a data-driven pricing model for distribution systems considering competition

This paper addresses the need to investigate customer behavior and incorporate competition, given the significant shifts in consumer preferences. to achieve this, mathematical modeling is used to design a distribution system that maximizes profits in a competitive market, comprising a wholesaler and multiple retailers across multiple periods and products. customer behavior is captured through a behavior-based pricing process at the retail level, with equilibrium values determined using bi-level programming based on stackelberg modeling, which accounts for asymmetric competition. the model is solved using two distinct approaches: structural modification and data-driven learning models. in the structural modification approach, the bi-level model is linearized and converted into a single-level equivalent. meanwhile, in the data-driven approach, the pricing process is managed using the clique clustering method, which helps develop a rule-based pricing system grounded in data extraction. numerical examples and sensitivity analyses are provided to illustrate the concepts, and the outcomes are compared to highlight managerial implications and avenues for future research.