On designing a resilient green supply chain to mitigate ripple effect: a two-stage stochastic optimization model

TL;DR

This paper develops a two-stage stochastic optimization model for designing a resilient green supply chain that minimizes costs and emissions while mitigating ripple effects during disruptions like COVID-19.

Contribution

It introduces a multi-stage, multi-period green supply chain model with six resilience strategies considering upstream and downstream disruptions, incorporating uncertainty in a novel way.

Findings

Optimal supply chain structure identified

Resilience strategies effectively mitigate ripple effects

Capacity decline impacts optimal solutions and strategies

Abstract

Disasters and disruptions such as the COVID-19 pandemic can significantly interrupt supply chains and industries. To control these disruptions, decision-makers must focus on supply chain resiliency. This paper proposes a multi-stage, multi-period green supply chain design model and six resilience strategies, with downstream and upstream disruptions taken into account to analyze both the ripple and bullwhip effect, respectively. To control the mentioned disruptions and handle the uncertainties of parameter estimations, a two-stage stochastic optimization approach is devised. The objectives are to minimize the total cost of disruption, and $CO_{2}$ emission under the cap-and-trade mechanism as a government-issued emission regulation. The proposed decision-making framework and solution approach are validated using a numerical experiment followed by sensitivity analysis. The results show the optimum structure of the supply chain and the best resilient strategies to mitigate the ripple effect. Moreover, the effect of a decline in capacity of facilities on the optimal solution and the applied resilient strategies is investigated. This study provides managerial insights to help governments set the proper amount of cap, and supply chain managers to predict the demand behaviour of essential and non-essential products in the event of disruptions.