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networks caused by rising energy costs and fierce competition among carriers. Additionally, society demands higher standards of ecological sustainability management in the transportation business sector (Leonardi and Baumgartner 2004). The fact that the three biggest companies in the world in the logistics sector (UPS, Deutsche Post, FedEx) are CEP service providers illustrates the potential savings of resources that can be achieved by op-timizing the delivery networks of these companies (Klaus and Kille 2004). Several thousand vehicles and drivers are used in order to guarantee the in-time delivery of the packages. In many cases last mile delivery is provided by motor carriers which are organized as small independent companies and act as subcontractors of the CEP service providers. Current optimization approaches for CEP networks mostly rely on the analysis of ex-post data by calculating the optimal location of hubs and depots for the CEP service providers in the network for the long term perspective and by employing tour planning mechanisms for the medium and short term perspective. However, the potential of this kind of ex-post analytics is now nearly completely exhausted. To our knowledge there is no short term ex-ante optimization for the operative handling of last mile delivery planning. Actual optimization procedures for CEP networks lack the following properties:  solutions that allow a simulation-based tour planning without predefined fixed delivery areas while simultaneously allowing for the exploitation of driver learning,  methods that allow the short term exchange of transportation tasks be-tween subcontractors in order to allow a better integration of constraints into the current tour planning methods,

Informationssysteme in Transport und Verkehr: Collaborative Optimization of Last Mile Networks for Courier, Express and Parcel Delivery Services