Sustainable supply chain planning for biomass-based power generation with environmental risk and supply uncertainty considerations: a real-life case study

This paper addresses the design and planning of a supply chain (SC) system for power generation from biomass by using various technologies. A two-stage stochastic programming model is developed to find an effective design strategy under stochastic and highly seasonal biomass supply. The biomass usage, as a renewable energy source, for the power generation affects the environment and society in multiple ways, such as social expectations, life-threatening issues, and greenhouse gas emissions. As a consequence, in the stochastic model, by the social life cycle assessment (S-LCA) approach, the SC's social impact is guaranteed to be larger than a minimum acceptable rate. Furthermore, the environmental risk of the SC is quantified based on its air pollutant and greenhouse gas emissions and mitigated. To deal with the biomass supply uncertainty, discrete scenarios are generated using a backward scenario reduction approach. Computational results are presented on a real-life case study in Iran to show the stochastic model's applicability in evaluating the economic potential, the sustainability aspects, and the required infrastructure for the planning of the SC system. In addition, to drive managerial insights, sensitivity analysis on key parameters of the optimisation problem is done.

Automated summary

This paper explores the design and planning of a supply chain system for power generation from biomass. By developing a two-stage stochastic programming model, the study analyzes various factors affecting the system, including social and environmental aspects. Through social life cycle assessment (S-LCA), the social impact is quantified and sustainability is improved by reducing environmental risks. The research findings demonstrate the effectiveness of the stochastic model in assessing economic potential and guiding the planning of the supply chain system.