This study proposes a hybrid fuzzy–optimization framework for EOQ systems in which demand varies with selling price

Abstract:

This study presents a hybrid fuzzy–optimization framework for economic order quantity (EOQ) systems in which demand is influenced by selling price. Traditional EOQ models typically assume deterministic and price-independent demand, limiting their applicability in real-world environments where uncertainty and price sensitivity are significant. To address this gap, the proposed framework integrates fuzzy set theory with mathematical optimization to simultaneously capture uncertainty in key inventory parameters and optimize pricing and replenishment decisions. Demand is modeled as a price-dependent fuzzy function, allowing for flexible representation of vague or imprecise market information. The resulting hybrid model yields optimal order quantities and selling prices that minimize total inventory cost while accounting for uncertainty in demand, holding cost, and ordering cost. Numerical experiments demonstrate the robustness and managerial relevance of the approach, showing that it offers improved decision quality compared with classical EOQ formulations. This framework provides practitioners with a powerful tool for inventory management under uncertain, price-responsive demand conditions.