Dynamic Pricing and Logistics Optimization in the Peruvian LPG Market: An Integrated Model for Margin Maximization

Abstract

We develop an integrated optimization framework combining transportation network modeling and dynamic pricing to maximize net margins in liquefied petroleum gas distribution. Applied to a selected gas firm, our approach addresses simultaneous procurement cost minimization and revenue management under price volatility, capacity constraints, and heterogeneous demand elasticities. Using 2024 operational data (167,475 transactions across 83 customer clusters), we demonstrate that the integrated model reduces unit procurement costs by 1.5\% while increasing net margins by 30\% depending on volume constraints. The transportation component employs linear programming to minimize weighted procurement costs across nine LPG sources. The pricing module estimates cluster-specific demand curves and applies markup rules derived from constant-elasticity theory. Iterative coupling converges in 5--6 iterations to internally consistent solutions. Commercial clusters exhibit near-zero price sensitivity while residential distributors show elastic demand, creating opportunities for price discrimination that capture substantial value in commodity distribution networks.