Effective Neighborhood Operators for Solving the Flexible Demand Assignment Problem
Rather than dealing with the traditional one-dimension bin packing problem to minimize the cost in demand assignments, the flexible demand assignment (FDA) problem studied in this paper considers the balance between revenue and cost. Compared with a number of solution methods in operations research, we solve the FDA problem by three specially designed operators of neighborhood construction for search space-namely, One Bin Repack, Two Bins Repack, and Unpack. Extensive computational results clearly show the superiority of the three proposed operators based on simple local search over the best published results.

title: A compromised large-scale neighborhood search heuristic for capacitated air cargo loading planning
abstract: 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.

title: A Compromised Large-Scale Neighborhood Search Heuristic for Cargo Loading Planning
abstract: 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.

title: A stochastic beam search for the berth allocation problem
abstract: 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.

title: k-Center Problems with Minimum Coverage
abstract: 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 .

title: Meta-heuristic Algorithm for the Transshipment Problem with Fixed Transportation Schedules
abstract: 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.

title: Query Planning for Searching Inter-dependent Deep-Web Databases
abstract: Increasingly, many data sources appear as online databases, hidden behind query forms, thus forming what is referred to as the deep web. It is desirable to have systems that can provide a high-level and simple interface for users to query such data sources, and can automate data retrieval from the deep web. However, such systems need to address the following challenges. First, in most cases, no single database can provide all desired data, and therefore, multiple different databases need to be queried for a given user query. Second, due to the dependencies present between the deep-web databases, certain databases must be queried before others. Third, some database may not be available at certain times because of network or hardware problems, and therefore, the query planning should be capable of dealing with unavailable databases and generating alternative plans when the optimal one is not feasible. 
 
This paper considers query planning in the context of a deep-web integration system. We have developed a dynamic query planner to generate an efficient query order based on the database dependencies. Our query planner is able to select the topKquery plans. We also develop cost models suitable for query planning for deep web mining. Our implementation and evaluation has been made in the context of a bioinformatics system, SNPMiner. We have compared our algorithm with a naive algorithm and the optimal algorithm. We show that for the 30 queries we used, our algorithm outperformed the naive algorithm and obtained very similar results as the optimal algorithm. Our experiments also show the scalability of our system with respect to the number of data sources involved and the number of query terms.

title: WiMAX System Performance with Multiple Transmit and Multiple Receive Antennas
abstract: 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).

title: Ab initio ro-vibrational structure of the C 2n isotopes of H 2 O +
abstract: The ro-vibrational structures of the C2" isotopes ofH20+ have been calculated from variational solution of the normal coordinate Eckart-Watson Hamiltonian. The calculations use the discrete ab initio potential energy surface of Weis et al. (1989). Where comparisons can be made, the assignment of the vibrational states is in excellent agreement with experiment and with the ab initio variational calculation of Weis et al., who utilised a different force field and an internal coordinate nuclear Hamiltonian (instead of the Eckart-Watson amiltonian). Furthermore, the calculated rotational levels of the ground and the first excited vibrational states of H20+ and D20+ are in excellent agreement with experiment.

title: DIET — A Scalable, Robust and Adaptable Multi-Agent Platform for Information Management
abstract: The complexity of the global information infrastructure demands new approaches to managing distributed information. The Decentralised Information Ecosystem Technologies (DIET) project has produced a software platform based on a lightweight, scalable, multi-agent system that can be used to support a variety of information management applications that deal with some of this complexity. This paper describes the main components of the DIET software platform, and several examples of its application. It also considers the diversity of potential applications for this technology.

title: Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources
abstract: 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.