Integrating Automotive Production and Intralogistics Planning: A Systematic Literature Review of Optimization Problems and Research Directions

Abstract

The increasing complexity of automotive production systems, driven by mass customization, the transition from internal combustion engine vehicles to electric vehicles, competitive markets, and cost pressure, has intensified the need for advanced optimization across production and intralogistics planning. Optimization refers to the process of determining the best feasible solution to a decision problem according to a defined objective function, subject to given constraints. Whereas most reviews focus on individual problem classes, this systematic literature review adopts an integrated production and intralogistics perspective. Following the PRISMA statement, four research questions on problem types, their classification, solution methods, and trends are addressed by searching four databases (Web of Science, IEEE Xplore, ACM Digital Library, and Science Direct), yielding 194 publications from 1983 to 2025. The analysis shows that production planning, scheduling, resource management, and uncertainty and robustness are the dominant problem types, while metaheuristics, exact methods, and simulation are the most common, frequently hybridized solution methods. Publication activity has risen sharply, with 73.7% of studies appearing since 2019 and material feeding, sustainability, human–robot collaboration, and machine learning showing increased recent publication activity. A taxonomy classifying optimization problems among five dimensions, problem type, decision level, objectives, solution methodology, and real-data usage is proposed to guide researchers and practitioners towards an integrated, industrially deployed optimization.