Three heuristic procedures for the stochastic,two-machine flow shop problem

Although the deterministic flow shop model is one of the most widely studied problems in scheduling theory, its stochastic analog has remained a challenge. No computationally efficient optimization procedure exists even for the general two-machine version. In this paper, we describe three heuristic procedures for the stochastic, two-machine flow shop problem and report on computational experiments that compare their effectiveness. We focus on heuristic procedures that can be adapted for dispatching without the need for computer simulation or computer-based search. We find that all three procedures are capable of quickly generating solutions close to the best known sequences, which were obtained by extensive search.     

Reduction of system matrices of planar beam in ANCF by component mode synthesis method

A method of reducing the system matrices of a planar flexible beam described by an absolute nodal coordinate formulation (ANCF) is presented. In this method, we focus that the bending stiffness matrix expressed by adopting a continuum mechanics approach to the ANCF beam element is constant when the axial strain is not very large. This feature allows to apply the Craig–Bampton method to the equation of motion that is composed of the independent coordinates when the constraint forces are eliminated. Four numerical examples that compare the proposed method and the conventional ANCF are demonstrated to verify the performance and accuracy of the proposed method. From these examples, it is verified that the proposed method can describe the large deformation effects such as dynamic stiffening due to the centrifugal force, as well as the conventional ANCF does. The use of this method also reduces the computing time, while maintaining an acceptable degree of accuracy for the expression characteristics of the conventional ANCF when the modal truncation number is adequately employed. This reduction in CPU time particularly pronounced in the case of a large element number and small modal truncation number; the reduction can be verified not only in the case of small deformation but also in the case of a fair bit large deformation.     

Long memory time series forecasting by using genetic programming

Real-world time series have certain properties, such as stationarity, seasonality, linearity, among others, which determine their underlying behaviour. There is a particular class of time series called long-memory processes, characterized by a persistent temporal dependence between distant observations, that is, the time series values depend not only on recent past values but also on observations of much prior time periods. The main purpose of this research is the development, application, and evaluation of a computational intelligence method specifically tailored for long memory time series forecasting, with emphasis on many-step-ahead prediction. The method proposed here is a hybrid combining genetic programming and the fractionally integrated (long-memory) component of autoregressive fractionally integrated moving average (ARFIMA) models. Another objective of this study is the discovery of useful comprehensible novel knowledge, represented as time series predictive models. In this respect, a new evolutionary multi-objective search method is proposed to limit complexity of evolved solutions and to improve predictive quality. Using these methods allows for obtaining lower complexity (and possibly more comprehensible) models with high predictive quality, keeping run time and memory requirements low, and avoiding bloat and over-fitting. The methods are assessed on five real-world long memory time series and their performance is compared to that of statistical models reported in the literature. Experimental results show the proposed methods’ advantages in long memory time series forecasting.     

Shortcut fusion rules for the derivation of circular and higher-order programs

Functional programs often combine separate parts using intermediate data structures for communicating results. Programs so defined are modular, easier to understand and maintain, but suffer from inefficiencies due to the generation of those gluing data structures. To eliminate such redundant data structures, some program transformation techniques have been proposed. One such technique is shortcut fusion, and has been studied in the context of both pure and monadic functional programs. In this paper, we study several shortcut fusion extensions, so that, alternatively, circular or higher-order programs are derived. These extensions are also provided for effect-free programs and monadic ones. Our work results in a set of generic calculation rules, that are widely applicable, and whose correctness is formally established.     

Expert-driven genetic algorithms for simulating evaluation functions

In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function’s parameters for computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program consists of a much smaller number of parameters than the expert’s. The extended experimental results provided in this paper include a report on our successful participation in the 2008 World Computer Chess Championship. In principle, our expert-driven approach could be used in a wide range of problems for which appropriate experts are available.     

Visual quality assessment algorithms: what does the future hold?

