Multi-objective stochastic optimization problem: a systematic literature review

In recent years, researchers have widely applied multi-objective stochastic optimization problems (MOSOPs) to decision-making problems involving conflicting objectives under different uncertainties. This study presents a systematic literature review and bibliometric analysis of MOSOP research published between 2018 and 2025, based on 572 published papers indexed in the Scopus database. Through keyword analysis, five main research areas are identified: energy, sustainability, management, engineering, and other fields. This classification represents the main areas where MOSOP has been applied to address uncertainty and trade-offs in real-world systems. The strongest methodological and practical relevance of MOSOP appears in energy systems and closely related sustainability applications. Accordingly, these two domains receive particular emphasis throughout this review. High-impact publications are analyzed to explore application areas, stochastic sources, objective functions, system constraints, and solution algorithms. Overall, the findings highlight that MOSOP related to energy systems represents the most mature and fastest-growing research direction within the literature reviewed. Additionally, the results indicate increased use of MOSOP in renewable energy, energy storage systems, sustainable transportation, electric vehicles, and transportation logistics. Despite its many advantages, there are still gaps in modeling stochastic and conflicting objectives, in advanced solution algorithms, and in interdisciplinary applications of MOSOP. This literature review provides a structured overview of MOSOP literature, highlights research trends and methods, and identifies opportunities for future work to address real-world stochastic optimization challenges.