An Intelligent GbMFPA Model for Sales Optimization in Distributed Grid-Market
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Grid market enables a sensible distributed and user-vendor friendly environment for improving the sale, services and incentives. In this paper, a multi-factor evaluation based intelligent GbMFPA model is investigated to optimize the production allocation in bid-based Grid Market. The bid-market model is established with intermediate well-informed setup between grid server and clients for selling rare and antique products. The architectural processing is summarized as Dynamic Price updation and Incentive-and-Profile driven scheduling. The continuous monitoring on customer-interest, bid-count, deadline-criticality and product availability parameters is adopted by the server for cyclic updation on product-price. The framework is also integrated with a multi-aspects based scheduler for effective allocation of available products to the bid-user. The composite evaluation is conducted under product-availability, deadline-criticality, product-popularity and user-trust parameters for incentive-gain based product allocation. This dual-layer processed connected and cyclic framework is designed and implemented to increase user admissibility and to gain maximum profit. The proposed framework is implemented in a real open environment with product characteristic specifications. The price-update and sale-price observations are collected and presented to verify the client side and vendor benefits achieved from this framework. The implementation is performed on seven rare and antique products with relative characterization and environmental configuration. The results identified that a significant gain in the price-hike is achieved from the proposed intelligent GbMFPA system.
KeywordsGrid computing Bid market Scheduling Product allocation Middle-layer
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