Creating algorithms capable of predicting the perceived quality of a visual stimulus defines the field of objective visual quality assessment (QA). The field of objective QA has received tremendous attention in the recent past, with many successful algorithms being proposed for this purpose. Our concern here is not with the past however; in this paper we discuss our vision for the future of visual quality assessment research. We first introduce the area of quality assessment and state its relevance. We describe current standards for gauging algorithmic performance and define terms that we will use through this paper. We then journey through 2D image and video quality assessment. We summarize recent approaches to these problems and discuss in detail our vision for future research on the problems of full-reference and no-reference 2D image and video quality assessment. From there, we move on to the currently popular area of 3D QA. We discuss recent databases, algorithms and 3D quality of experience. This yet-nascent technology provides for tremendous scope in terms of research activities and we summarize each of them. We then move on to more esoteric topics such as algorithmic assessment of aesthetics in natural images and in art. We discuss current research and hypothesize about possible paths to tread. Towards the end of this article, we discuss some other areas of interest including high-definition (HD) quality assessment, immersive environments and so on before summarizing interesting avenues for future work in multimedia (i.e., audio-visual) quality assessment.     

The use of coevolution and the artificial immune system for ensemble learning

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm. An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles may be found using only implicit diversity promotion techniques.     

Efficient storage and fast querying of source code

Enabling fast and detailed insights over large portions of source code is an important task in a global development ecosystem. Numerous data structures have been developed to store source code and to support various structural queries, to help in navigation, evaluation and analysis. Many of these data structures work with tree-based or graph-based representations of source code. The goal of this project is to elaborate a data storage that enables efficient storing and fast querying of structural information. The naive adjacency list method has been enhanced with the use of recent data compression approaches for column-oriented databases to allow no-loss albeit compact storage of fine-grained structural data. The graph indexing has enabled the proposed data model to expeditiously answer fine-grained structural queries. This paper describes the basics of the proposed approach and illustrates its technical feasibility.     

Does RFID improve firms’ financial performance? an empirical analysis

Radio frequency identification (RFID) is viewed as a technology that improves supply chain efficiency by enhancing inventory efficiency, optimizing logistics, and coordinating the flow of materials. Although RFID has gained great attention in many business applications, the financial gain that accrues over time from RFID adoption is not well understood. We examine the effects of RFID on firm profits while adjusting for self-selection of adoption choice. We find that firms self-select into a certain adoption mode on the basis of their organizational characteristics. Our results also show that RFID confers significant benefits for firms that have adopted RFID. Interestingly, improved inventory ratio and sales efficiency begin to play a greater role in shaping higher profitability over time for firms that have adopted RFID possibly due to time-consuming processes for them to reap the benefits from RFID. However, we find that the values of RFID that accrue to firms are not universal across firm. That is, our results suggest that RFID confers a significant value for certain firms while it does not for other firms with unobservable disadvantages. In sum, our study sheds new light on what drives firms to adopt RFID and on which firms achieve higher financial performance in a post-adoption period as a result of RFID adoption.     

Inexpensive high availability solutions for the SIP-based VoIP service

In the era of IP-based service, people expect a simple, cheap, and competent Voice over IP (VoIP) service as an alternative of the traditional voice over PSTN. The introduction of the SIP protocol realizes the expectation. Following the cost saving spirit of VoIP, we focus on studying inexpensive high availability solutions for the SIP-based VoIP Service. In this paper, Peer-to-Peer (P2P) based and DN-LB based schemes are mainly compared in the paper. A P2P-based scheme enables an inexpensive high availability solution to the VoIP service by the shared computation resources form P2P nodes. Such a P2P-based solution may be appropriate for an individual VoIP user. However, a caller may take a large volume of messages to find out a callee via the proxy nodes in the P2P network. This inherent property of a P2P network may induce the message overhead and long call setup delay. Based on above, another inexpensive scheme, which is a probing-based name resolution solution, is proposed to achieve high availability and load balancing for the VoIP service. We tag the probing mechanism onto the open source project Domain Name Relay Daemon (DNRD) to become a domain name resolution based load balancer (DN-LB). With DN-LB, all request messages from clients can be fairly distributed to all failure-proof proxy servers in the server farm without using any additional costly intermediate network device and changing the standard SIP architecture. Such a DN-LB based solution may be a good choice for a VoIP service provider.