A survey of problems,solution techniques,and future challenges in scheduling semiconductor manufacturing operations

In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.     

Combination of convex theories: Modularity,deduction completeness,and explanation

Decision procedures are key components of theorem provers and constraint satisfaction systems. Their modular combination is of prime interest for building efficient systems, but their effective use is often limited by poor interface capabilities, when such procedures only provide a simple “sat/unsat” answer. In this paper, we develop a framework to design cooperation schemas between such procedures while maintaining modularity of their interfaces. First, we use the framework to specify and prove the correctness of classic combination schemas by Nelson–Oppen and Shostak. Second, we introduce the concept of deduction complete satisfiability procedures, we show how to build them for large classes of theories, then we provide a schema to modularly combine them. Third, we consider the problem of modularly constructing explanations for combinations by re-using available proof-producing procedures for the component theories.     

Designing and evaluating an energy efficient Cloud

Cloud infrastructures have recently become a center of attention. They can support dynamic operational infrastructures adapted to the requirements of distributed applications. As large-scale distributed systems reach enormous sizes in terms of equipment, the energy consumption issue becomes one of the main challenges for large-scale integration. Like any other large-scale distributed system, Clouds face an increasing demand in energy. In this paper, we explore the energy issue by analyzing how much energy virtualized environments cost. We provide an energy-efficient framework dedicated to Cloud architectures and we validate it through different experimentations on a modern multicore platform. We show on a realistic example that our infrastructure could save 25% of the Cloud nodes’ electrical consumption.     

Co-constructing with stakeholders a role-playing game to initiate collective management of erosive runoff risks at the watershed scale

Erosive runoff is a recurring problem and is a source of sometimes deadly muddy floods in the Pays de Caux (France). The risk results from a conjunction of natural factors and human activity. Efficient actions against runoff in agricultural watersheds are well known. However they are still difficult to implement as they require co-operation between stakeholders. Local actors thus need tools to help them understand the collective consequences of their individual decisions and help to initiate a process of negotiation between them. We decided to use a participatory approach called companion modelling (ComMod), and, in close collaboration with one of the first group of local stakeholders, to create a role-playing game (RPG) to facilitate negotiations on the future management of erosive runoff. This paper describes and discusses the development of the RPG and its use with other groups of local stakeholders within the framework of two game sessions organized by two different watershed management committees. During the joint construction step, stakeholders shared their viewpoints about the environment, agents, rules, and how to model runoff in preparation for the creation of the RPG. During the RPG sessions, two groups of eight players, including farmers, mayors and watershed advisors, were confronted with disastrous runoff in a fictive agricultural watershed. Results showed that they managed to reduce runoff by 20–50% by engaging a dialogue about grass strips, storage ponds and management of the intercrop period. However, further progress is still needed to better control runoff through the implementation of better agricultural practices because, during the RPG sessions, the watershed advisors did not encourage farmers to do so. Because of the complexity of management problems, results of jointly constructing the game and the RPG sessions showed that modelling and simulation can be a very useful way of accompanying the collective learning process. This new way of working was welcomed by the participants who expressed their interest in organizing further RPG sessions.     

Bond graph based control of a three-phase inverter with LC filter – Connection to passive and active loads

This paper proposes a model of a three phase electrical inverter with a LC output filter in delta connection used in a renewable energy supply system. The concept of inverse bond graph via bicausality is used for the control law design. The control law robustness is tested by connecting passive and active (induction machine) loads.     

