Reliability evaluation of a multistate flexible flow shop with stochastic capacity for multiple types of jobs

A flexible flow shop (FFS) is a general manufacturing system that has been studied by numerous researchers. Owing to maintenance, partial failure, the possibility of failure, unexpected situations, etc., the number of functioning machines in a stage should be represented by multiple levels. It is appropriate to regard the capacity in each stage (i.e., the number of machines in a stage) as stochastic. Unlike the previous research, which dealt with the FFS problems under the assumption of the fixed capacity, this paper extends the deterministic capacity to the stochastic case in every stage. The FFS with stochastic capacity is modeled as a multistate flexible flow shop network (MFFSN), where each edge denotes a stage with stochastic capacity and each node denotes a buffer. The addressed problem is to evaluate network reliability, the probability that the MFFSN can complete a customer's order composed of multiple types of jobs within a time threshold. An efficient algorithm integrating a systematic branch-and-bound approach is proposed to obtain the lower boundary vectors, in terms of a pair of capacity vectors generated from two estimated demand vectors. Two practical cases, a tile production system and an apparel manufacturing system, are presented to demonstrate the proposed algorithm and to discuss the changes in network reliability within different time thresholds, respectively.     

A game theoretic framework for stochastic multipath routing in self-organized MANETs

In this paper we propose a game theoretic framework for stochastic multipath routing in mobile ad hoc networks (MANETs). In a MANET, intelligent and adaptive attackers may try to hijack, jam or intercept data packets traveling from source to destination. In our proposed game, at each stage the source node keeps track of the available multiple paths, the residual bandwidth of the paths and the strategy of the attackers from the information gathered during the previous stage. Based on these observations, the source node selects a path for data communication and switching strategy among the multiple established paths between the source node and the destination node. Accordingly, it selects an optimal routing strategy to send data packets to the destination at each stage of the game. Using minimax-Q learning, the selected routing strategy maximizes the expected sum of per stage discounted payoff, which is the utilization of residual bandwidth between a source–destination pair along with the probability that the path is safe. Performance analysis and numerical results show that our proposed scheme achieves significant performance gains in terms of residual bandwidth utilization, average end-to-end delay, packet delivery ratio, routing overhead and security.     

Improved Complex-valued Radial Basis Function (ICRBF) neural networks on multiple crack identification

This paper introduces a new class of neural networks in complex space called Complex-valued Radial Basis Function (CRBF) neural networks and also an improved version of CRBF called Improved Complex-valued Radial Basis Function (ICRBF) neural networks. They are used for multiple crack identification in a cantilever beam in the frequency domain. The novelty of the paper is that, these complex-valued neural networks are first applied on inverse problems (damage identification) which come under the category of function approximation. The conventional CRBF network was used in the first stage of ICRBF network and in the second stage a reduced search space moving technique was employed for accurate crack identification. The effectiveness of proposed ICRBF neural network was studied first on a single crack identification problem and then applied to a more challenging problem of multiple crack identification in a cantilever beam with zero noise as well as 5% noise polluted signals. The results proved that, the proposed ICRBF and real-valued Improved RBF (IRBF) neural networks have identified the single and multiple cracks with less than 1% absolute mean percentage error as compared to conventional CRBF and RBF neural networks, mainly because of their second stage reduced search space moving technique. It appears that IRBF neural network is a good compromise considering all factors like accuracy, simplicity and computational effort.     

面向全局搜索的自适应领导者樽海鞘群算法

针对实际配送过程中客户需求、车辆服务时间随机可变,提出带软时间窗的随机需求和随机服务时间的车辆路径问题.以配送车辆行驶路径为研究对象,建立基于配送成本、时间惩罚成本、修正成本的配送车辆路径优化模型,并提出一种混合禁忌搜索算法.该算法将最近邻算法和禁忌搜索算法相结合,将时间窗宽度及距离作为最近邻算法中节点选择标准;并对禁忌搜索算法中禁忌长度等构成要素进行自适应调整,引入自适应惩罚系数.实验结果表明,改进后的混合禁忌搜索算法具有较强的寻优能力、较高的鲁棒性,同时算法所得车辆行驶路径受客户需求变动影响较小.     

