Publication

Abstract : In the field of material demand aggregation, the manufacturers face a great demand for a variety of raw materials. And these raw materials are likely to be supplied by different suppliers at different costs. They also opt to offer slab rates for certain amount of raw materials. But the slab rate and the quantity for which the slab rate is offered differ from one supplier to anotherThe paper aims at optimizing the above problem by genetic algorithm. Also, the periods of requirements for the raw materials so varies from one manufacturer to another. For instance, one manufacturer may be in need of raw materials within the tenth of a month and another manufacturer may be in the want of the same raw materials within the fifth. Probably the demand placing by a manufacturer for any raw materials also varies. If it is preferred to buy the raw materials beforehand to enjoy high slab rate, certain manufacturers will be affected because of the high inventory costs incurred as their requirement periods of raw materials will be more. Similarly, if it is suggested to purchase the raw materials just in time in order to avoid the inventory costs, the manufacturers may miss the benefits of high slab rates. In this paper it is demonstrated that web based agents are having the responsibility of analyzing the periods of purchasing of raw materials for each manufacturer and to select the better period for all manufacturers to purchase the raw materials. So that the manufacturers can enjoy the benefits of slab rates and they can also minimize the inventory costs so that the manufacturers can get cost-effective raw materials.As the manufacturers are remotely located we use the hub and spoke mechanism to identify the central location wherein the bulk material is received and distributed to all the manufacturers