Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer's incentives

Closed-loop supply chains (CLSCs) are essential for maximising the value creation over the entire life cycle of a product. The design of these networks is increasing due to growing online businesses and rising sustainability awareness. This study develops a multi-objective optimisation model for sustainable CLSC network problem considering supply chain's inherent complexity (multi-echelon, multi-product, multi-mode and multi-period nature) along with price-sensitive demand, consumer's incentives and different quality levels of product. The proposed model seeks to optimise total cost and carbon emissions generated by production, distribution, transportation, and disposal activities. A two-stage algorithm, through the integration of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Co-Kriging approach is utilised to determine the trade-off between costs and carbon emissions in the CLSC network. Data collected from a leading European household appliance company was used to analyse and interpret the developed model. The results show that the proposed two-stage approach provides robust outcomes and is computationally less expensive than the epsilon constraint approach. The study evidences the positive effects of incentive pricing on returned goods in the reverse logistics network and provides multiple trade-off solutions for supply chain managers to make informed decisions.

Automated summary

This study develops a multi-objective optimization model for sustainable closed-loop supply chain network problem. The proposed model considers the inherent complexity of the supply chain and factors such as price-sensitive demand, consumer's incentives, and different quality levels of the product. The results show that the proposed two-stage algorithm provides robust outcomes and is computationally less expensive. The study emphasizes the positive effects of incentive pricing in the reverse logistics network.