An important supply chain coordination concept in distribution logistics is Vendor Managed Inventory (VMI). With VMI, customers leave the responsibility of managing their inventory and deciding on replenishment frequencies to the vendor (or distributor) who can then integrate these decisions across multiple customers, leading to sizeable cost savings opportunities. The resulting optimisation problem of deciding which customers to serve when and how to combine deliveries into cost-efficient routes is known as the Inventory Routing Problem (IRP). In this Master’s dissertation, we will study the cyclic IRP, in which customers are assumed to have stable product consumption rates. In previous research, customers are assigned to routes, and then the optimal frequency of every tour is determined. In this thesis, the concept of nested routes will be explored, in which not all customers are visited in every iteration of a route. (E.g., replenishing a remote customer could only be included in every second iteration of a route to reduce travel distance, but will increase holding cost at that customer due to the larger delivery quantity.) The goal of this thesis is to elaborate heuristics and local search algorithms to efficiently design and evaluate nested routes for the cyclic IRP. Subsequent computational experiments on a set of benchmark instances should then illustrate the savings potential of nested routes, as well as the performance of the heuristic algorithms being developed. The thesis comprises five chapters. Chapter one has offered a general introduction of the study and the problem description. The second chapter summarises the previous papers and literature written about the different topics explained in this thesis. The third chapter includes the different models used to solve the different set of benchmark instances and, the chapter four contains all the results and comments of every model used. Finally, in the last chapter, the final conclusions and the further research is embraced.

Outgoing