Solving Rich Vehicle Routing Problem Using Three Steps Heuristic

Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set

A comparative study of process mediator components that support behavioral incompatibility

Most businesses these days use the web services technology as a medium to allow interaction between a service provider and a service requestor. However, both the service provider and the requestor would be unable to achieve their business goals when there are miscommunications between their processes. This research focuses on the process incompatibility between the web services and the way to automatically resolve them by using a process mediator. This paper presents an overview of the behavioral incompatibility between web services and the overview of process mediation in order to resolve the complications faced due to the incompatibility. Several state-of the-art approaches have been selected and analyzed to understand the existing process mediation components. This paper aims to provide a valuable gap analysis that identifies the important research areas in process mediation that have yet to be fully explored.Comment: 20 Pages, 9 figures and 8 Tables; International Journal on Web Service Computing (IJWSC), September 2011, Volume 2, Number

Identifying Students' Summary Writing Strategies Using Summary Sentence Decomposition Algorithm

The Summary writing is one of the important skills taught in schools. A summary is a condensed version of an existing text. Its production differs from other types of writing where it requires the use of specific strategies. Most research on summary assessments focused on the end product of summary writing instead of its process. Research has shown that lack of strategic skills is a cause of students' difficulties in writing good summaries. There are a few systems available to assist teachers in assessing students summaries based on content and style. But virtually none have been developed to assess the process particularly in identifying the strategies used. To address this need, we propose an algorithm based on summary sentence decomposition to identify students' strategies in summary writing. We first analyzed experts' written summaries, extracted the strategies used in the summaries, formulated a set of heuristics rules to define the strategies and finally transformed the rules using position-based method into summary sentence decomposition algorithm (SSDA). For evaluation, we measured the algorithm's functionality in identifying the different strategies. We also compared its performance against human experts. The results based on 168 summary sentences indicate that the algorithm successfully identified these syntax level strategies: deletion, sentence combination, copy-paste, syntactic transformation and sentence reordering. In comparison to human performance, the algorithm's performance closely matched that of human with 94 accuracy in identifying the syntax level strategies. For future work, the algorithm will be extended to identify the semantic level strategies, diagnose the strategies used and provide constructive feedback

Information security professional perceptions of knowledge-sharing intention in virtual communities under social cognitive theory

Knowledge sharing is an important component of knowledge management systems. Security knowledge sharing substantially reduces risk and investment in information security. Despite the importance of information security, little research based on knowledge sharing has focused on the security profession. Therefore, this study analyses key factors, containing attitude, self-efficacy, personal outcome expectation, and facilitating condition, in respect of the information security workers intention to share knowledge. Information security professionals in virtual communities, including the Information Security Professional Association (ISPA), Information Systems Security Association (ISSA), Society of Information Risk Analysts (SIRA), and LinkedIn security groups, were surveyed to test the proposed research model. Confirmatory factor analysis (CFA) and the structural equation modelling (SEM) technique were used to analyse the data and evaluate the research model. The results showed that the research model fit the data well and the structural model suggests a strong relationship between attitude and knowledge sharing intention. Hypotheses regarding the influence of self-efficacy and personal outcome expectation, to knowledge sharing attitude were upheld. Facilitating condition showed significant influences on moderating between attitude and intention

Application of fuzzy set theory to evaluate the rate of aggregative risk in information security

Organizations use different types of information system to reach their goals. Decision makers are required to allocate a security budget and treatment strategy based on the risk priority of information systems. Each of the information systems has different components or assets. However, there is difficulty in aggregating the risk of each component. In this research a model is created to aggregate the risk of information system components to support decisions. Since there is uncertainty in the information security risk analysis area, we used fuzzy set theory in our model

Intelligent Decision Support Systems for Oil Price Forecasting

This research studies the application of hybrid algorithms for predicting the prices of crude oil. Brent crude oil price data and hybrid intelligent algorithm (time delay neural network, probabilistic neural network, and fuzzy logic) were used to build intelligent decision support systems for predicting crude oil prices. The proposed model was able to predict future crude oil prices from August 2013 to July 2014. Future prices can guide decision makers in economic planning and taking effective measures to tackle the negative impact of crude oil price volatility. Energy demand and supply projection can effectively be tackled with accurate forecasts of crude oil prices, which in turn can create stability in the oil market. The future crude oil prices predict by the intelligent decision support systems can be used by both government and international organizations related to crude oil such as organization of petroleum exporting countries (OPEC) for policy formulation in the next one year.  DOR: 98.1000/1726-8125.2015.0.47.0.0.73.10

Developing an intelligent system to acquire meeting knowledge in problem-based learning environments

Abstract. MALESAbrain[1-3] is an intelligent algorithm which originally is designed for problem-based learning (PBL) environment. Similarly, the algorithm proposed in MALESAbrain can be used to deal the problem of conducting a meeting among learners to solve problems. This project adapts the original MALESAbrain definitions and algorithm to create an intelligent learning tool; then testing the tool in a students ’ meeting to discuss“To build up programming skills for computer science students, do you agree JAVA is a proper language in the first year foundation course for computer science students”? Consequently, this paper concludes that MALESAbrain is a new methodology for meeting, which (1) reduces the unnecessary human intervention and (2) changes a meeting atmosphere from debate to problem-based learning for the knowledge acquisition. Keywords: Problem-based learning (PBL), Knowledge acquisition, MALESAbrain, Artificial knowledge cell (AK-cell

Unsupervised learning in second-order neural networks for motion analysis

This paper demonstrates how unsupervised learning based on Hebb-like mechanisms is sufficient for training second-order neural networks to perform different types of motion analysis. The paper studies the convergence properties of the network in several conditions, including different levels of noise and motion coherence and different network configurations. We demonstrate the effectiveness of a novel variability dependent learning mechanism, which allows the network to learn under conditions of large feature similarity thresholds, which is crucial for noise robustness. The paper demonstrates the particular relevance of second-order neural networks and therefore correlation based approaches as contributing mechanisms for directional selectivity in the retina