Reliability evaluation for an intermittent production system with stochastic number of normal machines

In an intermittent production system (IPS), a number of normal machines in a workstation may present multiple levels owing to maintenance, possibility of failure, etc. It means that the number of machines in each workstation is stochastic. This paper proposes a key performance index (KPI), which reflects the probability that an IPS can complete demand d within time constraint T. Such a probability is defined as system reliability. The IPS is modeled as a stochastic network, in which each arc is regarded as a workstation with stochastic number of normal machines, and each node is represented as a buffer. The concept of minimal machine vector (MMV), which indicates the minimal capacity required at each workstation to satisfy the demand and time constraints, is presented for evaluating the system reliability. In particular, a novel algorithm based on depth-first search is proposed to derive all MMVs. This algorithm avoids searching for unnecessary child nodes, and thus increases efficiency. Two practical examples, a printed circuit board and a footwear manufacturing systems, are used to illustrate the proposed algorithm. Such a KPI can provide information to production managers to understand the probability that an order can be completed on time.     

Topology optimization framework for structures subjected to stationary stochastic dynamic loads

The field of topology optimization has progressed substantially in recent years, with applications varying in terms of the type of structures, boundary conditions, loadings, and materials. Nevertheless, topology optimization of stochastically excited structures has received relatively little attention. Most current approaches replace the dynamic loads with either equivalent static or harmonic loads. In this study, a direct approach to problem is pursued, where the excitation is modeled as a stationary zero-mean filtered white noise. The excitation model is combined with the structural model to form an augmented representation, and the stationary covariances of the structural responses of interest are obtained by solving a Lyapunov equation. An objective function of the optimization scheme is then defined in terms of these stationary covariances. A fast large-scale solver of the Lyapunov equation is implemented for sparse matrices, and an efficient adjoint method is proposed to obtain the sensitivities of the objective function. The proposed topology optimization framework is illustrated for four examples: (i) minimization of the displacement of a mass at the free end of a cantilever beam subjected to a stochastic dynamic base excitation, (ii) minimization of tip displacement of a cantilever beam subjected to a stochastic dynamic tip load, (iii) minimization of tip displacement and acceleration of a cantilever beam subjected to a stochastic dynamic tip load, and (iv) minimization of a plate subjected to multiple stochastic dynamic loads. The results presented herein demonstrate the efficacy of the proposed approach for efficient multi-objective topology optimization of stochastically excited structures, as well as multiple input-multiple output systems.

     

A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand

We study a two-stage, multi-item inventory system where stochastic demand occurs at stage 1, and nodes at stage 1 replenish their inventory from stage 2. Due to the complexity of stochastic inventory optimization in multi-echelon system, few analytical models and effective algorithms exist. In this paper, we establish exact stochastic optimization models by proposing a well-defined supply–demand process analysis and provide an efficient hybrid genetic algorithm (HGA) by introducing a heuristic search technique based on the tradeoff between the inventory cost and setup cost and improving the initial solution. Monte Carlo method is also introduced to simulate the actual demand and thus to approximate the long-run average cost. By numerical experiments, we compare the widely used installation policy and echelon policy and show that when variance of stochastic demand increase, echelon policy outperforms installation policy and, furthermore, the proposed heuristic search technique greatly enhances the search capacity of HGA.     

HEURISTIC SEARCH UNDER CONTRACT

In this article, we present a heuristic search technique (Contract Search) that can be adapted automatically for a specific node contract. We analyze the node expansion characteristics of best‐first search techniques and identify a probabilistic model (rank profiles) that characterizes the search under restricted expansions. We use the model to formulate an optimal strategy to choose level dependent restriction bounds, maximizing the probability of obtaining the optimal cost goal node under the specified contract. We analyze the basic properties of the rank profiles and establish its relation with the search space configuration and heuristic error distributions. We suggest an approximation scheme for the profile function for unknown search spaces. We show how the basic framework can be adapted to achieve different objectives (like optimizing the expected quality) considering multiple goals and approximate solutions. Experimental comparison with anytime search techniques like ARA* and beam search on a number of search problems shows that Contract Search outperforms these techniques over a range of contract specifications.     

A genetic algorithm for multiprocessor scheduling

The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented     

A stochastic connectionist approach for global optimization withapplication to pattern clustering

In this paper, a stochastic connectionist approach is proposed for solving function optimization problems with real-valued parameters. With the assumption of increased processing capability of a node in the connectionist network, we show how a broader class of problems can be solved. As the proposed approach is a stochastic search technique, it avoids getting stuck in local optima. Robustness of the approach is demonstrated on several multi-modal functions with different numbers of variables. Optimization of a well-known partitional clustering criterion, the squared-error criterion (SEC), is formulated as a function optimization problem and is solved using the proposed approach. This approach is used to cluster selected data sets and the results obtained are compared with that of the K-means algorithm and a simulated annealing (SA) approach. The amenability of the connectionist approach to parallelization enables effective use of parallel hardware.     

复杂条件下的社区搜索方法

社区搜索旨在寻找包含给定节点集的社区，能够快速获取个性化的社区信息.针对现有社区搜索算法难以满足复杂搜索条件的现状，提出条件社区搜索这一新问题.解决该问题有助于对社交网络进行智能分析，在复杂搜索条件下为用户提供更好的社区结果.首先，基于布尔表达式，给出条件社区搜索问题的形式化定义，可有效表达给定节点不能出现在社区内以及给定节点中至少有一个出现在社区内的要求.接着，提出解决条件社区搜索问题的通用框架，包括对搜索条件进行简化、根据简化后的搜索条件进行多次单项条件社区搜索、合并各单项条件社区搜索的结果等主要步骤.同时，提出"社区搜索+过滤"的方法和给点加权的方法来进行单项条件社区搜索.最后，真实数据集上的大量实验结果表明所提方法的正确性和有效性.     

基于节点可达度的公交多路径搜索算法

针对公交网络路径搜索问题,以复杂网络的角度进行了相关研究。根据出行者实际需求,提出一种基于节点可达度的公交多路径搜索算法。采用复杂二分网络模型来描述公交网络,将公交线路和公交站点分别看做一类节点,每条公交线路与它所经过的公交站点之间存在连边;在分析网络社团结构的基础上定义了节点可达度,算法根据节点可达度逐步搜索直至目的节点,搜索过程保留可能存在的多条最佳路径。实验结果表明,该方法能够得到最小换乘的多条有效路径。     

多中心联合配送模式下集货需求随机的VRPSDP问题

针对多中心联合配送模式下集货需求随机的同时配集货车辆路径问题(MDVRPSDDSPJD), 构建了两阶段MDVRPSDDSPJD模型. 预优化阶段基于随机机会约束机制以及车载量约束为客户分配车辆, 生成预优化方案; 重优化阶段采用失败点重优化策略对服务失败点重新规划路径. 根据问题特征, 设计了自适应变邻域文化基因算法(Adaptive memetic algorithm and variable neighborhood search, AMAVNS), 针对文化基因算法易早熟、局部搜索能力弱等缺陷, 将变邻域搜索算法的深度搜索能力运用到文化基因算法的局部搜索策略中, 增强算法的局部搜索能力; 提出自适应邻域搜索次数策略和自适应劣解接受机制平衡种群进化所需的广度和深度. 通过多组算例验证了提出模型及算法的有效性. 研究成果不仅深化和拓展了VRP (Vehicle routing problem)相关理论研究, 也为物流企业制定车辆调度计划提供一种科学合理的方法.     

A new stochastic search algorithm bundled honeybee mating for solving optimization problems

This paper presents a new stochastic search technique to solve optimization problems. The new stochastic search of parallel vector evaluated honeybee mating optimization (VEHBMO) technique mimics the honeybee’s mating. The effectiveness of the proposed technique is compared with other stochastic optimization methods through standard benchmark functions. Also, the proposed VEHBMO is applied over real engineering problems of economic load dispatch and environmental/economic power dispatch problems. Obtained results confirm the validity of the proposed stochastic search technique.     

Real-time stochastic optimal control for traffic signals of multiple intersections

In this paper, a real-time stochastic optimal control method of traffic signal is modified. In addition, H-GA-PSO algorithm is proposed to search optimal traffic signals based on the stochastic model. The H-GA-PSO algorithm is a modified Hierarchical Particle Swarm Optimization (H-PSO) algorithm based on Genetic Algorithm (GA) processing. Finally, the effectiveness of the stochastic optimal control method with H-GA-PSO algorithm is shown through simulations at multiple intersections using a micro-traffic simulator.     

Urban rail transit planning using a two-stage simulation-based optimization approach

In urban metro systems, stochastic disturbances occur repeatedly as a result of an increment of demands or travel time variations, therefore, improving the service quality and robustness through minimizing the passengers waiting time is a real challenge. To deal with dwell time variability, travel time and demand uncertainty, a two-stage GA-based simulation optimization approach is proposed in order to minimize the expected passenger waiting times. The proposed method here has the capability of generating robust timetables for a daily operation of a single-loop urban transit rail system. The first stage of the algorithm includes the evaluation of even-headway timetables through simulation experiments. In the second stage, the search space is limited to the uneven-headway patterns in such a manner where the algorithm keeps the average of headways close to the best even-headway timetable, obtained from the first stage. The optimization is intended to adjust headways through simulation experiments. Computational experiments are conducted on Tehran Metropolitan Railway (IRAN) and the outcomes of optimized timetable obtained by this proposed method are demonstrated. This newly proposed two-stage search approach could achieve to a more efficient solution and speed up the algorithm convergence.     

Evolving recurrent perceptrons for time-series modeling

Evolutionary programming, a systematic multi-agent stochastic search technique, is used to generate recurrent perceptrons (nonlinear IIR filters). A hybrid optimization scheme is proposed that embeds a single-agent stochastic search technique, the method of Solis and Wets, into the evolutionary programming paradigm. The proposed hybrid optimization approach is further augmented by "blending" randomly selected parent vectors to create additional offspring. The first part of this work investigates the performance of the suggested hybrid stochastic search method. After demonstration on the Bohachevsky and Rosenbrock response surfaces, the hybrid stochastic optimization approach is applied in determining both the model order and the coefficients of recurrent perceptron time-series models. An information criterion is used to evaluate each recurrent perceptron structure as a candidate solution. It is speculated that the stochastic training method implemented in this study for training recurrent perceptrons can be used to train perceptron networks that have radically recurrent architectures.     

Multiple Scattering Approximation in Heterogeneous Media by Narrow Beam Distributions

Fast realistic rendering of objects in scattering media is still a challenging topic in computer graphics. In presence of participating media, a light beam is repeatedly scattered by media particles, changing direction and getting spread out. Explicitly evaluating this beam distribution would enable efficient simulation of multiple scattering events without involving costly stochastic methods. Narrow beam theory provides explicit equations that approximate light propagation in a narrow incident beam. Based on this theory, we propose a closed‐form distribution function for scattered beams. We successfully apply it to the image synthesis of scenes in which scattering occurs, and show that our proposed estimation method is more accurate than those based on the Wentzel‐Kramers‐Brillouin (WKB) theory.     

Fault detection filter design for stochastic networked control systems

This paper is concerned with the fault detection filter (FDF) design for networked control systems subject to time‐varying transmission intervals and delays, packet dropouts, and communication constraints. The considered communication constraint is that only one network node is allowed to gain access to the shared communication channel. Also, the accessing of each node is scheduled by a specified stochastic protocol, and the remote FDFs perform the FD task only with these partially available measurements. By focus on the network‐induced phenomena, the whole FD system are first modeled in the framework of switched stochastic systems with multiple stochastic parameters. Subsequently, by using the multi‐Lyapunov functional approach and novel analysis approach, less conservative conditions including some previous existing results are derived to construct such FDFs. Finally, an example is given to illustrate the effectiveness of the proposed method.Copyright © 2013 John Wiley & Sons, Ltd.     

基于反向学习的群居蜘蛛优化WSN节点定位算法

针对启发优化算法在WSN节点定位问题中定位精度不高和收敛速度较慢的缺陷,提出基于反向学习的群居蜘蛛优化WSN节点定位算法.为减少前期随机搜索,所提出算法首先通过Bounding-box方法得到未知节点可能存在的区域,在该区域初始化启发个体,并将加权中心反向学习策略与群居蜘蛛群优化算法相结合,求解未知节点估计位置,提高算法全局搜索能力.仿真结果表明,相比于传统算法,所提出算法收敛速度更快,节点定位精度更高.