Pricing and advertising decisions in a direct-sales closed-loop supply chain

Remanufacturing and recycling of end-of-life products have changed the structure of supply chain networks, and the option of closed-loop supply chains gains popularity. Growing strict environmental and social legislations are expected to enhance sustainability of closed-loop supply chains. This study focuses on the pricing and advertising decisions in a closed-loop supply chain network. Although pricing decisions have been well-studied in this area, there is a little attention to advertising decisions. It is well-known that advertising plays a significant role in influencing customer behavior in returning end-of-life products for the closed-loop supply chain. Therefore, this research develops an operational and tactical plan for promoting advertising programs considering different elasticity effects. As such, the proposed optimization plan considers the pricing decisions in a more compre-hensive view, where the price of similar products in the market and their substitution degree have a high impact on the profitability of manufacturers in a direct-sales closed-loop supply chain. Hence, the main novelty of this paper is to develop a new optimization model with pricing and advertising decisions in a direct-sales closed-loop supply chain. Since the proposed model is more complex than the majority of existing optimization models in the area of closed-loop supply chains, another novelty of this paper is to propose an improvement to the standard particle swarm optimization algorithm using the crowd-learning theory. The developed algorithm is validated by the exact solver and compared with the state-of-the-art algorithms in this research area. An extensive compu-tational experiment is performed considering a number of comparative metrics. The findings show the superior performance of the proposed metaheuristic against the alternative solution approaches in terms of computational time and solution quality. Moreover, some important insights are obtained from this research, which could provide a basis for configuration of pricing schemes and advertising campaigns to improve the efficiency of closed-loop supply chains.

Automated summary

This study focuses on pricing and advertising decisions in a closed-loop supply chain network. While pricing decisions have received significant attention, advertising decisions have been overlooked. The research develops an operational and tactical plan for promoting advertising programs considering different elasticity effects. The proposed optimization model considers pricing decisions in a comprehensive manner and introduces an improvement to the standard particle swarm optimization algorithm using crowd-learning theory. The findings show that the proposed metaheuristic outperforms alternative solution approaches in terms of computational time and solution quality. The research provides valuable insights for configuring pricing schemes and advertising campaigns to enhance the efficiency of closed-loop supply chains.