A Dimension-Reduction Algorithm for Multi-Stage Decision Problems with Returns in a Partially Ordered Set

In this paper a two-stage algorithm for finding non- dominated subsets of partially ordered sets is established. A connection is then made with dimension reduction in time-dependent dynamic programming via the notion of a bounding label, a function that bounds the state-transition cost functions. In this context, the computational burden is partitioned between a time-independent dynamic programming step carried out on the bounding label and a direct evaluation carried out on a subset of “real" valued decisions. A computational application to time-dependent fuzzy dynamic programming is presented

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 bath 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