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A Novel And Efficient Approach For Materials Demand Aggregation Using Genetic Algorithm

Campus : Bengaluru

School : School of Engineering

Department : Mechanical

Year : 2009

Abstract : Summary Today, web-based material demand aggregation is one of the important as well as the active research areas in the supply chain management. In general, the Demand Aggregation technique synchronizes and combines the requirements with the involvement of a web-based agent to offer a clear picture of purchasing requests throughout the enterprise. The effectiveness of supply chain management also depends on the demand aggregation. The purpose of utilizing material demand aggregation is to offer all the buyers (manufacturers) in a cost effective manner. But the web-based agent faces difficulty in selecting the raw materials suppliers as enormous suppliers supply different kinds of raw materials. Moreover, each supplier offers dissimilar slab rates for different quantities of the raw materials which add additional challenge to the web-based agent. Now, the selection of the suppliers who can fulfill the aggregated demand of raw materials in a cost-effective manner, with optimal slab rate, as long as the quantities supplied is closer to the aggregated demand, by the web-based agent is critical. To work in such a situation, we propose an approach to the web-based agent in conjunction with the aggregation of requirements of the manufacturers, the customary facet in material demand aggregation, by utilizing the Genetic algorithm, one of the popular algorithms in evolutionary computations. Our approach intends to facilitate all the manufacturers in acquiring all the raw materials with optimal slab rates by recognizing the suppliers who can supply the raw materials to all the manufacturers in an optimal cost assisted by the web based agent.

Cite this Research Publication : Tatavarthy Srinivas Rao Balkrishna Rao, Dr N. V. R Naidu, Dr k. Mallikharjuna Babu. "A Novel And Efficient Approach For Materials Demand Aggregation Using Genetic Algorithm", April 2009, Volume No. 9, Page No. 4.

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