An efficient branch and bound algorithm for assembly line balancing problems with parallel multi-manned workstations

In the event that big-sized complex products (containing a large number of assembly tasks most of which have long task times) are produced in simple or two-sided assembly lines, hundreds of stations are essentially required. Long product flow time, a large area for establishment of the line, a high budget for the investment of equipment, and tools in stations and several work-in-process are also required for these kinds of products. In order to avoid these disadvantages, assembly lines with parallel multi-manned workstations can be utilized. In this paper, these lines and one of their balancing problems are addressed, and a branch and bound algorithm is proposed. The algorithm is composed of a branching scheme, some efficient dominance and feasibility criteria based on a problem-specific knowledge. A heuristic-based guidance for enumeration process is included as an efficient component of the algorithm as well. VWSolver algorithm proposed for a special version of the problem in the literature has been modified and compared with the proposed algorithm. Results show that proposed algorithm outperforms VWSolver in terms of both CPU times and quality of feasible solutions found.     

改进的分支定界算法

如何进行最优的特征选择是模式识别的研究重点之一。目前比较常用的最优特征选择方法是BAB和BAB＋算法,然而此算法搜索时间比较长。在此基础上详细地阐述改进的分支定界的原理以及算法,该算法的基本思想是通过剪切那些肯定不会产生最优解的分支,同时引入了部分路径和父路径的概念,以达到决策树能够快速搜索到最优解的目的。实验结果证明了该算法的有效性及优越性。     

一种高效动态LEO卫星网络流量调节路由算法

针对考虑负载均衡的LEO卫星网络路由算法存在控制网络开销偏大、路由更新不及时以及流量调节机制分配不均等问题,提出了一种基于负载均衡的动态LEO卫星网络路由算法DRLB。根据卫星节点路径记录信息以及后向Agent读取策略设计新的路由机制,获得动态卫星拓扑结构;分析前向Agent的分组格式并删除冗余字段,达到减小网络开销目的;根据数据发送时间间隔构造前向Agent选址策略,提高路由更新效率,通过考虑卫星所处纬度流量分配不均问题,改进流量调节因子,获得更好的负载均衡效果。仿真结果表明,与SDRZ-MA算法相比,DRLB算法在减缓星地之间的控制开销、平均端到端时延等方面具有较好的优势。     

An efficient branch and bound algorithm to solve the quadratic integer programming problem

The quadratic integer programming problem is considered. It will be shown in which order the variablesx ₁, ...,x _n should be ramified in order to reduce the number of knots being studied to a minimum. There areO(n ³) operations necessary to determine a favourable ramification. Numerical tests confirm the efficiency of the given algorithm.     

An asynchronous and iterative load balancing algorithm for discrete load model

Diffusion algorithms are some of the most popular algorithms for dynamic load balancing in which loads move from heavily loaded processors to lightly loaded neighbor processors. To achieve a global load balance in a parallel computer, the algorithm is iterated until the load difference between any two processors is smaller than a specified value. Therefore, one fundamental property to be studied is algorithm convergence. Several analytical works on the convergence of different diffusion load balancing algorithms have been carried out, but they treat loads as non-negative real quantities. In this paper, we describe the Diffusion Algorithm Searching Unbalanced Domains (DASUD) algorithm, which uses loads as non-negative integer values and, unlike existing algorithms, reaches a local balance situation where the maximum load difference between any two processor in the set of neighbor processors for each processor is one load unit. The convergence property of an asynchronous implementation of DASUD using integer loads is proven theoretically.     

An optimal migration algorithm for dynamic load balancing

The problem of redistributing the work load on parallel computers is considered. An optimal redistribution algorithm, which minimises the Euclidean norm of the migrating load, is derived. The relationship between this algorithm and some existing algorithms is discussed and the convergence of the new algorithm is studied. Finally, numerical results on randomly generated graphs as well as on graphs related to real meshes are given to demonstrate the effectiveness of the new algorithm. © 1998 John Wiley & Sons, Ltd.     

