A generalization of dynamic programming for Pareto optimization in dynamic networks

The algorithm in this paper is designed to find the shortest path in a network given time-dependent cost functions. It has the following features: it is recursive; it takes place both in a backward dynamic programming phase and in a forward evaluation phase; it does not need a time-grid such as in Cook and Halsey and Kostreva and Wiecek's ‘Algorithm One’; it requires only boundedness (above and below) of the cost functions; it reduces to backward multi-objective dynamic programming if there are constant costs. This algorithm has been successfully applied to multi-stage decision problems where the costs are a function of the time when the decision is made. There are examples of further applications to tactical delay in production scheduling and to production control.