The shortest path problem with resource constraints finds the least cost path between two nodes in a network while respecting constraints on resource consumption. The problem is mainly used as a subproblem inside column generation for crew scheduling and vehicle routing problems. The standard approach for the subproblems is based on dynamic programming. This class of methods is generally effective in practice when there are only a few resources, but it seems to be time-consuming for huge instances with many resources. To handle this problem, we propose a new exact primal algorithm called the multidirectional dynamic programming algorithm (MDDPA). The proposed approach splits the state space into small disjoint subspaces. These subspaces are sequentially explored in several iterations, where each iteration builds on the previous ones, to reduce the dimension of the subspaces to explore and to quickly generate better paths. Computational experiments on vehicle and crew scheduling instances show the excellent performance of our approach compared to the standard dynamic programming method. In particular, MDDPA is able to generate feasible paths with up to 90% of the optimal cost in less than 10% of the time required by standard dynamic programming. This feature is useful in column generation and may greatly reduce the computational effort, because we can stop the MDDPA solution process once columns with sufficiently negative reduced costs are obtained.