Balancing partner preferences for logistics costs and carbon footprint in a horizontal cooperation

Horizontal cooperation in logistics has gathered momentum in the last decade as a way to reach economic as well as environmental benefits. In the literature, these benefits are most often assessed by aggregating all demand and then optimizing the supply chain at the level of the coalition. However, such an approach ignores the individual preferences of the participating companies and forces them to agree on a unique coalition objective. Companies with different (potentially conflicting) preferences could improve their individual outcome by diverging from this joint solution. In order to prevent such individualistic behavior, we propose an optimization framework that explicitly accounts for the individual partners’ interests. In the models presented in this paper, all partners are allowed to specify their preferences regarding the decrease in logistical costs versus reduced CO $$_{2}$$ 2 emissions. Consequently, all stakeholders are more likely to accept the solution, and the long-term viability of the collaboration is improved. The contribution of our work is threefold. First, we formulate a multi-partner multi-objective location-inventory model. Second, we distinguish two approaches to solve such a multi-partner multi-objective optimization problem, each focusing primarily on a single dimension. The result is a set of Pareto-optimal solutions that support the decision and negotiation process. Third, we propose and compare three different solution techniques to construct a unique solution which is fair and efficient for the coalition. Our numerical experiments not only confirm the potential of collaboration but—more importantly—also reveal valuable managerial insights on the effect of dissimilarities between partners with respect to size, geographical overlap and operational preferences.