Lagrangian relaxation-based heuristics for solving uncertain supply chain network design problem addressing supplier’s ESG factor

Purpose The uncertain supply chain network design (SCND) problem, considering suppliers’ environmental, social and governance (ESG) ratings, has been infrequently addressed in the literature. Looking at the importance of ESG ratings in achieving supply chain sustainability, this study aims to fill the gap by incorporating supplier ESG factors into SCND within an uncertain environment. Design/methodology/approach This paper presents a multi-period, multi product SCND model that integrates ESG factors and accounts for uncertainties in supply and production capacities. The model seeks to minimize total operational costs by determining the optimal selection of plant and warehouse locations across multiple time periods. Uncertainties in supply and production capacities are managed through a chance-constrained programming approach with right-hand side stochasticity. A Lagrangian relaxation-based heuristic method is applied to address the NP-hard nature of the problem. Findings The efficacy of the proposed model is illustrated through a numerical example, demonstrating its capability to optimize material flows across the supply chain under uncertain conditions. The model simultaneously considers economic and ESG factors in procurement decisions. A sensitivity analysis is conducted to examine different operational scenarios and their implications on the model’s outcomes. Originality/value To the best of the authors’ knowledge, this study is one of the first to integrate ESG factors into SCND under uncertainty. The proposed model provides a robust framework for decision-makers to optimize supply chain operations while considering both economic and ESG objectives in an uncertain environment.