A p-Robust Green Supply Chain Network Design Model under Uncertain Carbon Price and Demand

Abstract

The problem of designing a multi-product, multi-period green supply chain network under uncertainties in carbon price and customer demand is studied in this paper. The purpose of this study is to develop a robust green supply chain network design model to minimize the total cost and to effectively cope with uncertainties. A scenario tree method is applied to model the uncertainty, and a green supply chain network design model is developed under the p-robustness criterion. Furthermore, the solution method for determining the lower and upper bounds of the relative regret limit is introduced, which is convenient for decision-makers to choose the corresponding supply chain network structure through the tradeoff between risk and cost performance. In particular, to overcome the large scale of the model caused by a high number of uncertain scenarios and reduce the computational difficulty, a scenario reduction technique is applied to filter the scenarios. Numerical calculations are executed to analyze the influence of relevant parameters on the performance of the designed green supply chain network. The results show that the proposed p-robust green supply chain network design model can effectively deal with carbon and demand uncertainties while ensuring cost performance, and can offer more choices for decision-makers with different risk preferences.

Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda

“…The two work together to make the supply chain sustainable and meeting sustainability goals. Brandenburg (2017) states that uncertainties might influence the supply chain configuration decisions, and several other studies propose indicators to guide the decision‐making process within supply chains (Fahimnia & Jabbarzadeh, 2016; Mari et al, 2016; Qiu et al, 2019; Zahiri et al, 2017). Darom et al (2018) suggest that including sustainability into recovery plan evaluation might result in better optimization of resources and environmental impact.…”