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.

title: A genetic algorithm for machine scheduling problem under shared resource constraints
abstract: In this paper, we present a popular machine scheduling problem with resource constraints in manufacturing engineering. A genetic algorithm based approach is put forward and illustrated, including a specialised encoding and two kinds of decoding greedy schemes. Compared with both several kinds of lower bounds we present in this paper and the results from ILOG OPL software package, it is shown that our rapid genetic algorithm achieved significant results with stable and near-optimal performance.

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: The shortest route cut and fill problem in linear topological structure
abstract: In this paper, we study the Shortest Route Cut and Fill (SRCF) problem where a fleet of vehicles with fixed capacities travel between cut and fill points to finish all cut and fill requirements so that the travel time of the fleet is minimized. We state and classify the SRCF problem and present a complete view of its computational complexities with NP-hardness proof for the basic linear topological structure. Moreover, a simple cycle-based optimization method is proposed to solve the SRCF in linear time complexity, for the version of single vehicle in linear structure without time window constraint.

title: A Survey on Vehicle Routing Problem with Loading Constraints
abstract: In the classical Vehicle Routing Problem (VRP), a fleet of vehicles is available to serve a set of customers with known demand. Each customer is visited by exactly one vehicle as well as the objective is to minimize the total distance or the total charge incurred. Recent years have seen increased attention on VRP integrated with additional loading constraints, known as 2L-CVRP or 3L-CVRP. In this paper we present a survey of the state-of-the-art on 2L-CVRP/3L-CVRP.

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: 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: Numerical examination of performance of some exchange-correlation functionals for molecules containing heavy elements
abstract: The performance of 17 exchange-correlation functionals for molecules containing heavy elements are numerically examined through four-component relativistic density DFT calculations. The examined functionals show the similar accuracy as they do for the molecules containing light elements only except for bond lengths. LDA and OP86 produce good results for bond lengths and frequencies but bad bond energies. Different functionals do not show much different performance for bond energies except LDA. BP86 and GP86 produce results with average accuracy while LYP does not perform well. Although encouraging results are obtained with functional B97GGA-1, other heavily parameterized and meta-GGA functionals do not produce impressive results.

title: Precoded Single Carrier Data Transmission with Orthogonal Frequency Domain Multiplexing Pilots
abstract: A transmission scheme of orthogonally multiplexing pilots in frequency domain with single carrier data signal is proposed in this paper. In this scheme, information-bearing data symbols are modulated in time domain using a simple precode linear transformation, and multiplexed with pilot symbols in frequency domain. The proposed single carrier transmitter has lower complexity and the transmitted signal has lower peak to average power ratio (PAPR) than that of OFDM. In addition, same channel estimation algorithms for OFDM can be applied to the proposed single carrier transmission system due to the similar orthogonal multiplexing of pilot and data symbols in frequency domain. Equalizers based on MMSE and zero-forcing criteria are also presented. Receiver performance is analyzed and compared with OFDM using numerical examples.

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: 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.