Minimizing the number of machines for scheduling jobs with equal processing times

In this paper, we consider a parallel machine environment when all jobs have the same processing time and arbitrary release dates and deadlines of the jobs are given. We suppose that the available number of machines, which can be used simultaneously, may vary over time. The aim is to construct a feasible schedule in such a way that the maximal number of simultaneously used machines is minimal. We give a polynomial algorithm for this problem.     

A polynomial-time DNA computing solution for the Bin-Packing Problem

It is suggested here an algorithm based on stickers for the DNA Computing model [S. Roweis, E. Winfree, R. Burgoyne, N. Chelyapov, M. Goodman, P. Rothemund, L. Adleman, A sticker-based model for DNA Computation, Journal of Computational Biology 5 (1998) 615–629] that solves the well known Bin-Packing Problem (BPP), that belongs to the class -Hard in the strong sense, in a time bounded by , where n is the quantity of items and q the space requirements expressed in bits.To the best of the authors’ knowledge, this is the first polynomial time algorithmic solution for the BPP in such a model.     

Idle regulation in non-clairvoyant scheduling of parallel jobs

The optimization of parallel applications is difficult to achieve by classical optimization techniques because of their diversity and the variety of actual parallel and distributed platforms and/or environments. Adaptive algorithmic schemes, capable of dynamically changing the allocation of jobs during the execution to optimize global system behavior, are the best alternatives for solving this problem. In this paper, we focus on non-clairvoyant scheduling of parallel jobs with known resource requirements but unknown running times, with emphasis on the regulation of idle periods in the context of general list policies. We consider a new family of scheduling strategies based on two phases which successively combine sequential and parallel execution of jobs. We generalize known worst-case performance bounds by considering two extra parameters, in addition to the number of processors and maximum processor requirements considered in the literature, namely, job parallelization penalty and idle regulation factor. Furthermore, we prove that under certain conditions of idle regulation, the performance guarantee of parallel job scheduling in space-sharing mode can be improved.     

An effective hybrid algorithm for computing symbolic determinants

A hybrid algorithm for computing the determinant of a matrix whose entries are polynomials is presented. It is based on the dimension-decreasing algorithm [22] and the parallel algorithm for computing a symbolic determinant of [19]. First, through the dimension-decreasing algorithm, a given multivariate matrix can be converted to a bivariate matrix. Then, the parallel algorithm can be applied to effectively compute the determinant of the bivariate matrix. Experimental results show that the new algorithm can not only reduce enormously the intermediate expression swell in the process of symbolic computation, but also achieve higher degree of parallelism, compared with the single parallel algorithm given in [19].     

Scheduling jobs with release dates on parallel batch processing machines

In this paper we consider the problem of scheduling jobs with release dates on parallel unbounded batch processing machines to minimize the maximum lateness. We show that the case where the jobs have deadlines is strongly NP-hard. We develop a polynomial-time approximation scheme for the problem to minimize the maximum delivery completion time, which is equivalent to minimizing the maximum lateness from the optimization viewpoint.     

A novel parallel quantum genetic algorithm for stochastic job shop scheduling

In this paper, a Novel Parallel Quantum Genetic Algorithm (NPQGA) is proposed for the stochastic Job Shop Scheduling Problem with the objective of minimizing the expected value of makespan, where the processing times are subjected to independent normal distributions. Based on the parallel evolutionary idea and some concepts of quantum theory, we simulate a model of parallel quantum computation. In this frame, there are some demes (sub-populations) and some universes (groups of populations), which are structured in super star-shaped topologies. A new migration scheme based on penetration theory is developed to control migration rate and direction adaptively between demes, and a novel quantum crossover strategy is devised among universes. The quantum evolution is executed in every deme by applying some improvement operators (the coding mechanism aiming at job shop, the new quantum rotation angle and the catastrophe operator). Experiment results show NPQGA's effectiveness and applicability.     

Optimization via simulation for logistic systems planning and control

This text summarizes the author’s PhD thesis, presented in February 2007 at the University of Calabria and supervised by Roberto Musmanno and Pasquale Legato. The work deals with the field of optimization via simulation. A special emphasis is put on problems with discrete decision variables because they are especially relevant in engineering applications (e.g., in the design of logistic systems). In particular, the thesis focuses on a critical decision problem at marine container terminals. For tackling the problem, an optimization via simulation procedure is presented. Computational results are obtained by using high performance computing systems. The thesis is written in Italian and is available from the author upon request.      

Complete classification of spatial surfaces with parallel mean curvature vector in arbitrary non-flat pseudo-Riemannian space forms

Submanifolds with parallel mean curvature vector play important roles in differential geometry, theory of harmonic maps as well as in physics. Spatial surfaces in 4D Lorentzian space forms with parallel mean curvature vector were classified by B. Y. Chen and J. Van der Veken in [9]. Recently, spatial surfaces with parallel mean curvature vector in arbitrary pseudo-Euclidean spaces are also classified in [7]. In this article, we classify spatial surfaces with parallel mean curvature vector in pseudo-Riemannian spheres and pseudo-hyperbolic spaces with arbitrary codimension and arbitrary index. Consequently, we achieve the complete classification of spatial surfaces with parallel mean curvature vector in all pseudo-Riemannian space forms. As an immediate by-product, we obtain the complete classifications of spatial surfaces with parallel mean curvature vector in arbitrary Lorentzian space forms.      

地方政务数据交换云架构研究与设计

针对目前地方政府部门信息资源交换共享中存在的交换效率低、重复建设等问题,通过分析数据交换技术、应用现状和交换需求等,研究和设计了基于云计算技术的政务数据交换云总体架构和子系统.数据交换云可实现在一个物理交换平台上运行多个可管理的虚拟交换子平台,有效地提高政府投资效益和信息交换效率,方便政府部门使用和管理,并能有效解决数据交换与应用耦合度高的问题.