An improved branch and bound algorithm for computing k-nearest neighbors

In 1975 Fukunaga and Narendra proposed an efficient branch and bound algorithm for computing k-nearest neighbors. Their algorithm, after a hierarchical decomposition of the design set into disjoint subsets, employs two rules in order to eliminate the necessity of calculating many distances. This correspondence discusses the applicability of two additional rules for a further reduction of the number of distance computations. Experimental results using samples from bivariate Gaussian and uniform distributions suggest that the number of distance computations required by the modified is typicaly one fourth of that of the Fukunaga-Narendra algorithm.     

一种动态的入侵检测系统负载均衡算法

目前的入侵检测不仅需要模式匹配,而且需要协议异常检测,提出了一种新动态的负载均衡算法,采用两层结构,对网络流量按照服务类型进行初步划分之后分别对每部分流量进行二次分配,并对每种类型的流量进行相应的协议异常检测。该算法能在不牺牲系统性能的前提下有效提高网络入侵检测系统的检测效率,降低误检率,并可有效地适应网络流量的变化,降低漏检率。     

Dynamic load balancing and efficient load estimators for asynchronous iterative algorithms

In a previous paper, we have shown the very high power of asynchronism for parallel iterative algorithms in a global context of grid computing. In this article, we study the interest of coupling load balancing with asynchronism in such algorithms. After proposing a noncentralized version of dynamic load balancing which is best suited to asynchronism, we verify its efficiency by some experiments on a general partial differential equation (PDE) problem. Finally, we give some general conditions for the use of load balancing to obtain good results with this kind of algorithm and discuss the choice of the residual as an efficient load estimator.     

A load index and load balancing algorithm for heterogeneous clusters

This paper presents a load balancing algorithm specifically designed for heterogeneous clusters, composed of nodes with different computational capabilities. The method is based on a new index, which takes into consideration two levels of processors heterogeneity: the number of cores per node and the computational power of each core. The experimental results show that this index allows achieving balanced workload distributions even on those clusters where heterogeneity can not be neglected.     

A grid-based dynamic load balancing approach for data-centric storage in wireless sensor networks

In many data-centric storage techniques, each event corresponds to a hashing location by event type. However, most of them fail to deal with storage memory space due to high percentage of the load is assigned to a relatively small portion of the sensor nodes. Hence, these nodes may fail to deal with the storage of the sensor nodes effectively. To solve the problem, we propose a grid-based dynamic load balancing approach for data-centric storage in sensor networks that relies on two schemes: (1) a cover-up scheme to deal with a problem of a storage node whose memory space is depleted. This scheme can adjust the number of storage nodes dynamically; (2) the multi-threshold levels to achieve load balancing in each grid and all nodes get load balancing. Simulations have shown that our scheme can enhance the quality of data and avoid hotspot of the storage while there are a vast number of the events in a sensor network.     

基于软件定义网络的数据中心网络负载均衡算法研究

针对传统网络的分布式架构使得负载均衡技术难以满足低成本、高灵活性、自适应调整的要求,提出一种基于SDN的数据中心网络负载均衡算法。首先,根据路径当前负载状况和链路负载波动为路径设置了一个权重,并以此作为路径选择依据;其次,设置了一个负载均衡度用于衡量网络负载状况;最后,针对需要调度的流,进一步限定了其流量大小范围,保证了高效的流调度。仿真结果表明,与其他算法相比,所提算法能有效提高网络资源利用率并均衡全网负载。     

A branch and bound algorithm for numerical Max-CSP

The Constraint Satisfaction Problem (CSP) framework allows users to define problems in a declarative way, quite independently from the solving process. However, when the problem is over-constrained, the answer “no solution” is generally unsatisfactory. A Max-CSP \(\mathcal{P}_m = \langle V, \textbf{D}, C \rangle\) is a triple defining a constraint problem whose solutions maximize the number of satisfied constraints. In this paper, we focus on numerical CSPs, which are defined on real variables represented as floating point intervals and which constraints are numerical relations defined in intension. Solving such a problem (i.e., exactly characterizing its solution set) is generally undecidable and thus consists in providing approximations. We propose a Branch and Bound algorithm that provides under and over approximations of a solution set that maximize the maximum number \({m_{\mathcal P}}\) of satisfied constraints. The technique is applied on three numeric applications and provides promising results.     

