This paper reviews the state-of-the-art in facility location models for distribution system design. It discusses various types of models, including continuous, network, and mixed-integer programming models, as well as their applications. Continuous location models, such as the Weber problem, focus on minimizing distances between facilities and demand points. Network location models, like the p-median problem, aim to minimize the sum of distances between nodes and the nearest facility. Mixed-integer programming models are used to handle capacity constraints and multiple sourcing decisions. The paper also addresses dynamic and probabilistic models, which account for changing conditions over time and uncertainty in input data. Key considerations include the trade-off between fixed and variable costs, the impact of capacity constraints, and the need for robust solutions in the face of uncertainty. The paper highlights the importance of considering different objectives, such as minimizing costs or maximizing market share, and the role of various solution methods, including exact algorithms and heuristics. It concludes with a discussion of the practical relevance and challenges of applying these models in real-world scenarios.This paper reviews the state-of-the-art in facility location models for distribution system design. It discusses various types of models, including continuous, network, and mixed-integer programming models, as well as their applications. Continuous location models, such as the Weber problem, focus on minimizing distances between facilities and demand points. Network location models, like the p-median problem, aim to minimize the sum of distances between nodes and the nearest facility. Mixed-integer programming models are used to handle capacity constraints and multiple sourcing decisions. The paper also addresses dynamic and probabilistic models, which account for changing conditions over time and uncertainty in input data. Key considerations include the trade-off between fixed and variable costs, the impact of capacity constraints, and the need for robust solutions in the face of uncertainty. The paper highlights the importance of considering different objectives, such as minimizing costs or maximizing market share, and the role of various solution methods, including exact algorithms and heuristics. It concludes with a discussion of the practical relevance and challenges of applying these models in real-world scenarios.