标,综合考虑解体、编组调机能力限制、到发列车车流接续、车流在站停留时间约束的影响,建立了车站货运列车编组调度问题的多目标非线性混合整数规划模型,结合该优化模型难以求解的特点,将编组调度问题分解为配流、待解车列解体和待编车列编组三个子问题,进而设计了求解该问题的分层启发式算法,对正常和特殊运输组织条件下的列车编组调度问题进行了求解.     

动态空间调度的混合遗传算法

提出了一种基于混合遗传算法的动态空间调度方法。首先利用遗传算法产生多个可行的分段调度序列,再采用动态决定分段位置的启发式算法——平均最大空闲矩形策略对遗传算法产生的调度序列进行解码。同时以完工时间和平台利用率的加权和作为适应度函数,充分考虑了空间调度问题所特有的动态性和时空关联性。遗传进化过程收敛后得到近似最优解,实现了调度方案的全局优化。对船厂实际生产数据进行了实证分析以及与其它算法的对比分析,证明了所提方法在空间调度问题上的有效性和实用性。     

项目鲁棒调度的资源分配启发式算法研究

合理的资源配置是提高项目调度鲁棒性一种有效的方法。本文针对项目鲁棒调度问题,提出了Max-PRUA资源分配启发式算法,以期通过生成鲁棒性高的资源分配方案来提高调度计划的鲁棒性。本算法设计了最大化利用优先关系和不可避免弧传递资源的资源分配两项策略来传递最大资源量,以减少由额外约束传递的资源量,降低对项目调度鲁棒性的影响。为寻优最优资源分配方案,配合局部搜索算法,本算法构建了动态活动组GRA,通过对组内活动顺序重排以生成多种资源分配方案,以利于从解空间中寻优出最佳的鲁棒性方案。最后通过大量的仿真实验验证和与其它算法进行比较,结果表明本算法对于不同规模和不同因素影响的项目均有较好的适应性,生成的资源分配方案对调度计划鲁棒性影响较小,是一种有效的算法。     

基于混合算法的实时订货信息下的车辆调度优化

实时订货信息下的车辆调度是随机性车辆调度中货物需求量、需求点均不确定的情况下的车辆调度.针对该问题,本文构建了配送总成本最小的目标函数,提出了采用混合算法求解的思路.即以局部搜索法求得初始解,采用遗传算法优化初始解,并在送货时间更新后,利用禁忌搜索法求解速度快的特点改进调度方案,得到订货信息不断更新的条件下的车辆调度方案.通过实例分析,本方法既可解决电子商务条件下实时订货的车辆调度问题,也具有求解结果可靠、求解过程快速的特点.     

The just-in-time scheduling problem in a flow-shop scheduling system

We study the problem of maximizing the weighted number of just-in-time (JIT) jobs in a flow-shop scheduling system under four different scenarios. The first scenario is where the flow-shop includes only two machines and all the jobs have the same gain for being completed JIT. For this scenario, we provide an O(n³) time optimization algorithm which is faster than the best known algorithm in the literature. The second scenario is where the job processing times are machine-independent. For this scenario, the scheduling system is commonly referred to as a proportionate flow-shop. We show that in this case, the problem of maximizing the weighted number of JIT jobs is NP-hard in the ordinary sense for any arbitrary number of machines. Moreover, we provide a fully polynomial time approximation scheme (FPTAS) for its solution and a polynomial time algorithm to solve the special case for which all the jobs have the same gain for being completed JIT. The third scenario is where a set of identical jobs is to be produced for different customers. For this scenario, we provide an O(n³) time optimization algorithm which is independent of the number of machines. We also show that the time complexity can be reduced to O(n log n) if all the jobs have the same gain for being completed JIT. In the last scenario, we study the JIT scheduling problem on m machines with a no-wait restriction and provide an O(mn²) time optimization algorithm.     

A polynomial algorithm for no-wait cyclic hoist scheduling in an extended electroplating line

This paper addresses cyclic hoist scheduling in a no-wait electroplating line where a part visits some processing tanks more than once and multiple duplicate tanks are used at some production stages. We prove that such an extended problem can be solved in polynomial time.     

工件有到达时间及可拒绝下的同类平行机排序问题的近似算法

本文研究工件有到达时间且可拒绝下的同类平行机排序问题。在该问题中, 给定一个待加工工件集, 每个工件在到达之后, 可以被选择安排到$m$台同类平行机器中的某一台机器上进行加工, 也可以被选择拒绝加工, 但需支付一定的拒绝惩罚费用。目标函数是最小化接受工件集的最大完工时间与拒绝工件集的总拒绝费用之和。当$m$为固定常数时, 设计了一个伪多项式时间动态规划精确算法; 当$m$为任意输入时, 设计了一个近似算法, 当接受工件个数大于$(m-1)$时, 该算法近似比为3, 当接受工件个数小于$(m-1)$时, 该算法近似比为$(2+\rho)$, 其中$\rho$为机器加工速度最大值和最小值的比值。最后通过算例演示了算法的运行。     

