Cooperation is widely seen as a major avenue to achieve more efficient and sustainable resource utilization in numerous transportation problems. Nevertheless, successful implementations require planning models that consider cooperation incentives for individuals to support the formation of coalitions and effective collaboration. The application of allocation methods in cooperative game theory, however, results to be complex and hard, even for simple integer optimization games. As a consequence, these methods are hardly ever applied to real-world combinatorial optimization problems with realistic problem instances. Thus, in this talk, mathematical programming approaches for a cooperative routing problem with profits allocated according to two major allocation methods in cooperative game theory are presented. Results demonstrate that these algorithms enable us to solve problem instances of realistic size to effectively reduce costs and improve resource utilization earned by cooperation. The approaches overcome two major obstacles in modeling cooperation for transportation and related combinatorial optimization problems by applying cooperative game theory.