Design a Sustainable Supply Chain under Uncertainty using Life Cycle Optimisation and Stochastic Programming

Abstract

This work addresses the life cycle economic and environmental optimisation of a supply chain network considering both design and operational decisions under uncertainty. A general modelling framework is proposed that integrates the functional-unit-based life cycle optimisation methodology and the two-stage stochastic programming approach for sustainable supply chain optimisation under uncertainty. a stochastic mixed-integer linear fractional programming (SMILFP) model is developed to tackle multiple uncertainties regarding feedstock supply uncertainty and product demand uncertainty. To address the computational challenge of solving large-scale SMILFP problems, an efficient solution algorithm that takes advantage of the efficiency of parametric algorithm and the decomposition-based multi-cut L-shaped method is used. A case study based on a spatially explicit model for the county-level hydrocarbon biofuel supply chain is presented in Illinois to demonstrate the applicability of the proposed modelling and algorithmic framework.