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.

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 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: 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: 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: 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: Estimate Test Execution Effort at an Early Stage: An Empirical Study
abstract: Software testing is becoming more and more important as it is a widely used activity to ensure software quality. Test execution becomes an activity in the critical path of a project. In this case, early estimation of test execution effort can benefit both tester managers and software projects. This paper reports an empirical study on early test execution effort estimation. In the study, we propose an approach which mainly consists of two parts: test case number prediction from use cases and test effort estimation based on the test suite execution vector model which combines test case number, test execution complexity and its tester together. For each part, we evaluate it with the data of real projects from a financial software company.

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: Inequivalent manifold ranking for content-based image retrieval
abstract: We propose to improve the effectiveness and scalability of graph based manifold ranking methods in image retrieval applications by emphasizing reliable images while damping the effect of noisy or irregular ones. Label information is firstly passed between most reliable data points, then propagated to less reliable ones on manifold structure. By treating these images inequivalently, undesirable effect of noisy samples is greatly reduced, thus effectiveness of manifold ranking algorithms is enhanced. Also, graph size in terms of number of nodes and edges is dramatically reduced, resulting in a great speed-up of the algorithm. Our experiment on real world image data set demonstrates the effectiveness of the proposed approach.

title: Even aberration measurement of lithographic projection system based on optimized phase-shifting marks
abstract: In the present paper, we propose a novel method for measuring the even aberrations of lithographic projection optics by use of optimized phase-shifting marks on the test mask. The line/space ratio of the phase-shifting marks is optimized to obtain the maximum sensitivities of Zernike coefficients corresponding to even aberrations. Spherical aberration and astigmatism can be calculated from the focus shifts of phase-shifting gratings oriented at 0^o, 45^o, 90^o and 135^o at multiple illumination settings. The PROLITH simulation results show that, the measurement accuracy of spherical aberration and astigmatism obviously increase, after the optimization of the measurement mark.

title: Analytical energy gradient evaluation in relativistic and nonrelativistic density functional calculations
abstract: The expressions of analytical energy gradients in density functional theory and their implementation in programs are reported. The evaluation of analytical energy gradients can be carried out in the fully 4-component relativistic, approximate relativistic, and nonrelativistic density functional calculations under local density approximation or general gradient approximation with or without frozen core approximation using different basis sets in our programs. The translational invariance condition and the fact that the one-center terms do not contribute to the energy gradients are utilized to improve the calculation accuracy and to reduce the computational effort. The calculated results of energy gradients and optimized geometry as well as atomization energies of some molecules by the analytical gradient method are in very good agreement with results obtained by the numerical derivative method.