带机器准备时间的机器覆盖问题的在线、半在线算法

研究以极大化最小机器负载为目标的机器带准备时间的同型机排序问题.证明了LS算法是求解该问题的最好的在线算法,它的最坏情况界为1/m.同时给出了求解两台机的预先知道工件最大加工时间,预先知道工件集的总加工时间以及预先知道工件从大到小到达这三种情形下最好的半在线算法,这三个算法的最坏情况界分别为2/3,2/3以及3/4.     

带机器准备时间的平行机在线与半在线排序

本文研究带机器准备时间的m台平行机系统在线和半在线排序问题.对在线排序问题,我们证明了LS算法的最坏情况界为2-1/m.对已知工件加工时间递减,已知总加工时间和已知工件最大加工时间三个半在线模型,我们分析了它们的下界和所给算法的最坏情况界.对其中两台机情形均得到了最好近似算怯。     

Pm‖Cmax问题的算法AKK的一个改进的最坏情况性能比

本文考虑的是平行机排序问题Pm‖Cmax.对此问题Knuth和Kleitman给出了一个近似算法AKK,Graham证明了此算法的最坏情况性能比不大于1＋1-1/m/1＋｜k/m｜,而且当k≡0（modm）时这个界是紧的.在本文中我们给出了此算法的一个改进的最坏情况性能比： 1＋max{1-1/m/1＋k1＋1/m,1-1/m-k2/1＋k1},其中k1和k2为非负整数且k1m＋k2=k.本文证明了当k2≠0时,它好于Graham的结果,同时我们给出了两个实例说明这个界是紧的.     

带机器准备时间的平行机ordinal排序及近似算法

本文研究带机器准备时间的m台平行机ordinal在线排序问题。讨论了在极小化最大机器完工时间和极小化最大工件完工时间两种目标下的不同下界和相应的在线近似算法。对第一个目标，我们得到了3/2的下界和最坏情况界为2-1/m的近似算法。对第二个目标，我们得到了最坏情况为m的最好近似算法。我们还对一些特殊情况进行了分析。     

带约束的平行机排序的一个近似算法

讨论有资源约束和有机器准备时间的平行机排序问题,资源约束为每个机器至多可加工k个工件,在极小化makespan的上给出了一个匹配算法,证明其最坏情况最紧界是2-m^-1,并进一步给出了它的两个带参数的最坏情况界。     

极小化加权完工时间和的Flowshop问题的算法

本文讨论了极小化加权完工时间和的Flowshop问题.我们给出了一个最坏情况误差界为m的启发式算法,对于m=2的情况,如果工件具有一致权因子,即pi相似文献   

可转包两台流水作业机排序的近似算法

研究可转包的两台流水作业机排序问题, 目标是极小化最大完工时间和总外包费用之和. 首先给出最坏情况界为2的近似算法, 接着对工件满足有序化约束的情形给出最坏情况界为\frac{3}{2}的改进算法, 以上算法界均为紧界.     

带服务器的三台平行机排序问题的复杂性和近似算法

讨论带有两具服务器的三台机的平行机排序问题,在这个问题的实例中,每一个工件都有一个时间长度 安装操作必须要由服务器来完成,每一个服务器在同一时刻只能安装一个工件,用三段式描述表示此问题即为Ｐ３,Ｓ２／ｓｉ＝１／Ｃｍａｘ,证明了此问题为ＮＰ－Ｃ的,分别给出了在在线和离线条件下的近似算法,并且估计了算法的最坏情况界。     

Pm||C_(max)问题的算法A_(KK)的一个改进的最坏情况性能比(英文)

本文考虑的是平行机排序问题Pm||Cmax.对此问题Knuth和Kleitman给出了一个近似算法AKK,Graham证明了此算法的最坏情况性能比不大于1 (1-1/m/1 |k/m|),而且当k(?)0(modm)时这个界是紧的.在本文中我们给出了此算法的一个改进的最坏情况性能比:1 max{1-1/m/1 k1 1/m,1-1/m-k2/1 k1},其中k1和k2为非负整数且k1m k2=k.本文证明了当k2≠0时,它好于Graham的结果,同时我们给出了两个实例说明这个界是紧的.     

极大化提前完工总量平行机排序问题的LPT算法

研究带有共同交货期的三台平行机排序问题。工件在加工过程中不允许中断, 目标是极大化所有工件的提前完工量, 即在交货期前所加工工件(或部分) 的总加工时长。由于该问题是NP-难问题, 本文应用经典LPT算法来解决该问题。我们证明了LPT算法求解该问题的最坏情况界至多为$\frac{15}{13}$, 并给出实例说明最坏情况界的下界为$\frac{27}{25}$。     

On the on-line maintenance scheduling problem

A machine instantly serves requests but needs to undergo maintenance after serving a maximum of L requests. We want to maximize the number of requests served. In the on-line version, we prove that serving L requests before placing a maintenance is 0.5-competitive and is best possible for deterministic algorithms. We describe a 0.585-competitive randomized algorithm and show an upper bound of \(2L/(3L-1)\). We also analyze the empirical performance of various on-line algorithms on specific arrival distributions.     

