Robust Graph Coloring for Uncertain Supply Chain Management
This paper studies a new Robust Graph Coloring (RGC) problem for modelling the uncertain resource constraint assignment problems in supply chain management. Most optimization tasks under uncertainty were studied by the stochastic programming based on the prediction of the future changes. However, in this study, we have another motivation concerning the robustness of the model to deal with various uncertain changes rather than focusing on the prediction of the input. RGC is an extension of the classical graph coloring problem, which maintains the number of colors as a constraint and models the uncertain scenarios based on the edge weight in the graph. A case studies of RGC - Robust Aircraft Assignment is first introduced. Then, several new techniques are proposed to solve such an NP-hard problem approximately, including the partition based encoding, the improvement graph and k-exchange cycle based neighborhood construction and several meta-heuristics including local search, simulated annealing, tabu search and hybrid method. The experimental results show that the above techniques solve the RGC accurately and effectively.

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 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: 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: 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: Analysis on Network Model Parameters of BP Neural Network in the Assessment for Bridge Reliability

title: Short-Term Traffic Flow Prediction Based on Parallel Quasi-Newton Neural Network
abstract: Identifying and predicting the situation of traffic flow play an important role in traveler information broadcast and real-time traffic control. In this paper, a short-term traffic flow prediction model based on the parallel self-scaling quasi-Newton (SSPQN) neural network is presented. In this method, a set of parallel search directions are generated at the beginning of each iteration. Each of these directions is selectively chosen from a representative class of quasi-Newton (QN) methods. Inexact line searches are then carried out to estimate the minimum point along each search direction. Experimental and analytical results demonstrate the feasibility of applying SSPQN to traffic flow prediction and prove that it can better satisfy real-time demand of traffic flow prediction.

title: SNPMiner: A Domain-Specific Deep Web Mining Tool
abstract: In this paper, we propose a novel query-oriented, mediator-based biological data querying tool, SNPMiner. The system searches and queries single nucleotide polymorphisms (SNPs) data from eight widely used web accessible databases. The system provides a domain-specific search utility, which can access and collect data from the deep web. This is a web-based system, so any user can use the system by accessing our server from their own computers. The system includes three important components, which are the Web server interface, the dynamic query planner, and the Web page parser. The Web server interface can provide end users a unified and friendly interface. The dynamic query planner can automatically schedule an efficient query order on all available databases according to user's query request. The Web page parser analyzes the layout of HTML files and extracts desired data from those files. The final results of the query are organized in a tabular format, which can be reviewed by a biological researcher.

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: Personality Based Latent Friendship Mining
abstract: Methods and apparatus for detecting imperfections in adhesive coatings formed on fingers of beams are disclosed. The present invention relates, in one aspect, to a method for preparing a first work piece for joining with a second work piece. The method involves forming a finger in the first work piece, then applying an adhesive coating on the finger. A determination is made to determine whether the adhesive coating is defective through the used of a sensing mechanism which is arranged to scan across a portion of the finger. In one embodiment, a defect in the adhesive coating on the finger is defined as a void.