An integrated framework for solving the green supplier selection and order allocation problem in steam procurement

Abstract

Industrial steam procurement is a decision-making challenge that requires balancing cost efficiency, supplier quality, and environmental sustainability. In this study, we address the steam procurement problem by considering green supplier selection and order allocation. To account for the dynamic nature of steam pricing, block-rate pricing policies are used. Due to the discontinuous cost variations caused by block-rate pricing across consumption thresholds, we aim to improve demand forecasting accuracy by developing a time-series ensemble model based on Bayesian optimization. Additionally, we integrate hybrid multi-criteria decision-making techniques to incorporate the qualitative supplier evaluations beyond cost-based criteria. Finally, a multi-objective linear programming model is developed to optimize the trade-offs among the total cost of purchase (TCP), the total value of purchase (TVP), and carbon emissions. We validate the proposed framework with an application to a major manufacturer in Ulsan, Republic of Korea. The optimized procurement strategy increases TVP by 25% and reduces carbon emissions by 10% without raising TCP. We also present a sensitivity analysis that examines the impact of price volatility. Lastly, we further explore multiple scenarios that incorporate renewable energy sources.