Multi-Objective Facility Location Problems: Systematic Literature Review and research agenda

Facility Location deals with the design of mathematical models and solution techniques to find the optimal placement for one or more facilities to be selected from a set of potential candidate sites. Besides their theoretical relevance, Facility Location problems have been intensively studied due to their wide-ranging practical applications in both the private and public sectors, supporting complex and strategic decision-making. As there are often multiple, potentially conflicting, objectives to consider at the modelling stage in these contexts, research in the area of Multi-Objective Facility Location Problems (MOFLPs) is extensive and growing fast. In this study, we aim to delineate the boundaries of existing contributions by focusing on modelling and algorithmic aspects, while also considering the managerial contexts that contribute to the Multi-Objective nature of the resulting problems. To this end, we conduct a thorough Systematic Literature Review of 288 relevant papers published in international peer-reviewed academic journals between 2011 and 2025. Alongside assessing the state of the art, the analysis revealed relevant gaps in the structured integration of uncertainty, time-based dynamics and fairness-oriented metrics, which limits the real-world applicability of MOFLP solutions. To identify the methodological and practical implications of these limitations, we outline key research priorities, including the development of tailored solution strategies, the integration of equity and robustness considerations, and the stronger incorporation of Multi-Criteria Decision Analysis within optimisation processes. Finally, we discuss how these challenges may affect the application of MOFLP approaches in designing Supply Chains and planning public services. • Facility Location problems’ strategic nature leads to Multi-Objective approaches. • Multi-Objective Facility Location problems provide valuable decision-making support. • State-of-art for modelling, algorithmic and managerial aspects need to be assessed. • Thorough analysis of a sample of 288 relevant papers published from 2011 to 2025. • Identify major gaps and future research directions for researchers and practitioners.