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.

title: Robust airport gate assignment
abstract: In this paper, we propose a new strategy for the robust constraint resource assignment problem and apply it to solve the robust airport gate assignment (RAGA). RAGA attempts to accurately build an evaluation criteria for the ability of an aircraft-to-gate assignment to handle uncertainty on aircraft schedule; and to accurately and effectively search the most robust airport gate assignment. We model the RAGA by a stochastic programming model and transform it into a binary programming model by introducing the unsupervised estimation functions without knowing any information on the real-time arrival and departure time of aircrafts in advance. Moreover, a partition-based search space encoding, two neighborhood operators for single or multiple aircrafts reassignment, and a hybrid meta-heuristic combining a tabu search and a local search are proposed to solve RAGA efficiently. Experimental results on the real-life test data from Hong Kong International Airport demonstrate that the proposed RAGA model provides a valuable tool for the airport to improve its robustness in uncertain operations

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: Flexible Demand Assignment Problem
abstract: This paper proposed a state-of-the-art local search for solving Flexible Demand Assignment problem (FDA) which considers the balance between revenue and cost in demand assignment. Different than the published studies, our research splits the FDA problem into three core subproblems as operators for neighborhood construction. The three specified subproblems One Bin Repack, Two Bins Repack and Unpack are proposed completely based on mathematical modelling, computational complexity, executive conditions and greedy solving methods. Benchmark experimental results have shown that the proposed local search improved to the best published heuristics by 2.34%.

title: A New Neighborhood Based on Improvement Graph for Robust Graph Coloring Problem
abstract: In this paper, we propose a new neighborhood structure based on the improvement graph for solving the Robust Graph Coloring Problem, an interesting extension of classical graph coloring Different from the traditional neighborhood where the color of only one vertex is modified, the new neighborhood involves several vertices In addition, the questions of how to select the modified vertices and how to modify them are modelled by an improvement graph and solved by a Dynamic Programming method The experimental results clearly show that our new improvement graph based k-exchange cycle neighborhood improves the accuracy significantly, especially for large scale heuristic search.

title: A Survey on Reviewer Assignment Problem
abstract: Research into Reviewer Assignment Problem (RAP) is still in its early stage but there is great world-wide interest, as the foregoing process of peer-review which is the brickwork of science authentication. The RAP approach can be divided into three phases: identifying assignment procedure, computing the matching degree between manuscripts and reviewers, and optimizing the assignment so as to achieve the given objectives. Methodologies for addressing the above three phases have been developed from a variety of research disciplines, including information retrieval, artificial intelligent, operations research, etc. This survey is not only to cover variations of RAP that have appeared in the literature, but also to identify the practical challenge and current progress for developing intelligent RAP systems.

title: A product information modeling framework for product lifecycle management
abstract: The Product Lifecycle Management (PLM) concept holds the promise of seamlessly integrating all the information produced throughout all phases of a product's life cycle to everyone in an organization at every managerial and technical level, along with key suppliers and customers. PLM systems are tools that implement the PLM concept. As such, they need the capability to serve up the information referred to above, and they need to ensure the cohesion and traceability of product data. We describe a product information-modeling framework that we believe can support the full range of PLM information needs. The framework is based on the NIST Core Product Model (CPM) and its extensions, the Open Assembly Model (OAM), the Design-Analysis Integration model (DAIM) and the Product Family Evolution Model (PFEM). These are abstract models with general semantics, with the specific semantics about a particular domain to be embedded within the usage of the models for that domain. CPM represents the product's function, form and behavior, its physical and functional decompositions, and the relationships among these concepts. An extension of CPM provides a way to associate design rationale with the product. OAM defines a system level conceptual model and the associated hierarchical assembly relationships. DAIM defines a Master Model of the product and a series of abstractions called Functional Models-one for each domain-specific aspect of the product-and two transformations, called idealization and mapping, between the master model and each functional model. PFEM extends the representation to families of products and their components; it also extends design rationale to the capture of the rationale for the evolution of the families. The framework is intended to: (1) capture product, design rationale, assembly, and tolerance information from the earliest conceptual design stage-where designers deal with the function and performance of products-to the full lifecycle; (2) facilitate the semantic interoperability of next-generation CAD/CAE/CAM systems; and (3) capture the evolution of products and product families. The relevance of our framework to PLM systems is that any data component in the framework can be accessed directly by a PLM system, providing fine-grained access to the product's description and design rationale.

title: On the competitiveness of on-line real-time task scheduling
abstract: With respect to on-line scheduling algorithms that must direct the service of sporadic task requests we quantify the benefit of clairvoyancy, i.e., the power of possessing knowledge of various task parameters of future events. Specifically, we consider the problem of preemptively sheduling sporadic task requests in both uni- and multi-processor environments. If a task request is successfuly scheduled to completion, a value equal to the task's execution time is obtained; otherwise no value is obtained. We prove that no on-line scheduling algorithm can guarantee a cumulative value greater than 1/4th the value obtainable by a clairvoyant scheduler; i.e., we prove a 1/4th upper bound on the competitive factor of on-line real-time schedulers. We present an online uniprocessor scheduling algorithm TD1 that actually has a competitive factor of 1/4; this bound is thus shown to be tight. We further consider the effect of restricting the amount of overloading permitted (the loading factor), and quantify the relationship between the loading factor and the upper bound on the competitive factor. Other results of a similar nature deal with the effect of value densities (measuring the importance of type of a task). Generalizations to dual-processor on-line scheduling are also considered. For the dual-processor case, we prove an upper bound of 1/2 on the competitive factor. This bound is shown to be tight in the special case when all the tasks have the same density and zero laxity.

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: A New Multi-view Learning Algorithm Based on ICA Feature for Image Retrieval
abstract: In content-based image retrieval (CBIR), most techniques involve an important issue of how to efficiently bridge the gap between the high-level concepts and low-level visual features. We propose a novel semi-supervised learning method for image retrieval, which makes full use of both ICA feature and general low-level feature. Our approach can be characterized by the following three aspects: (1) The ICA feature, as proved to be representative of human vision, is adopted as a view to describe human perception; (2) A multi-view learning algorithm is introduced to make the most use of different features and dramatically reduce human relevance feedback needed to achieve a satisfactory result; (3) A new semi-supervised learning algorithm is proposed to adapt to the small sample problem and other special constrains of our multi-view learning algorithm. Our experimental results and comparisons are presented to demonstrate the effectiveness of the proposed approach.

title: An Adaptive MIMO System Based on Unified Belief Propagation Detection
abstract: An adaptive multiple input and multiple output (MIMO) system based on the unified belief propagation (BP) detection is proposed. A BP detector composed of a serial of component detectors is first presented. With a unified design of the BP detector, a multi-rate MIMO system adaptive to channel variations is then developed, which aims at achieving the maximal throughput performance. The usage of the unified BP detector eliminates the multiple detector modules at the receiver in conventional multi-mode adaptive MIMO systems. Further, the linear complexity to space time block length and parallel operation of the unified BP detection provide the advantages of low complexity, low detection delay and smooth switching between different data rates. With the average receiving signal-to-noise ratio (SNR) based switching mechanism, the proposed adaptive MIMO framework adaptively chooses the appropriate rate for the transmission. Simulation results are provided to show the BER and complexity performance of a specific BP detector as well as the throughput performance of the adaptive scheme.