A Genetic Algorithm for a Joint Routing and Scheduling Problem in Heterogeneous Networks

Abstract

In this paper, we address the information routing problem in heterogeneous decision networks with known messages sizes. A decision network is a set of connected nodes in which some nodes are decision makers (DMs) requesting information, others are information providers (sources) or neutral nodes. An information might be relevant to many DMs and can be provided by different sources with different accuracies. The information value for each DM is modelled as a time dependent utility function. The problem is therefore to generate a set of efficient routing plans to satisfy the DMs’ requests. The congestion problem is solved by determining the optimal transmission schedule along the chosen paths. The joint routing-scheduling problem is modelled as a bi-objective optimization problem that maximizes the overall utility and reliability of the generated paths. A multiobjective genetic algorithm (MOGA) is proposed to solve such an NP-hard problem. We show through empirical experiments that the MOGA provides a representative sample of the efficient set. We also develop an upper bound for the first objective, to validate the quality of the generated potentially efficient solutions.