Joint location and pricing for recycling service planning

Abstract

This paper explores an emerging business model for Waste Recycling Platforms that collect users’ recycling demands online and provide door-to-door recycling services offline. Unlike existing literature, which typically assumes constant user demand, we account for demand uncertainty and develop a location optimization model using two-stage stochastic programming. By analyzing the relationship between random demand and recycling prices, we extend this model to a joint location-pricing model. We also design a Benders Decomposition-based algorithm to solve the model using actual operational data from a Waste Recycling Platform in Beijing. Our findings reveal that incorporating both location and pricing decisions into the model makes it more realistic and capable of yielding better optimal profits. Additionally, parameter analysis suggests that the platform could benefit from moderately relaxing the confidence level of its random constraints and focusing more on controlling operating costs.