This thesis studies the logistical problem of serving customer demands by electric vehicles, a currently important problem given the growing emphasis on sustainability in industry. In optimization, this is called the electric vehicle routing problem (E-VRP), and it is generally to computationally demanding to solve with a single comprehensive model. The method of this thesis is therefore a heuristic approach, in which the routing and the charging of the vehicles are done separately. The routing disregards the energy considerations, and the scheduling assigns routes and charging to a fleet of electric vehicles. When considering the charging separately, it can be model more realistically than what is usually done in E-VRP.

A bibliographical review of both E-VRP and of electrical scheduling modeling is conducted, with a focus on which aspects are normally prioritized. Then two different models for the routing, and four different models for the scheduling are described. This routing-scheduling framework is subsequently tested on both artificial trial instances and a real-world problem. Promising results are observed in terms of runtime, with the ability to obtain good solutions on large instances within seconds. The real-world scenario was also solved quickly. Additionally, the results suggest a versatility of the routing-scheduling framework across various E-VRP variations.