工件有到达时间及可拒绝下的同类平行机排序问题的近似算法

本文研究工件有到达时间且可拒绝下的同类平行机排序问题。在该问题中, 给定一个待加工工件集, 每个工件在到达之后, 可以被选择安排到$m$台同类平行机器中的某一台机器上进行加工, 也可以被选择拒绝加工, 但需支付一定的拒绝惩罚费用。目标函数是最小化接受工件集的最大完工时间与拒绝工件集的总拒绝费用之和。当$m$为固定常数时, 设计了一个伪多项式时间动态规划精确算法; 当$m$为任意输入时, 设计了一个近似算法, 当接受工件个数大于$(m-1)$时, 该算法近似比为3, 当接受工件个数小于$(m-1)$时, 该算法近似比为$(2+\rho)$, 其中$\rho$为机器加工速度最大值和最小值的比值。最后通过算例演示了算法的运行。     

堆取料机调度问题的一个近似算法

研究源自煤炭港口料场管理的堆取料机调度问题.该问题中,堆取料机从长为L的料场作业区最左端开始空机或载料运行,两者的速度之比为s：1（s ≥ 1）.决策者需确定储料堆在作业区的分布以及堆取料机处理它们的先后次序,目的是极小化一批煤料运输船的在港服务时间.考虑堆取料机处理完毕需要回到料场终端的作业模型,证明了存在最坏情况界不超过1+1/4s的近似算法.     

An exact algorithm for minimizing resource availability costs in project scheduling

A new exact algorithm that solves the Resource Availability Cost Problem (RACP) in project scheduling is shown to yield a significant improvement over the existing algorithm in the literature. The new algorithm consists of a hybrid method where an initial feasible solution is found heuristically. The branching scheme solves a Resource-Constrained Project Scheduling Problem (RCPSP) at each node where the resources of the RACP are fixed. The knowledge of previously solved RCPSPs is used to produce cuts in the search tree. A worst-case-performance theorem is established for this new algorithm. Experiments are performed on instances adapted from the PSPLIB database. The new algorithm can be used to minimize any resource availability cost problem once a procedure for the underlying resource-constrained problem is available.     

工件的运输和继列分批加工协作排序问题

近年来,工件的运输和加工协作排序问题在物流和供应链管理领域得到广泛关注. 讨论了先用 $\ m$ 台车辆将工件从等待区域运输到继列分批处理机处, 再进行分批加工的协作排序问题, 加工一批工件需要支付一定的费用, 目标为最小化工件的总完工时间与批的加工费用之和. 在工件的加工时间都相等的情况下, 如果工件运输方案确定, 给出了多项式时间的动态规划算法; 如果工件运输方案不确定, 证明了该问题是{\, NP}-难的, 给出了车辆返回时间 $\ t=0$ 时, 最差性能比等于 $\ 2-\frac{1}{m}$ 的近似算法.     

A heuristic algorithm for the hospital health examination scheduling problem

This study considers the problem of health examination scheduling. Depending on their gender, age, and special requirements, health examinees select one of the health examination packages offered by a health examination center. The health examination center must schedule all the examinees, working to minimize examinee/doctor waiting time and respect time and resource constraints, while also taking other limitations, such as the sequence and continuity of the examination procedures, into consideration. The Binary integer programming (BIP) model is one popular way to solve this health examination scheduling problem. However, as the number of examinees and health examination procedures increase, solving BIP models becomes more and more difficult, if not impossible. This study proposes health examination scheduling algorithm (HESA), a heuristic algorithm designed to solve the health examination scheduling problem efficiently and effectively. HESA has two primary objectives: minimizing examinee waiting time and minimizing doctor waiting time. To minimize examinee waiting time, HESA schedules the various parts of each examinee’s checkup for times when the examinee is available, taking the sequence of the examination procedures and the availability of the resources required into account. To minimize doctor waiting time, HESA focuses on doctors instead of examinees, assigning waiting examinees to a doctor as soon as one becomes available. Both complexity analysis and computational analyses have shown that HESA is very efficient in solving the health examination scheduling problem. In addition to the theoretical results, the results of HESA’s application to the concrete health examination scheduling problems of two large hospitals in Taiwan are also reported.     

A green train scheduling model and fuzzy multi-objective optimization algorithm

Train scheduling model is traditionally formulated to minimize the energy consumption for reducing the operation cost. As the European Union formulates the first carbon emission trading scheme in the world, it is necessary to extend the operation cost to include the expenses for buying/selling the carbon emission allowances. In this paper, we propose a multi-objective train scheduling model by minimizing the energy and carbon emission cost as well as the total passenger-time, and named it as green train scheduling model. For obtaining a non-dominated timetable which has equal satisfactory degree on both objectives, we apply a fuzzy multi-objective optimization algorithm to solve the model. Finally, we perform two numerical examples to illustrate the efficiency of the proposed model and solution methodology.