Enhancing resilience: a stochastic mathematical model for optimizing investments in sustainable reverse supply chains

The objective of this paper is to determine the effective allocation of financial resources in order to make resilient Sustainable Reverse Supply Chains (SRSC) that are subject to random interruptions. To this end, a stochastic optimization model was proposed that considers the resilient capabilities of absorption, adaptation and recovery, and it was applied in a supply chain in the southeast region of Brazil that deals with lead-acid battery waste by reintroducing it to the production cycle or for the business of recovering its value and minimizing environmental impacts. Hence, four scenarios were analyzed with different probabilities of disruptive events occurring so as to allocate investments. We found that the portion of the costs of the reverse supply chain that include post-event costs and penalties resulting from interruptions remains relatively constant between the different scenarios. However, when analyzing pre-disruptive event investments in alternative transport and purchase of waste from other chains, it was observed that restoring the supply of waste was reestablished well before the deadline for all experiments of lesser severity. This demonstrates the relevance of the proposed model in the decision-making process for investments in resilience involving an SRSC.