Considering pricing and uncertainty in designing a reverse logistics network

TL;DR

This paper develops a model for reverse logistics that optimizes collection center locations, pricing, and sorting policies under uncertain return quality and quantity, incorporating risk measures to improve profit outcomes.

Contribution

It introduces a MINLP model that integrates CVaR risk assessment into reverse logistics network design, accounting for uncertainty in returns and pricing strategies.

Findings

Considering CVaR improves profit margins.

Risk parameters influence pricing and logistics decisions.

Model adapts conservatively with higher risk weights.

Abstract

Companies try to maximize their profits by recovering returned products of highly uncertain quality and quantity. In this paper, a reverse logistics network for an Original Equipment Manufacturer (OEM) is presented. Returned products are selected for remanufacturing or scrapping, based on their quality and proportional prices are offered to customers. A Mixed Integer Non-linear Programming (MINLP) model is proposed to determine the location of collection centers, the optimum price of returned products and the sorting policy. The risk in the objective function is measured using the Conditional Value at Risk (CVaR) metric. CVaR measures the risk of an investment in a conservative way by considering the maximum lost. The results are analyzed for various values of the risk parameters ({\alpha}, and {\lambda}). These parameters indicate that considering risk affects prices, the classification of returned products, the location of collection centers and, consequently, the objective function. The model performs more conservatively when the weight of the CVaR part ({\lambda}) and the value of the confidence level {\alpha} are increased. The results show that better profits are obtained when we take CVaR into account.