Development of a database and decision support system for performance evaluation of soccer players

In this study, we investigate the general parameters to evaluate the performance of soccer players and develop a database for performance evaluation of soccer players and a relevant decision support system (DSS) to help people such as technical director. In the proposed DSS, the data is collected by Data Collectors by using a proposed database. It helps the technical director to realize the performances of soccer players quickly with real-time during games

A sector analysis for RFID technologies: fundamental and technical analysis for financial decision making problems

Automatic identification technologies have been used in a wide range of applications for reducing the amount of time and labor needed to input data and improving data accuracy. As an important automatic identification technology, radio frequency identification (RFID) technologies allow contactless reading and these technologies are particularly successful in manufacturing and other environments where traditional identification technologies such as bar codes can not perform well. By integrating the RFID technology into their business models, companies may save time, lower labor cost, improve products quality and provide better service. RFID is the wireless technology that uses RF communication to identify, track and manage objects and collect and store data. RFID technology enables companies to develop applications that create value by tracking and identifying objects, animals or people. Business applications of RFID technology can be seen in areas such as manufacturing, supply chain management, software integration, security systems, asset tracking and many others. RFID technology was predicted to be one of the “top ten” technologies in 2004 by CNN. Although, the RFID market is less than five years old, it has been applied to many different industries, from retail industry to logistics, or from healthcare to service business industry – and it is still growing. Particularly, RFID has fundamental influences on today's retailing and supply chain management for applications like asset tracking the inventory control and management. RFID technology also finds major application in mobile phones and is widely used in toll collection of highways, for payments in restaurants, vending machines, retail and parking lots. There are a wide range of RFID systems currently being used or being developed. Examples to these systems include but not limited to the following; automatic vehicle and personnel access control for security (Simpson, 2006), airport passenger and baggage tracking (Ferguson, 2006), tracing blood for cutting down errors such as giving patients wrong blood types (Ranger, 2006), payment process systems (Ramachandran, 2006), production control in manufacturing (Liu & Miao, 2006), transfusion medicine (Knels, 2006) real-time inventory control by automated identification of items in warehouses, tracking and management of physical files, tracking of books in the libraries (Shadid, 2005). For some other applications, interested reader is referred to (Finkenzeller, 2003; Smith, 2004). RFID solution providers claim that their technology and solutions bring significant benefits and have valuable advantages in practice. As new RFID solutions being developed and more RFID tags and equipments being used, these solutions will become more cost effective and RFID businesses are expected to grow rapidly. Since RFID is fairly new, it’s difficult to measure resulting sales increases or heightened customer satisfaction quotients. On the other hand, according to IDC estimation (IDC is a subsidiary of International Data Group, a leading technology media, research, and events company and provider of market intelligence, advisory services, and events for the information technology, telecommunications, and consumer technology markets), companies in the retail sector will spend nearly $1.3 billion on RFID in their supply chain operations in 2008, compared to about$91.5 million in 2003 which corresponds to annual growth rate of 70 percent. In a similar look; the Wireless Data Research Group projected that the global market for RFID increased from $1 billion in 2003 to$3 billion in 2007 (Asif & Mandviwalla, 2005). There are two major drivers of this growth. The first one is the adoption of RFID technology by major retailers and government agencies. The second one is the reduction in the price of RFID tags, readers, and IT systems required to deploy RFID. Given the huge potential of RFID technology, there has been a huge emergence of RFID specialty companies and the development of RFID practices within many market-leading companies. Due to huge emergence, it is desirable to make a sector analysis. In this study, we perform a sector analysis for RFID technologies for researchers and analysts. We investigate public RFID companies traded on the stock exchange markets, summarize their financial performance, describe their RF products, services, and applications, and perform fundamental and technical analysis

Augmented neural networks and problem-structure based heuristics for the bin-packing problem

In this paper, we apply the Augmented-neural-networks (AugNN) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority- rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which sub problems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem-structure based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 seconds per problem. We also discuss the computational complexity of our approach