Showing papers by "Raymond A. Patterson published in 2002"

Hybrid Fiber Coaxial Network Design

Abstract: Much interest exists in broadband network services to deliver a variety of products to consumers, such as Internet access, telephony, interactive TV, and video on demand. Due to its cost efficiency, Hybrid Fiber Coaxial (HFC) technology is currently being considered by most Telcos and cable companies as the technology to deliver these products. The topological HFC network design problem as implemented by several major companies is a form of the capacitated tree-star network design problem. We propose a new formulation for this problem and present a heuristic based on hierarchical decomposition of the problem. The proposed solution methodology exploits an Adaptive Reasoning Technique (ART), embedded as a meta-heuristic over specialized heuristics for the subproblems. In this context, we demonstrate the dynamic use of an exact solution technique within ART. The generalizability of the proposed solution methodology is demonstrated by applying it to a second problem, the Traveling Salesman Problem (TSP). Computational results are presented for both the HFC network design problem and the TSP, indicating high-quality solutions expending a very modest computational effort. The proposed solution method is found to be effective, and is shown to be easily adaptable to new problems without much crafting, and as such, has a broad appeal to the general operations research community.

The Design of Reverse Distribution Networks: Models and Solution Procedures

Abstract: Reverse distribution, or the management of product return flows, induced by various forms of reuse of products and materials, has received growing attention throughout this decade. In this paper we discuss reverse distribution, and propose a mathematical programming model for a version of this problem. Due to the complexity of the proposed model, we introduce a heuristic solution methodology for this problem. The solution methodology complements a heuristic concentration procedure, where sub-problems with reduced sets of decision variables are iteratively solved to optimality. Based on the solutions from the sub-problems, a final concentration set of potential facility sites is constructed, and this problem is solved to optimality. The potential facility sites are then expanded in a greedy fashion to obtain the final solution. This “heuristic expansion ” was also performed using the solution found with a greedy heuristic to provide a short-list of potential facility sites. Computational tests demonstrate a great deal of promise for this solution method, as high-quality solutions are obtained while expending modest computational effort.