| Distribution is a very important component of logistics and supply chain management. Location-Routing Problem (LRP) simultaneously takes into consideration location, allocation, and vehicle routing decisions to design an optimal distribution network. Multi-layer and multi-product LRP is even more complex as it deals with the decisions at multiple layers of a distribution network where multiple products are transported within and between layers of the network. This dissertation focuses on modeling and solving complicated four-layer and multi-product LRPs which have not been tackled yet. The four-layer LRP represents a multi-product distribution network consisting of plants, central depots, regional depots, and customers. The LRP integrates location, allocation, vehicle routing, and transshipment problems.;Through the modeling phase, the structure, assumptions, and limitations of the distribution network are defined and the mathematical optimization programming model that can be used to obtain optimal solutions is developed. Since the mathematical model can obtain the optimal solution only for small-size problems, through the solving phase metaheuristic algorithms are developed to solve large-size problems. GRASP (Greedy Randomized Adaptive Search Procedure), probabilistic tabu search, local search techniques, the Clarke-Wright Savings algorithm, and a node ejection chains algorithm are combined to solve two versions of the four-layer LRP. Results show that the metaheuristic can solve the problem effectively in terms of computational time and solution quality. The presented four-layer LRP, which considers realistic assumptions and limitations such as producing multiple products, limited plant production capacity, limited depot and vehicle capacity, and limited traveling distances, enables companies to mimic the real world limitations and obtain realistic results. The main objective of this research is to develop solution algorithms that can solve large-size multi-product multi-layer LRPs and produce high-quality solutions in a reasonable amount of time. |
