Multi-Agent System Interaction in Integrated SCM\ud

Coordination between organizations on strategic, tactical and operation levels leads to more effective and efficient supply chains. Supply chain management is increasing day by day in modern enterprises.. The environment is becoming competitive and many enterprises will find it difficult to survive if they do not make their sourcing, production and distribution more efficient. Multi-agent supply chain management has recognized as an effective methodology for supply chain management. Multi-agent systems (MAS) offer new methods compared to conventional, centrally organized architectures in the scope of supply chain management (SCM). Since necessary data are not available within the whole supply chain, an integrated approach for production planning and control taking into account all the partners involved is not feasible. In this study we show how MAS architecture interacts in the integrated SCM architecture with the help of various intelligent agents to highlight the above problem

A system-level mathematical model for evaluation of power train performance of load-leveled electric-vehicles

The power train performance of load leveled electric vehicles can be compared with that of nonload leveled systems by use of a simple mathematical model. This method of measurement involves a number of parameters including the degree of load leveling and regeneration, the flywheel mechanical to electrical energy fraction, and efficiencies of the motor, generator, flywheel, and transmission. Basic efficiency terms are defined and representative comparisons of a variety of systems are presented. Results of the study indicate that mechanical transfer of energy into and out of the flywheel is more advantageous than electrical transfer. An optimum degree of load leveling may be achieved in terms of the driving cycle, battery characteristics, mode of mechanization, and the efficiency of the components. For state of the art mechanically coupled flyheel systems, load leveling losses can be held to a reasonable 10%; electrically coupled systems can have losses that are up to six times larger. Propulsion system efficiencies for mechanically coupled flywheel systems are predicted to be approximately the 60% achieved on conventional nonload leveled systems

Future fuels and engines for railroad locomotives. Volume 1: Summary

The potential for reducing the dependence of railroads on petroleum fuel, particularly Diesel No. 2 was investigated. Two approaches are studied: (1) to determine how the use of Diesel No. 2 can be reduced through increased efficiency and conservation, and (2) to use fuels other than Diesel No. 2 both in Diesel and other types of engines. Because synthetic hydrocarbon fuels are particularly suited to medium speed diesel engines, the first commercial application of these fuels may be by the railroad industry

Second-generation Biofuel Potential in India: Sustainability and Cost Considerations

This study is part of a larger research project on "Promoting Low-Carbon Transport in India", a major initiative of the United Nations Environment Programme (UNEP), hereafter referred to as the Low Carbon Transport (LCT) project in this document. The overall context in which the LCT project has been undertaken is the critical role of the transport sector in reducing greenhouse gas (GHG) emissions. India is currently the fourth largest GHG emitter in the world, although its per capita emissions are less than half the world's average. Furthermore, India's transport sector accounts for 13 percent of the country's energy related CO2 emissions. It is evident that opportunities exist to make India's transport growth more sustainable by aligning development and climate change agendas. At present, India is pursuing a comprehensive set of policies to move the country to a low-carbon growth path. In 2009, India announced that it would reduce the emissions intensity of its gross domestic product (GDP) by 20 percent to 25 percent over the 2005 levels by the year 2020. Specific measures to attain these goals are also being developed through the national missions identified in the National Action Plan on Climate Change (NAPCC) of 2008. The NAPCC recognises that GHG emissions from transport can be reduced by adopting a sustainability approach through a combination of measures such as increased use of public transport, higher penetration of bio-fuels, and enhanced energy efficiency of transport vehicles

Utilization of waste heat in trucks for increased fuel economy

Improvements in fuel economy for a broad spectrum of truck engines and waste heat utilization concepts are evaluated and compared. The engines considered are the diesel, spark ignition, gas turbine, and Stirling. The waste heat utilization concepts include preheating, regeneration, turbocharging, turbocompounding, and Rankine engine compounding. Predictions were based on fuel-air cycle analyses, computer simulation, and engine test data. The results reveal that diesel driving cycle performance can be increased by 20% through increased turbocharging, turbocompounding, and Rankine engine compounding. The Rankine engine compounding provides about three times as much improvement as turbocompounding but also costs about three times as much. Performance for either is approximately doubled if applied to an adiabatic diesel

Utilization of waste heat in trucks for increased fuel economy

The waste heat utilization concepts include preheating, regeneration, turbocharging, turbocompounding, and Rankine engine compounding. Predictions are based on fuel-air cycle analyses, computer simulation, and engine test data. All options are evaluated in terms of maximum theoretical improvements, but the Diesel and adiabatic Diesel are also compared on the basis of maximum expected improvement and expected improvement over a driving cycle. The study indicates that Diesels should be turbocharged and aftercooled to the maximum possible level. The results reveal that Diesel driving cycle performance can be increased by 20% through increased turbocharging, turbocompounding, and Rankine engine compounding. The Rankine engine compounding provides about three times as much improvement as turbocompounding but also costs about three times as much. Performance for either can be approximately doubled if applied to an adiabatic Diesel

Credit Card Fraud Detection on Class Imbalance Dataset

India is growing day by day and a number of enhancements to banking and finance are performed by the government. In this context, the government is frequently supporting digital payments for large as well as small transactions. However, it increases the transparency in payments but in the same ratio, the financial fraud cases are increasing. Among them, credit card fraud is a very common and frequent fraud in the banking system. However, there are a number of automated systems for credit card fraud detection available, but most of them are suffering from the class imbalance problem. The imbalanced training samples are misleading the Machine Learning (ML) algorithm, which leads to an increase in false alarm rates. In this paper, our aim is to contribute an ML method, which is able to deal with the class imbalance issue. Additionally, accurately identify fraud cases. In this context, first, we discuss the class imbalance issue and its available solutions. Then, adopt two appropriate over-sampling methods for handling the class imbalance i.e. ADASYN and SMOTE. Finally, a Binary Convolutional Neural Network has been implemented to classify the over-sampled dataset to classify transactions into fraud and legitimate. The experimental analysis of the model has been carried out based on the Kaggle dataset. The performance results of the proposed technique in terms of accuracy and Area Under the Precision-Recall Curve (AUPRC) are evaluated. According to the obtained results, we found the proposed methodology is enhancing the results and produce up to 99% accurate results