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A machine learning-based Biding price optimization algorithm approach.

Saleem Ahmad1, Sultan Salem2, Yousaf Ali Khan3

  • 1School of Business,Guangdong University of Foreign Studies Guangzhou, Guangdong, PR China.

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Summary

This study introduces a novel mechanism for used product trading companies to maximize sales and profits. It addresses challenges in procurement, quality, and pricing within a First-Price Sealed-Bid auction framework.

Keywords:
Bidding strategyMachine learningMechanismOptimizationProcurementReservation priceTrading companies

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Area of Science:

  • Business and Economics
  • Operations Research

Background:

  • Used product markets face challenges in customer attention and sales.
  • Existing procurement mechanisms do not adequately address limited supply and unpredictable quality.

Purpose of the Study:

  • To develop a mechanism for maximizing product sales in the used product market.
  • To incorporate profit implications into the sales maximization strategy.

Main Methods:

  • Literature review classifying procurement mechanisms.
  • Review of bidding strategy techniques to maximize an objective function.
  • Development of a predictive model for auction win probability and profit.

Main Results:

  • The proposed mechanism functions as a Response to Reverse Request for Quotation.
  • It aligns with First-Price Sealed-Bid auction assumptions, including a potential hidden reservation price.
  • The approach integrates bidding price, win probability, and profit for optimization.

Conclusions:

  • The developed mechanism offers a solution for used product trading companies to enhance sales and profitability.
  • It provides a novel approach to procurement in markets with limited supply and variable quality.
  • The tool supports strategic bidding in a First-Price Sealed-Bid auction context.