Inbound Logistics Optimization Under Uncertainty: Systematic Literature Review

Background: Inbound logistics (IL) is a critical subsystem of the supply chain (SC) that supports production destined for the end consumer. Its effectiveness is reduced by uncertainty, which generates inaccuracies in production planning, disruptions, bottlenecks, and waste. Methods: This article presents a systematic review to identify key concepts, variables, and optimization methodologies for IL under conditions of uncertainty. The PRISMA methodology and two article evaluation tools were applied. These methodologies allowed for the identification of 26,555 documents before applying inclusion and exclusion filters. After applying the selection criteria, the analysis concludes with the analysis of 39 articles that stood out for their empirical relevance and methodological soundness. Results: This study makes a theoretical contribution by integrating IL variables, optimization methods, and uncertainty within a structured framework. Conclusions: In practice, it facilitates decision-making by identifying key variables and approaches for designing more robust logistics systems in uncertain environments. Furthermore, the possibility of generating new research focused on optimization under conditions of uncertainty is recognized through the proposal of hybrid optimization models that integrate input variables from IL and formal methods to address uncertainty.