A hybrid AI/OR decision support tool for backbone communications network design

Abstract

Much of existing DSS literature views the role of human expertise as primarily that of selecting appropriate formal models for solving a problem or synthesizing sequences thereof. Once a model (or model sequence) is determined, values of decision variables are determined by the model(s) alone. Hence, automated methods for facilitating model selection and synthesis have received considerable attention. However, a single model is often not an accurate abstraction of reality. Also, results from multiple formal models often have to be combined heuristically to obtain practical solutions. Thus, in this paper we explore the premise that human expertise needs to interact with formal models during the process of searching for solution values. Specifically, we describe a hybrid decision support tool for the design of backbone communication networks, a problem recognized as being of considerable complexity. An internal representation of the design process that employs a blackboard, a truth maintainence system and dependency directed backtracking, allows human expertise and formal models to jointly determine decision variable values in a uniform manner. The design tool has been implemented using a combination of Lisp and Fortran. Computational experiments indicate that incorporating human expertise during the search process results in superior complete solutions and added flexibility in satisfying ad hoc requirements. We conjecture that this hybrid search approach is not limited to the telecommunication network design problem and can be extended to other applications.