We just received the great news that our paper “A Competitive Solution for Cooperative Truckload Delivery” (joint work with Behzad Hezarkhani and Marco Slikker) got accept in OR Spectrum. This paper is motivated by a project initiated by two major European logistics providers to create a consortium for cooperative planning of truckload delivery requirements of joining companies in order to, among others, reduce the costs of empty kilometres.
This paper discusses solutions for gain sharing in consortia of logistic providers where joint planning of truckload deliveries enables the reduction of empty kilometres. The highly competitive nature of freight transport markets necessitates solutions that distinguish among the logistics providers based on their characteristics, even in situations with two partners only.
Clearly, the optimal delivery plans of individual companies in most cases include a significant amount of unavoidable repositioning movements, i.e. empty kilometres, among their depots and various pick-up/delivery locations. By taking advantage of the synergy in aggregated networks of depots and delivery requirements, cooperating companies can decrease their overall empty kilometres. As the cooperating companies are usually in direct competition with each other, it is absolutely critical for them to understand how the cooperation would benefit them as well as their competitors. Thus the existence of formal models that unambiguously determine allocations of gains and justify their fairness and/or competitiveness in these situations are imperative to success of such consortia.
To the best of our knowledge, this paper is the first to formally incorporate an endogenous measure of competitiveness in logistics markets. This is done by considering the lowest possible price that a logistics provider is able to charge for a unit-distance of its delivery services within a specified scope without incurring loss. Such a measure reflects the internal efficiency of the logistics providers’ operations. Consequently, our solution takes advantage of information contained in a situation in addition to the savings generated in all possible coalitions to calculate the allocation.