LP-based online scheduling: from single to parallel machines

We study classic machine sequencing problems in an online setting. Specifically, we look at deterministic and randomized algorithms for the problem of scheduling jobs with release dates on identical parallel machines, to minimize the sum of weighted completion times: Both preemptive and non-preemptive versions of the problem are analyzed. Using linear programming techniques, borrowed from the single machine case, we are able to design a 2.62-competitive deterministic algorithm for the non-preemptive version of the problem, improving upon the 3.28-competitive algorithm of Megow and Schulz. Additionally, we show how to combine randomization techniques with the linear programming approach to obtain randomized algorithms for both versions of the problem with competitive ratio strictly smaller than 2 for any number of machines (but approaching two as the number of machines grows). Our algorithms naturally extend several approaches for single and parallel machine scheduling. We also present a brief computational study, for randomly generated problem instances, which suggests that our algorithms perform very well in practice. A preliminary version of this work appears in the Proceedings of the 11th conference on integer programming and combinatorial optimization (IPCO), Berlin, 8–10 June 2005.     

Minimizing the total completion time on-line on a single machine, using restarts

We give an algorithm to minimize the total completion time on-line on a single machine, using restarts, with a competitive ratio of 3/2. The optimal competitive ratio without using restarts is 2 for deterministic algorithms and e/(e−1)≈1.582 for randomized algorithms. This is the first restarting algorithm to minimize the total completion time that is proved to be better than an algorithm that does not restart.     

Randomized algorithms for total least squares problems

Motivated by the recently popular probabilistic methods for low‐rank approximations and randomized algorithms for the least squares problems, we develop randomized algorithms for the total least squares problem with a single right‐hand side. We present the Nyström method for the medium‐sized problems. For the large‐scale and ill‐conditioned cases, we introduce the randomized truncated total least squares with the known or estimated rank as the regularization parameter. We analyze the accuracy of the algorithm randomized truncated total least squares and perform numerical experiments to demonstrate the efficiency of our randomized algorithms. The randomized algorithms can greatly reduce the computational time and still maintain good accuracy with very high probability.     

Randomized on-line algorithms and lower bounds for computing large independent sets in disk graphs

We study the on-line version of the maximum independent set problem, for the case of disk graphs which are graphs resulting from intersections of disks on the plane. In particular, we investigate whether randomization can be used to break known lower bounds for deterministic on-line independent set algorithms and present new upper and lower bounds.     

Approximation algorithms for problems in scheduling with set-ups

In this paper, we present new approximation results for the offline problem of single machine scheduling with sequence-independent set-ups and item availability, where the jobs to be scheduled are independent (i.e., have no precedence constraints) and have a common release time.We present polynomial-time approximation algorithms for two versions of this problem. In the first version, the input includes a weight for each job, and the goal is to minimize the total weighted completion time. On any input, our algorithm produces a schedule whose total weighted completion time is within a factor 2 of optimal for that input.In the second version, the input includes a due date for each job, and the goal is to minimize the maximum lateness of any job. On any input, our algorithm produces a schedule with the following performance guarantee: the maximum lateness of a job is at most the maximum lateness of the optimal schedule on a machine that runs at half the speed of our machine.     

极小化总加权完工时间的Dial-a-Ride问题的在线随机算法

讨论一般度量空间上带单服务器的极小化总加权完工时间在线Dial-a-Ride问题.通过应用贪婪区间的技巧,提出了一个一般在线随机算法.根据这个算法,对于容量为1或者任意容量的一般度量空间上的在线Dial-a-Ride问题能得到一个竞争比为(2+√2)/ln(1+√2)的在线随机算法,这个算法不仅具有当前最好的竞争比,而且也改进了Krumke等人的结果.     

带有拒绝的单机和同型机排序问题

研究了带有拒绝的单机和同型机排序问题. 对于单机情形, 工件的惩罚费用是对应加工时间的\alpha倍.如果工件有到达时间, 目标为最小化时间表长与惩罚费用之和, 证明了这个问题是可解的.如果所有工件在零时刻到达, 目标为最小化总完工时间与惩罚费用之和, 也证明了该问题是可解的.对于同型机排序问题, 研究了工件分两批在线实时到达的情形, 目标为最小化时间表长与惩罚费用之和.针对机器台数2和m, 分别给出了竞争比为2和4-2/m的在线算法.