Central robust decision-making structure for reverse supply chain: a real pharmaceutical case

Abstract

Tons of hazardous pharmaceutical waste is introduced to the environment each year, showing the significance of medicines reverse channels. Meanwhile, establishing a pharmaceutical reverse channel is complex and costly, and therefore misestimation of uncertainty in waste collection operations can lead to severe financial losses. As conventional methods for uncertainty often fail to mitigate the risk and protect the system, we propose a central robust approach, inspired by scenario-based robust optimization and supply chain coordination concepts, which can significantly reduce waste management costs in the face of uncertainty. We study the profitability in a two-layer reverse channel, where the initial number of unwanted medications is subject to discrete uncertainty. Then, we extend our problem, assuming that the customer willingness to return the items is subject to random uncertainty. A non-linear programming model, a flexible transfer price agreement, and the Nash-bargaining game model are developed to optimize and coordinate the system performance. The analytical results provide reliable insights for industry owners to manage uncertainty in the collection process. We illustrate that implementing the proposed model boosts the return quantity, leads to a remarkable improvement in the profitability of the entire chain, and improves the system adaptability with respect to changing significant market parameters or revenue fluctuations.