Resource constrained scheduling of hierarchically structured designactivity networks

In this paper, a new approach to the problem of scheduling of design activities with precedence and multiple resource constraints is proposed. In addition to the AND type relationship, OR and EXCLUSIVE OR relationships may also exist between design activities. In order to handle these logical relationships, IDEF3 is used for network representation. A large network of design activities can be arranged in different levels of abstraction. A procedure is proposed to transform an IDEF3 model into a set of alternative precedence networks. In the networks selected, the activities that are resource independent are grouped with a partitioning procedure. In order to increase the efficiency of the search for the best schedule, a procedure based on the Christofides et al. (1987) reduction procedure is introduced to determine a lower bound on the completion time of the hierarchically structured design activity network     

Scheduling flexible machining and assembly systems

Most scheduling papers consider flexible machining and assembly systems as being independent. In this paper, a heuristic two-level scheduling algorithm for a system consisting of a machining and an assembly subsystem is developed. It is shown that the upper level problem is equivalent to the two machine flow shop problem. The algorithm at the lower level schedules jobs according to the established product and part priorities. Related issues, such as batching, due dates, process planning and alternative routes, are discussed. The algorithm and associated concepts are illustrated on a number of numerical examples.     

Combustion efficiency optimization and virtual testing: a data-mining approach

In this paper, a data-mining approach is applied to optimize combustion efficiency of a coal-fired boiler. The combustion process is complex, nonlinear, and nonstationary. A virtual testing procedure is developed to validate the results produced by the optimization methods. The developed procedure quantifies improvements in the combustion efficiency without performing live testing, which is expensive and time consuming. The ideas introduced in this paper are illustrated with an industrial case study.     

Computational Intelligence in Product Design Engineering: Review and Trends

Product design engineering is undergoing a transformation from informal and largely experience-based discipline to a science-based domain. Computational intelligence (CI) offers models and algorithms that can contribute greatly to design formalization and automation. This paper surveys CI concepts and approaches applicable to product design engineering. Taxonomy of the surveyed literature is presented according to the generally recognized areas in both product design engineering and CI. Some research issues that arise from the broad perspective presented in the paper have been signaled but not fully pursued. No survey of such a broad field can be complete; however, the material presented in the paper is a summary of state-of-the-art CI concepts and approaches in product design engineering.     

Locating an obstacle in a 3D finite depth ocean using the convex scattering support

We consider an inverse scattering problem in a 3D homogeneous shallow ocean. Specifically, we describe a simple and efficient inverse method which can compute an approximation of the vertical projection of an immersed obstacle. This reconstruction is obtained from the far-field patterns generated by illuminating the obstacle with a single incident wave at a given fixed frequency. The technique is based on an implementation of the theory of the convex scattering support [S. Kusiak, J. Sylvester, The scattering support, Commun. Pure Appl. Math. (2003) 1525–1548]. A few examples are presented to show the feasibility of the method.     

Evolution of thermal conductivity of In3Sbβ Teγ thin films up to 550 °C

The temperature dependent thermal conductivity of In–Sb–Te thin films has been measured by modulated photothermal radiometry in the 20–550 °C range for samples with different Te content. Significant changes with temperature are observed and ascribed to a sequence of structural transformations on the basis of in‐situ Raman spectra. The data suggest that the as‐deposited material consisting of a mixture of polycrystalline InSb_0.8Te_0.2and amorphous Te first undergoes a progressive crystallization of the amorphous part, mostly above 300 °C. Further increase in temperature above 460 °C leads, for higher Te content in the alloy, to the formation of crystalline In₃SbTe₂, intertwined with a less conductive compound, possibly InTe and/or InSb. Upon cooling to room temperature, the initial polycrystalline InSb_0.8Te_0.2phase is mostly recovered along with other compounds, with a slightly higher thermal conductivity than that of the as deposited material. (© 2016 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)     

Autonomous decision-making: a data mining approach

The researchers and practitioners of today create models, algorithms, functions, and other constructs defined in abstract spaces. The research of the future will likely be data driven. Symbolic and numeric data that are becoming available in large volumes will define the need for new data analysis techniques and tools. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. In this paper, a novel approach for autonomous decision-making is developed based on the rough set theory of data mining. The approach has been tested on a medical data set for patients with lung abnormalities referred to as solitary pulmonary nodules (SPNs). The two independent algorithms developed in this paper either generate an accurate diagnosis or make no decision. The methodology discussed in the paper depart from the developments in data mining as well as current medical literature, thus creating a variable approach for autonomous decision-making     

Planning Product Configurations Based on Sales Data

Manufacturing companies are focusing on mass customization. Delivering products that meet the requirements of individual customers complicates the production process, and diminishes the benefits of the economy of scale. By exploring commonality among products, this complexity can be significantly reduced. To determine product configurations sought by the customers and to produce them in large quantities, a new approach is proposed. The proposed approach uses a modified k-means clustering algorithm to analyze past sales data for capturing prime product configurations. The most suitable configurations are selected by solving an integer-programming model or using a sorting-based algorithm. The proposed approach was tested with an industrial case study involving sales data of large trucks collected over a period of one year.     

Selection of Invariant objects with a data-mining approach

An approach based on data-mining for identifying invariant objects in semiconductor applications is presented. An invariant object represents a set of parameter (feature) values of a process and the corresponding outcome, e.g., process quality. The key characteristic of an invariant object is that its outcome can be accurately predicted in the changing data environment. One of the most powerful applications of invariant objects involves the generation of robust settings for controllers in a multiparameter process. The prediction accuracy of such robust settings should be invariant in time, features, and data form. The notion of time-invariant objects refers to objects for which the prediction accuracy is not affected by time. Analogous to time-invariance, objects can be invariant in the features and the data form. The former implies that the prediction accuracy of a set of objects is not impacted by the set of features selected from the same data set. The outcomes of data-form invariant objects prevail despite the change in the data form (data transformation). The use of data transformation methods defined in this paper is twofold: first, to identify the invariance of objects and secondly, to enhance prediction accuracy. The concepts presented in this paper are illustrated with a numerical example and two semiconductor case studies.