27183053
Improved GRASP with Tabu Search for Vehicle Routing with Both Time Window and Limited Number of Vehicles
44734123
Meta-heuristic Algorithm for the Transshipment Problem with Fixed Transportation Schedules
In this work, we develop an efficient meta-heuristic genetic algorithm for a different type of transshipment problem with supplier and customer time windows, where flow through the crossdock (a kind of transshipment center) is constrained by fixed transportation schedules and crossdocks capacities, and which was proved to be NP -complete in the strong sense. And computational experiments are conducted to show the proposed heuristic outperforms CPLEX.
32718924
k-Center Problems with Minimum Coverage
The k-center problem is a well-known facility location problem and can be described as follows: Given a complete undirected graph G=(V,E), a metric d:V×V→ℝ + and a positive integer k, we seek a subset U ⊆ V of at most k centers which minimizes the maximum distances from points in V to U. Formally, the objective function is given by: 
 
$$ \textstyle\min_{U\subseteq V,|U|\leq k}\max_{v\in V}\min_{r\in U}d(v,r).$$ 
 
As a typical example, we may want to set up k service centers (e.g., police stations, fire stations, hospitals, polling centers) and minimize the maximum distances between each client and these centers. The problem is known to be \(\mathcal{NP}\)-hard [2].
39897206
A Compromised Large-Scale Neighborhood Search Heuristic for Cargo Loading Planning
In this work, we propose a compromised large-scale neighborhood, which is embedded in simulated annealing to solve a cargo loading planning problem arising in logistics industry. It is "compromised" because it makes a tradeoff between the extensive backward checking work incurred in traditional subset-disjoint restriction and the possible infeasibility resulting from the relaxing the restriction. Extensive experiments have shown the competitive advantages of the heuristic approach. The proposed neighborhood search method is generally applicable.
23454249
A compromised large-scale neighborhood search heuristic for capacitated air cargo loading planning
Cost effectiveness is central to the air freight forwarders. In this work, we study how an air freight forwarder should plan its cargo loading in order to minimize the total freight cost given a limited number of rented containers. To solve the problem efficiently for practical implementation, we propose a new large-scale neighborhood search heuristic. The proposed large-scale neighborhood relaxes the subset-disjoint restriction made in the existing literature; the relaxation risks a possibility of infeasible exchanges while at the same time it avoids the potentially large amount of checking effort required to enforce the subset-disjoint restriction. An efficient procedure is then used to search for improvement in the neighborhood. We have also proposed a subproblem to address the difficulties caused by the fixed charges. The compromised large-scale neighborhood (CLSN) search heuristic has shown stably superior performance when compared with the traditional large-scale neighborhood search and the mixed integer programming model.
15711911
A stochastic beam search for the berth allocation problem
In this paper, the optimization of the Berth Allocation Problem (BAP) is transformed into a multiple stage decision making procedure and a new multiple stage search method, namely stochastic beam search algorithm, is proposed to solve it. New techniques such as an improved beam search scheme, a two-phase node goodness estimation, and a stochastic node selection criteria are proposed. Real-life information provided by Singapore Port was collected as our test data. Experimental results show that the proposed stochastic beam search is more accurate and efficient than both the state-of-the-art meta-heuristic and the traditional determinist beam search.
13331481
Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources
Increasingly, biological data is being shared over the deep web. Many biological queries can only be answered by successively searching a number of distinct web-sites. This paper introduces a system that exploits parallelization for accelerating search over multiple deep web data sources. An interactive, two-stage multi-threading system is developed to achieve task parallelization, thread parallelization, and pipelined parallelization. We show the effectiveness of our system by considering a number of queries involving SNP datasets. We show that most of the queries can be accelerated significantly by exploiting these three forms of parallelism.
32208283
A Bidirectional Adaptive Sequential Gaussian Approximation for MIMO Systems
A bidirectional adaptive detection method was proposed to prevent error propagation based on sequential Gaussian approximation algorithm for MIMO systems. Conventional sequential Gaussian approximation detection executes a tree search at the receiver, when the number of searching paths is large, error propagation is well prevented at the cost of high complexity, when few paths are searched, serious error propagation may occur. In the new method, transmitted symbols were estimated parallel both in sequential order and inverse order, and the number of searching paths was determined by comparing the results of bidirectional detection, which can effectively prevent error propagation at reduced complexity. The novel algorithm can be executed at low delay because of parallel computing, and shows good performance at low complexity.
140126379
A Study on the Application of GA-BP Neural Network in the Bridge Reliability Assessment
18844092
Extracting Output Metadata from Scientific Deep Web Data Sources
Increasingly, many data sources appear as online databases, hidden behind query forms, thus forming the deep web. The popularity of this new medium for data dissemination is leading to new problems in data integration. Particularly, to enable data integration from multiple deep web data sources, one needs to obtain the metadata for each of the data sources. Obtaining the metadata, particularly, the output schema, can be very challenging. This is because, given an input query, many deep web data sources only return a subset of the output schema attributes, i.e, the ones that have a non-NULL value for the corresponding input. In this paper, we propose two approaches, which are the sampling model approach and the mixture model approach, respectively, to efficiently obtain an approximately complete set of output schema attributes from a deep web data source. Our experiments show while each of the above two approaches has limitations, a hybrid strategy, where we combine the two approaches, achieves high recall with good precision for most data sources.
206836734
WiMAX System Performance with Multiple Transmit and Multiple Receive Antennas
In this paper, the performance of WiMAX networks using multiple transmit and multiple receiver antennas are analyzed via system simulations. For downlink, two kinds of technologies are studied: i) open loop: where the base station has no knowledge of channel and uses mobile mode selection feedback to switch between single data stream STBC and multi-stream spatial multiplexing MIMO (SM-MIMO); ii) closed loop: where base station has explicit knowledge of downlink channel and transmits using adaptive beamforming. This paper compares the WiMAX downlink system performance using open loop and closed loop MIMO technologies with various frequency reuse factors and different data traffic models. The performance of WiMAX uplink is also analyzed with multiple receive antennas and using both single input multiple output (SIMO) and multi-user MIMO (MU-MIMO, a.k.a. SDMA).