多孔硅发光的物理机制——纳米量子限制效应及表面态在发光中的作用

采用阳极氧化腐蚀的方法制备了多孔硅（ＰＳ），这种ＰＳ的微结构为纳米量级的，并具有光致发光特性，这无疑将对全硅光电子学的发展具有很大意义。根据大量实验与理论分析，提出了这种ＰＳ发光的物理机制：纳米量子限制效应和表面态及其物质在发光中的作用，从量子力学的薛定锷方程出发，用沟道势阱的近似模型，推导得到进入量子线的电子和空穴的能量势垒，ＰＳ的有效带宽Ｅ＝Ｅ＿０＋ΔＥ，对于Ｓｉ（Ｅ＿０＝１．１２ｅｖ）。完美地解释了目前在ＰＳ研究中的ＰＬ谱的篮移现象。     

耦合模模型的峰峰值与有效值

介绍了峰峰值和有效值的偶合模方程,给出了峰峰值P矩阵和有效值P矩阵的表达式。利用峰峰值P矩阵和有效值P矩阵模拟了纵向耦合谐振结构以及阻抗元结构的声表面波滤波器的频响。对比两种P矩阵的模拟结果,说明了峰峰值耦合模方程和有效值耦合模方程分析频响的等效性。     

The course scheduling problem at Lufthansa Technical Training

Lufthansa Technical Training GmbH (LTT) performs training courses for Lufthansa Technik AG as well as for several other international airlines. Courses of about 670 different types are offered of which several hundred take place each year. The course scheduling problem faced by LTT is to construct a yearly schedule which maximizes the profit margin incurred while meeting a variety of complex precedence, temporal, and resource-related constraints. A “good” operational schedule should also meet a number of additional subordinate objectives. We formalize the problem and develop a heuristic scheme along with several priority rules, as well as a local search algorithm to determine well-suited weights for weighted composite rules. The operational planning situation of 1996 served as our major test instance; additional test instances were constructed by modifying this data. Several computational experiments were carried out to evaluate the performance of the algorithms. It turned out that the best so-found schedule is substantially better in terms of the profit margin incurred than the solution manually constructed by LTT.     

Fast neighborhood search for the single machine total weighted tardiness problem

Most successful heuristics for solving 1||∑w_jT_j are based on swap moves. We present an algorithm which improves the complexity of searching the swap neighborhood from O(n³) to O(n²). We show that this result also improves the complexity of the recently developed dynasearch heuristics.     

A decomposition heuristics for the container ship stowage problem

In this paper we face the problem of stowing a containership, referred to as the Master Bay Plan Problem (MBPP); this problem is difficult to solve due to its combinatorial nature and the constraints related to both the ship and the containers. We present a decomposition approach that allows us to assign a priori the bays of a containership to the set of containers to be loaded according to their final destination, such that different portions of the ship are independently considered for the stowage. Then, we find the optimal solution of each subset of bays by using a 0/1 Linear Programming model. Finally, we check the global ship stability of the overall stowage plan and look for its feasibility by using an exchange algorithm which is based on local search techniques. The validation of the proposed approach is performed with some real life test cases. This work has been developed within the research area: “The harbour as a logistic node” of the Italian Centre of Excellence on Integrated Logistics (CIELI) of the University of Genoa, Italy     

The k-means range algorithm for personalized data clustering in e-commerce

This paper describes the k-means range algorithm, a combination of the partitional k-means clustering algorithm with a well known spatial data structure, namely the range tree, which allows fast range searches. It offers a real-time solution for the development of distributed interactive decision aids in e-commerce since it allows the consumer to model his preferences along multiple dimensions, search for product information, and then produce the data clusters of the products retrieved to enhance his purchase decisions. This paper also discusses the implications and advantages of this approach in the development of on-line shopping environments and consumer decision aids in traditional and mobile e-commerce applications.     

傅里叶变换红外光谱固体制样技术

通过对溴化钾与纳米固体超强酸样品的比例进行了探讨 ,得出制样时溴化钾与样品比例范围为1∶ 0 .0 0 1— 1∶ 0 .0 2 9,其最佳比例范围为 1∶ 0 .0 0 98— 1∶ 0 .0 1 5 ,并且应用于其他固体样品的测定 ,结果基本一致。同时得出 ,固体样品的最少检测量为 2 .0× 1 0 -5g     

基于遗传禁忌算法的双资源约束下并行生产线调度研究

并行生产线调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题。在考虑遗传算法早熟收敛特性和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传算法和禁忌搜索算法的双资源并行作业车间的调度优化问题算法,即考虑到了产品的调度受到机器、工人等资源制约的影响,对算法中种群的构造,适应度计算,遗传操作等方面进行了研究,最后给出了实例仿真和结论。     

COSEARCH: A Parallel Cooperative Metaheuristic

In order to design a well-balanced metaheuristic for robustness, we propose the COSEARCH approach which manages the cooperation of complementary heuristic methods via an adaptive memory which contains a history of the search already done. In this paper, we present the idiosyncrasies of the COSEARCH approach and its application for solving large scale instances of the quadratic assignment problem (QAP). We propose an original design of the adaptive memory in order to focus on high quality regions of the search and avoid attractive but deceptive areas. For the QAP, we have hybridized three heuristic agents of complementary behaviours: a Tabu Search is used as the main search algorithm, a Genetic Algorithm is in charge of the diversification and a Kick Operator is applied to intensify the search. The evaluations have been executed on large scale network of workstations via a parallel environment which supports fault tolerance and adaptive dynamic scheduling of tasks.     

On the multi-attribute stopping problem with general value functions

We examine a sequential selection problem in which a single option must be selected. Each option's value is a function of its attributes, whose precise values can be ascertained at a given cost. We prove the optimality of a threshold stopping rule for a general class of objective functions.