Algorithms for min-buying in networks

TL;DR

This paper develops models and heuristics for pricing strategies in fuel retail networks, focusing on min-buying and price-matching decisions, incorporating buyer demand sensitivity and network structure adaptations.

Contribution

It introduces a bipartite graph model for min-buying, extends pricing to a binary logit demand model, and proposes MIP formulations and heuristics for practical problem-solving.

Findings

Heuristics perform well on large instances.

Price-matching significantly impacts demand and pricing strategies.

Demand sensitivity influences optimal pricing and buying decisions.

Abstract

The paper is motivated by pricing decisions faced by forecourt fuel retailers across their outlets on a road network. Through our modelling approach we are able adapt the network structure to a bipartite graph with demand nodes representing volumes of fuel from customers using a specific route that connects to the seller's outlet nodes that intersect that route on the network. Customers may have their demand satisfied at the lowest priced competitor on their route. However, the seller can satisfy some or all of this demand by matching or beating this price via one of their outlets intersecting the route. We give a practical extension to min-pricing by considering a binary logit variant for buyers evaluating the choice between two sellers. We derive two MIP formulations for min-buying in the case of general demand. We also propose several constructive heuristics, based on insertion and selection operations, suitable for problem instances beyond the scope of the exact methods. The performance of models and algorithms are evaluated in a numerical study and develop insights from the results. Importantly, we are able to highlight the value of price-matching decisions under buyer demand sensitivity.