一种自动布线方法：多级线探索法

本文介绍了一种在微机上实现的印制板自动布线方法──多级线探索法及其编程方法。这种方法是基于完备布线算法的概念，通过分析李氏算法和传统线探索法的不足之处而得到的。虽然它也是一种线探索法，但有较传统线探索法更强的探索能力，不需要结合李氏算法而能单独完成布线任务，是一种较理想的算法，通过实验获得了较为满意的效果。     

Determination of heavy metals and volatile aromatic compounds in used engine oils and sludges

Assessment of contaminant releases during utilization of used oils is essential for the determination of environmental acceptability. These paper reports the results of the study examining a toxic metal leachability from used engine oil and sludge samples employing leaching test (TCLP). The leaching test indicated that lead in oil samples exceeded 5-ppm concentration level what qualified them as a toxic waste. The samples of contaminated sludge were found to contain high concentration of total lead, barium and chromium, but the leaching test showed concentration below regulatory limit. The total content of benzene, ethylbenzene, toluene, and xylenes (BETX), and naphthalene in used oil and sludge samples was also determined and was found not to be a significant factor to contamination.     

Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15%. Moreover, improvements of up to 41% can be achieved for some test instances.     

A computer-enabled solution procedure for food wholesalers’ distribution decision in cities with a circular transportation infrastructure

This paper addresses the food distribution decision from a wholesaler's perspective, as the wholesalers are often faced with thorny issues such as when, where, how to deliver products at the lowest costs to best satisfy the retailers’ changing needs. This research relies on a distribution problem encountered at Northern Grocery Company in Beijing as an example to develop a two-stage solution procedure and subsequent computer-enabled programs for identifying efficient vehicle routing alternatives. The solution process, which integrates heuristic search algorithms and an integer programming model, can be applied to a wide range of distribution problems with specified route durations and a circular transportation network structure as exemplified by City of Beijing.     

Genetic adaptive state estimation

A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. This paper describes a GA that can perform on-line adaptive state estimation for linear and nonlinear systems. First, it shows how to construct a genetic adaptive state estimator where a GA evolves the model in a state estimator in real time so that the state estimation error is driven to zero. Next, several examples are used to illustrate the operation and performance of the genetic adaptive state estimator. Its performance is compared to that of the conventional adaptive Luenberger observer for two linear system examples. Next, a genetic adaptive state estimator is used to predict when surge and stall occur in a nonlinear jet engine. Our main conclusion is that the genetic adaptive state estimator has the potential to offer higher performance estimators for nonlinear systems over current methods.     

Primal or dual: which promises faster spatiotemporal search?

The existing predictive spatiotemporal indexes can be classified into two categories, depending on whether they are based on the primal or dual methodology. Although we have gained considerable empirical knowledge about various access methods, currently there is only limited understanding on the theoretical characteristics of the two methodologies. In fact, the experimental results in different papers even contradict each other, regarding the relative superiority of the primal and dual techniques. This paper presents a careful study on the query performance of general primal and dual indexes, and reveals important insight into the behavior of each technique. In particular, we mathematically establish the conditions that determine the superiority of each methodology, and provide rigorous justification for well-known observations that have not been properly explained in the literature. Our analytical findings also resolve the contradiction in the experiments of previous work.     

A Review of the Application of Meta-Heuristic Algorithms to 2D Strip Packing Problems

This paper is a review of the approachesdeveloped to solve 2D packing problems withmeta-heuristic algorithms. As packing tasks arecombinatorial problems with very large searchspaces, the recent literature encourages theuse of meta-heuristic search methods, inparticular genetic algorithms. The objective ofthis paper is to present and categorise thesolution approaches in the literature for 2Dregular and irregular strip packing problems.The focus is hereby on the analysis of themethods involving genetic algorithms. Anoverview of the methods applying othermeta-heuristic algorithms including simulatedannealing, tabu search, and artificial neuralnetworks is also given.     

Working memory capacity and the top-down control of visual search: Exploring the boundaries of "executive attention".

The executive attention theory of working memory capacity (WMC) proposes that measures of WMC broadly predict higher order cognitive abilities because they tap important and general attention capabilities (R. W. Engle & M. J. Kane, 2004). Previous research demonstrated WMC-related differences in attention tasks that required restraint of habitual responses or constraint of conscious focus. To further specify the executive attention construct, the present experiments sought boundary conditions of the WMC-attention relation. Three experiments correlated individual differences in WMC, as measured by complex span tasks, and executive control of visual search. In feature-absence search, conjunction search, and spatial configuration search, WMC was unrelated to search slopes, although they were large and reliably measured. Even in a search task designed to require the volitional movement of attention (J. M. Wolfe, G. A. Alvarez, & T. S. Horowitz, 2000), WMC was irrelevant to performance. Thus, WMC is not associated with all demanding or controlled attention processes, which poses problems for some general theories of WMC. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using immune genetic algorithm based hybrid techniques for short-term unit commitment problem

This paper presents a hybrid chaos search (CS), immune algorithm (IA)/genetic algorithm (GA), and fuzzy system (FS) method (CIGAFS) for solving short-term thermal generating unit commitment (UC) problems. The UC problem involves determining the start-up and shut-down schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve, and individual units. First, we combined the IA and GA, then we added the CS and the FS approach. This hybrid system was then used to solve the UC problems. Numerical simulations were carried out using three cases: 10, 20, and 30 thermal unit power systems over a 24 h period. The produced schedule was compared with several other methods, such as dynamic programming (DP), Lagrangian relaxation (LR), standard genetic algorithm (SGA), traditional simulated annealing (TSA), and traditional Tabu search (TTS). A comparison with an immune genetic algorithm (IGA) combined with the CS and FS was carried out. The results show that the CS and FS all make substantial contributions to the IGA. The result demonstrated the accuracy of the proposed CIGAFS approach.