Enhancing structural software coverage by incrementally computing branch executability

Structural code coverage criteria have been studied since the early seventies, and now they are well supported by commercial and open-source tools and are commonly embedded in several advanced industrial processes. Most industrial applications still refer to simple criteria, like statement and branch coverage, and consider complex criteria, like modified condition decision coverage, only rarely and often driven by the requirements of certification agencies. The industrial value of structural criteria is limited by the difficulty of achieving high coverage, due to both the complexity of deriving test cases that execute specific uncovered elements and the presence of many infeasible elements in the code. In this paper, we propose a technique that both generates test cases that execute yet uncovered branches and identifies infeasible branches that can be eliminated from the computation of the branch coverage. In this way, we can increase branch coverage to closely approximate full coverage, thus improving its industrial value. The algorithm combines symbolic analysis, abstraction refinement, and a novel technique named coarsening, to execute unexplored branches, identify infeasible ones, and mitigate the state space explosion problem. In the paper, we present the technique and illustrate its effectiveness through a set of experimental results obtained with a prototype implementation.     

Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks

The detection of bad weather conditions is crucial for meteorological centers, specially with demand for air, sea and ground traffic management. In this article, a system based on computer vision is presented which detects the presence of rain or snow. To separate the foreground from the background in image sequences, a classical Gaussian Mixture Model is used. The foreground model serves to detect rain and snow, since these are dynamic weather phenomena. Selection rules based on photometry and size are proposed in order to select the potential rain streaks. Then a Histogram of Orientations of rain or snow Streaks (HOS), estimated with the method of geometric moments, is computed, which is assumed to follow a model of Gaussian-uniform mixture. The Gaussian distribution represents the orientation of the rain or the snow whereas the uniform distribution represents the orientation of the noise. An algorithm of expectation maximization is used to separate these two distributions. Following a goodness-of-fit test, the Gaussian distribution is temporally smoothed and its amplitude allows deciding the presence of rain or snow. When the presence of rain or of snow is detected, the HOS makes it possible to detect the pixels of rain or of snow in the foreground images, and to estimate the intensity of the precipitation of rain or of snow. The applications of the method are numerous and include the detection of critical weather conditions, the observation of weather, the reliability improvement of video-surveillance systems and rain rendering.     

A network approach to the French system of legal codes—part I: analysis of a dense network

We explore one aspect of the structure of a codified legal system at the national level using a new type of representation to understand the strong or weak dependencies between the various fields of law. In Part I of this study, we analyze the graph associated with the network in which each French legal code is a vertex and an edge is produced between two vertices when a code cites another code at least one time. We show that this network distinguishes from many other real networks from a high density, giving it a particular structure that we call concentrated world and that differentiates a national legal system (as considered with a resolution at the code level) from small-world graphs identified in many social networks. Our analysis then shows that a few communities (groups of highly wired vertices) of codes covering large domains of regulation are structuring the whole system. Indeed we mainly find a central group of influent codes, a group of codes related to social issues and a group of codes dealing with territories and natural resources. The study of this codified legal system is also of interest in the field of the analysis of real networks. In particular we examine the impact of the high density on the structural characteristics of the graph and on the ways communities are searched for. Finally we provide an original visualization of this graph on an hemicyle-like plot, this representation being based on a statistical reduction of dissimilarity measures between vertices. In Part II (a following paper) we show how the consideration of the weights attributed to each edge in the network in proportion to the number of citations between two vertices (codes) allows deepening the analysis of the French legal system.     

Eliciting Preferences on Multiattribute Societies with a Choquet Integral

This paper reexamines the construction of indicators of standards of living, by focussing on the challenges raised by the subjectivity and the multidimensionality of living conditions. For that purpose, we apply Choquet integral-based multiattribute value theory to the elicitation, from rankings of multiattribute hypothetical societies, of individual preferences on different dimensions of living conditions. A simple application of the proposed approach highlights that preferences on multiattribute societies cannot, in general, be represented by an additive value model, as there exist complementarities and redundancies between different dimensions of standards of living. Our elicitation exercise reveals also a strong heterogeneity of individual preferences on hypothetical societies. Finally, we explore how elicited preferences can be used to cast a new light on the ranking of actual societies.     

A new feature selection method on classification of medical datasets: Kernel F-score feature selection

In this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg–Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection.