This paper considers the large-scale order batching problem (OBP) in picker-to-parts manual picking warehouses with new features of multiple blocks and ultra-narrow aisles, observed from one of the largest online retailers in China. To address these large instances, we develop a two-stage intelligent heuristic order batching algorithm which can help online retailers get satisfied batching solutions effectively. In the first stage, we cluster orders into groups according to the between-block routes applying Hong’s the route-packing-based order batching procedure (RBP) method. In the second stage, based on the former order groups, we construct batches according to the within-block narrow-aisles through heuristic search.

Canonical correlation analysis (CCA) is an effective spatial filtering algorithm widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). In existing CCA methods, training data are used for constructing templates of stimulus targets and the spatial filters are created between the template signals and a single-trial testing signal. The fact that spatial filters rely on testing data, however, results in low classification performance of CCA compared to other state-of-the-art algorithms such as task-related component analysis (TRCA). In this study, we proposed a novel CCA method in which spatial filters are estimated using training data only. This is achieved by using observed EEG training data and their SSVEP components as the two inputs of CCA and the objective function is optimized by averaging multiple training trials. In this case, we proved in theory that the two spatial filters estimated by the CCA are equivalent, and that the CCA and TRCA are also equivalent under certain hypotheses. A benchmark SSVEP data set from 35 subjects was used to compare the performance of the two algorithms according to different lengths of data, numbers of channels and numbers of training trials. In addition, the CCA was also compared with power spectral density analysis (PSDA). The experimental results suggest that the CCA is equivalent to TRCA if the signal-to-noise ratio of training data is high enough; otherwise, the CCA outperforms TRCA in terms of classification accuracy. The CCA is much faster than PSDA in detecting time of targets. The robustness of the training data-driven CCA to noise gives it greater potential in practical applications.

In the management of Internet Protocol networks, the number of flows is an important performance metric because it has useful applications in areas such as port scan detection, denial-of-service detection, and traffic analysis. Real-time counting of flows is particularly important because network operators can take immediate actions against detected network anomalies or performance degradation. This paper presents a method that enables real-time counting of flows classified by application. More useful information for network management can be obtained by counting classified flows. For example, the proposed method is helpful in determining the type of attacks or victim services for attack detection. The algorithm for counting classified flows is developed using the timestamp vector algorithm. This paper first explores a naïve method that has as many timestamp vector mechanisms as the application classes. However, this method is disadvantageous because it consumes very large memory space. To avoid this problem, a new method that considerably decreases memory consumption is proposed. In addition, the paper also investigates a method for improving measurement accuracy. The effectiveness of the proposed method is evaluated for real-world network data.

For the management of IP (Internet Protocol) networks, flow counting is significant because of various applications such as denial-of-service (DoS) attack detection, etc. Although several flow counting algorithms have been proposed in literature, they have disadvantages in computational time and accuracy. This paper proposes a flow counting algorithm that eliminates these disadvantages. The proposed method improves the computational time through two techniques: packet sampling and a new data structure based on multiple counters. For the accuracy, the existing flow counting methods is often degraded by regarding the terminated flows as existing and thus overestimating the number of flows. The paper examines a method that avoids such overestimation by utilizing TCP FIN messages. The effectiveness of the proposed method was evaluated and compared with an existing method experimentally.

Based on the study of motion vector distribution from several commonly used test image sequences, a new diamond search (DS) algorithm for fast block-matching motion estimation (BMME) is proposed in this paper. Simulation results demonstrate that the proposed DS algorithm greatly outperforms the well-known three-step search (TSS) algorithm. Compared with the new three-step search (NTSS) algorithm, the DS algorithm achieves close performance but requires less computation by up to 22% on average. Experimental results also show that the DS algorithm is better than the four-step search (4SS) and block-based gradient descent search (BBGDS), in terms of mean-square error performance and required number of search points.

This paper presents new efficient shortest path algorithms to solve single origin shortest path problems (SOSP problems) and multiple origins shortest path problems (MOSP problems) for hierarchically clustered data networks. To solve an SOSP problem for a network with n nodes, the distributed version of our algorithm reaches the time complexity of O(log(n)), which is less than the time complexity of O(log/sup 2/ (n)) achieved by the best existing algorithm. To solve an MOSP problem, our algorithm minimizes the needed computation resources, including computation processors and communication links for the computation of each shortest path so that we can achieve massive parallelization. The time complexity of our algorithm for an MOSP problem is O(m log(n)), which is much less than the time complexity of O(M log/sup 2/ (0)) of the best previous algorithm. Here, M is the number of the shortest paths to be computed and m is a positive number related to the network topology and the distribution of the nodes incurring communications, m is usually much smaller than M. Our experiment shows that m is almost a constant when the network size increases. Accordingly, our algorithm is significantly faster than the best previous algorithms to solve MOSP problems for large data networks.

In this paper, a new algorithm based on hierarchical aggregation/disaggregation and decomposition/composition (HAD) scheme is proposed to solve the optimal routing problems (ORP) for hierarchically structured networks of multi-layer backbones. Our algorithm has two major differences with the existing HAD algorithms for hierarchically clustered networks: (1) our algorithm works with more general networks than the networks with the clustered structure; (2) our algorithm parallelizes the computations for different commodities (message flows defined by a pair of origin node and destination node) so that it speeds up with a parallel time complexity of O(mlog/sup 2/(n)), which is much less than O(Mlog/sup 2/(n)) needed for the existing HAD algorithms. Here, n is the number of nodes in the network; M is the number of commodities and m is a positive number usually much smaller than M and is a function of the patterns of all the commodities including the locations of all origin nodes and destination nodes, and the flow demand of each commodity. Furthermore, our algorithm can make a trade-off between the run time and the optimality, i.e., by allowing the solution to be sub-optimal, our algorithm can save great amount of computation time. The implementation of the algorithm for a 200-node network is simulated using OPNET simulation package (OPNET or Optimized Network Engineering Tools is developed by MIL3, Inc.), and the test results are consistent with our analysis.