A machine learning-based Biding price optimization algorithm approach

Trading companies of used product market are struggling to gain customers attentaion and to sell the products. The aim of this research is to develop a mechanism that can maximize the sale of products while considering profit implications. The literature review classifies the procurement mechanism. Given the limited-supply nature, that also includes unpredictable quality levels and a procurement mechanism that perceives the company offering prices to suppliers on a single-item basis. The academic literature has not covered such a mechanism. Techniques like those that improve the required bidding strategy are reviewed and considered fit to be included in the support tool as the procedures intention to maximize an objective function depending on the bidding price and contain the probability of winning the auction and the profit made from the proceeding, the motivation laid on the approach that predicts the probability. It is determined that this assembles a Response to Reverse Request for Quotation that meets the assumptions of a First-Price Sealed-Bid(FPSD) auction that potentially includes a hidden reservation price.