面向云环境的Flink负载均衡策略

作为新一代的大数据计算引擎,Flink得到了广泛应用。Flink在云环境下进行容器化部署时,其默认任务调度算法不能感知节点的资源信息,导致即时调整负载和自主均衡能力较差,而主流的容器编排工具虽然提供了管理容器的可能性,却也未能结合Flink特点解决平衡资源利用的同时降低容器组内的通信开销问题。针对以上问题开展研究,提出了一种面向云环境的Flink负载均衡策略FLBS,综合考虑了Flink集群中算子的分布特点和容器间通信机制,以节点间通信开销和均衡负载作为评估标准。实验结果表明,与Flink默认调度策略相比,FLBS能够有效提高计算效率,提升系统性能。     

基于新的网格存优策略的多目标归档算法

网格方法被多个进化算法用来保持解集的分布性。基于ε支配概念的ε-MOEA本质上也是基于网格策略的。虽然ε-MOEA通常情况下都能在算法性能的各方面之间取得较为合理的折衷,但是由于其存在固有缺陷,很多时候表现出不容忽视的问题——当PF_true对某一维的变化率在该维不同区域的差异较大时,解集中边界个体或代表性个体丢失——严重影响解集的分布性。针对这一问题,定义了一种新的δ支配概念和虚拟“最优点”的概念,提出了一种新的网格存优策略,并将之应用于更新进化多目标归档算法的归档集。实验结果显示,基于新的存优策略的进化多目标归档算法（δ-MOEA）具有良好的性能,尤其在分布性方面比NSGA2和ε-MOEA好得多。     

A hierarchical adaptive distributed algorithm for load balancing

Load balancing/sharing exploits the communication facility between the servers of a distributed system, by using the exchanging of status information and jobs between any two servers of the system in order to improve the overall performance.We propose an adaptive distributed hierarchical scheme, the Virtual Tree Algorithm (VTA), which creates a virtual binary tree structure over the actual network topology. It uses the difference-initiated (DI) technique for load balancing, which needs remote information for the transfer policy and no additional information for the location policy. We demonstrate that the introduced virtual construction keeps the exchanged messages to relatively low levels. To evaluate the performance of our policy, we present both theoretical and simulation results. By using simulation, we compare our results with one of the most representative, adaptive, symmetrical and efficient algorithms, the Variable Threshold (V_THR) algorithm.     

A run-time load balancing strategy for highly parallel systems

We discuss a simple run-time load balancing strategy which applies to numerical applications working on planar domains with localized data dependency. We develop an iterative and adaptive partitioner, able to work in a distributed way among the processors of a parallel system. Our algorithm subdivides data space into general quadrilaterals, where each processor works on the data of one area. The topology of these domains is that of a rectangular grid and does not change during execution. In this way a very simple and efficient communication structure is given. The administration overhead due to irregular geometry is small. Also, the overhead caused by periodically read-justing load balance is rather small because of the adaptivity and parallelity of the partitioning algorithm. We ran an scientific application to compare our method with a method working by recursive bisection, and obtained satisfactory results.     

基于目标约束的分层动态负载均衡算法*

针对网格环境下的负载不均问题,提出了一种分层动态负载均衡机制,该机制采用随机服务模型描述网格任务流特性及其资源上的动态负载状态,将站点内负载平衡问题归结为目标约束规划问题。理论分析了分层负载均衡机制的有效性证明并设计了优化方案的求解算法,仿真实验结果显示,该分层负载均衡算法在平均响应时间、系统吞吐量方面优于以往的RBA算法和DBA算法。