A Survey on Reviewer Assignment Problem
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: On the Selection and Assignment with Minimum Quantity Commitments
abstract: Traditional selection and assignment problems analyze how to select and assign the suppliers to meet demands within minimum total costs. The maximum capacity and the fixed selection costs are two major restrictions imposed on suppliers, and extensively studied in literature.

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: The Capacitated Traveling Salesman Problem with Pickups and Deliveries on a Tree
abstract: The Capacitated Traveling Salesman Problem with Pickups and Deliveries (CTSP-PD)[1] can be defined on an undirected graph T=(V,E), where V is a set of n vertices and E is a set of edges. A nonnegative weight d(e) is associated with each edge e∈ E to indicate its length. Each vertex is either a pickup point, a delivery point, or a transient point. At each pickup point is a load of unit size that can be shipped to any delivery point which requests a load of unit size. Hence we can use a(v)=1,0,–1 to indicate v to be a pickup, a transient, or a delivery point, and a(v) is referred to as the volume of v. The total volumes for pickups and for deliveries are usually assumed to be balanced, i.e., $\sum_{v\in {\it V}}{\it a}({\it v})=0$, which implies that all loads in pickup points must be shipped to delivery points . Among V, one particular vertex r ∈ V is designated as a depot, at which a vehicle of limited capacity of k ≥ 1 starts and ends. The problem aims to determine a minimum length feasible route that picks up and delivers all loads without violating the vehicle capacity.

title: A New Hybrid Genetic Algorithm for the Robust Graph Coloring Problem
abstract: The RGCP (Robust Graph Coloring problem) is a new variant of the traditional graph coloring problem. It has numerous practical applications in real world like timetabling and crew scheduling. The traditional graph coloring problem focuses on minimizing the number of colors used in the graph. RGCP focuses on the robustness of the coloring so that the coloring is able to handle uncertainty that often occurs in the real world. By that, we mean that given a fixed number of colors we would like to color the graph so that adjacent vertices are assigned different colors with the consideration of the possible appearance of the missing edges. In this paper, we present a new hybrid genetic algorithm (GA), which embeds two kinds of local search algorithms – enumerative search and random search within the GA framework. In addition, we integrate a partition based encoding scheme with a specialized crossover operator into our GA method. We also propose an adaptive scheme that alternates between two local search methods to increase performance. Experimental results show that our new algorithm outperforms the best published results in terms of the quality of solutions and the computing time needed.

title: The one-commodity pickup and delivery travelling salesman problem on a path or a tree
abstract: Optimization algorithms for both path and tree topology classes of the one-commodity pickup and delivery travelling salesman problem (1-PDTSP) are proposed in this article, which focus on minimizing the route distance to transport products among pickup and delivery customers by a single vehicle with a limited capacity of k . Each pickup customer provides one unit volume of the product while each delivery customer requires one unit volume of the product. For the path case, we propose an O(n2/ min (k , n)) algorithm for any arbitrary k , and two O(n) algorithms for k = 1 and k = ∞. For the tree case, O(n2) and O(n) algorithms are proposed for k = 1 and k = ∞, respectively. Moreover, when k is arbitrary, the problem becomes NP-hard in the strong sense.

title: Failure probability of wire bonding packages
abstract: This paper presents a methodology/model for elastro-plastic analysis of the wire bonding packages. The methodology is based on finite element method for rate sensitive materials and applicable to very large deformation processes. Ultrasonic power is simulated as displacement boundary condition. Stress induced by the mismatch of the coefficient of thermal expansion (CTE) in the system because of temperature increase due to application of the ultrasonic or the reflow process is considered. The effect of temperature increase on material property is also accounted for. Nonlinear spring elements are adopted to simulate the gradual formation of intermetallic bonding layer between the ball and the electric pad. With these spring elements, the effect of stress and strain relaxation of the ball in the subsequent procedures such as reflow process is taken into account. The mechanical behavior of the silicon chip during the wire bonding with the ultrasonic irradiation and reflow process is thus revealed. Failure probability with reference to the stress distribution is discussed. The developed FE methodology is simple but effective to evaluate the failure probability of wire-bonding packages.

title: Application of differential evolution for harmonic worst-case identification of mass rapid transit power supply system
abstract: Mass rapid transit (MRT) operation results in voltage and current harmonic distortions in AC supply systems. Power-supply standards limit the acceptable harmonic-distortion levels. In order to observe these limits, it is necessary to identify the worst-case harmonic distortion in MRT system. Train operating modes and system configurations affect the level of harmonic distortions. Harmonic worst-case identification can be treated as an optimization problem in terms of train separations and traffic conditions. The approach uses approximate train movement and consumption models, and AC/DC harmonic loadflow for evaluating the harmonic distortions. This paper uses a new method called differential evolution (DE) for solving the problem. Parallel studies using genetic algorithm (GA) are also carried out. Comparative results demonstrate the favourable features of DE for large-scale optimization with real variables, as the method is efficient and fast converging.

title: A Study on the Application of GA-BP Neural Network in the Bridge Reliability Assessment

title: Integrating expert vision systems and spatial databases by unifying knowledge representation schemes: development in remote sensing image analysis expert systems and geographical information systems
abstract: Integration of remote sensing and geographical information system (GIS) technologies can significantly strengthen our capability to handle geographical information. However, so far little practical integration has been achieved. The major barriers to the integration include the problems with data format, data accuracy and system communication. Previous efforts made on these problems could not provide satisfied solutions. 
This research explores a new path for achieving the integration. The three problems, which at first may seem to be independent of each other, are considered as different aspects of a problem with knowledge representation and are tackled by unifying the systems to be integrated on a common knowledge representation framework. Three new techniques--the communication subschema, the relational-linear quadtree, and the fuzzy representation method--have been developed to provide such a framework. A unified data model has been defined to implement the techniques. 
The three techniques have been successfully applied to the integration of GIS and remote sensing systems. Encouraging results have been achieved in the practice of designing, implementing, and integrating a GIS and two remote sensing image analysis systems.

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.