E-Commerce and its Effects in Supply Chain Management

E-Commerce in today’s world is playing an important role in global business. A quantitative and quantitative research was conducted with professional members, employees working in organization which are dealing with E-Business or online working via internet and their views gave an empirical and dynamic approach to make business efficient and effective. Connectivity with market to find best manufacture for goods and for manufacture to finds its buyer for their finished goods. Flow of information become faster and gathering of data become easier to make activities of business to get optimal results. Numbers of goods are available complications to finds goods are removed by complimentary goods. Supplies of goods at proper time in order to meet demand by consumer plays win to win game. Improved methods of technology like RFID make work easier and accessible information about goods to management. Manpower in forms of labor is reducing which leads to decrease in the cost of goods. Ordering cost saves time and gives a specified time both producers to make commodity and consumer for delivery of goods. E-Commerce with contrast of components of supply chain in the form of ware house, logistics, number of listing suppliers, number of listing items on one web in the form of website on one platform give dynamic image to businesses in the race of competitive world. Business transactions done with internet not only reduce time but also make documents or records for longer time able to send data from distant parts of the world. Through websites we can get completive price information and other relevant information for goods.

Advancements in Genetic Programming for Data Classification

This thesis aims to advance the state of the art in data classification using Genetic programming (GP). GP is an evolutionary algorithm that has several outstanding features making it ideal for complex problems like data classification. However, it suffers from a few limitations that reduce its significance. This thesis targets at proposing optimal solutions to these GP limitations. The problems covered in this thesis are: 1. Increase in GP tree complexity during evolution that results in long training time. 2. Lack of convergence to a single (optimal) solution. 3. Lack of methodology to handle mixed data-type without type transformation. 4. Search of a better method for multi-class classification. Through this work, we have proposed a method which achieves significant reduction in bloat for classification task. Moreover, we have presented a Particle Swarm Optimization based hybrid approach to increase performance of GP evolved classifiers. The approach offers better performance in less computational effort. Another approach introduces a new two layered paradigm for mixed type data classification with an added feature that uses data in its original form instead of any transformation or pre-processing. The last but not the least contribution is an efficient binary encoding method for multi-class classification problems. The method involves smaller number of GP evolutions, reducing the computation and suffers from fewer conflicts yielding better results. All of the proposed methods have been tested and our experiments conclude the efficiency of proposed approaches.