Darren L. Deeter’s research while affiliated with University of Pittsburgh and other places

This paper describes a heuristic optimization approach using genetic algorithms. The method solves general network design problems to optimality, or near-optimality, with respect to reliability. The optimization formulation in this paper relaxes the previous restrictions that appear in the literature. Network design is expanded to include links of differing reliability and to select from multiple choices for each possible link component. This significantly expands the search space, necessitating a heuristic approach, but also is much more reflective of actual communications network design problems. The approach can use either exact or approximate network reliability calculations and its flexibility, effectiveness and efficiency are demonstrated on a series of increasingly constrained all-terminal reliability test problems.

This paper describes a general approach to optimal design of communications networks when considering both economics and reliability. The approach uses a genetic algorithm to identify the best topology of network arcs to collectively meet cost and network reliability considerations. This approach is distinct because it is highly flexible and can readily solve many versions of the network design problem, including formulations not seen in the literature before which are more reflective of actual design scenarios. The method is shown to be effective, computationally efficient and flexible on a suite of diverse test problems. 3 1. Introduction The problem of how to cost effectively design a network so that certain constraints are met and an objective is optimized is relevant in many real world applications such as telecommunications [3, 23, 33], computer networking [2, 13, 37], sewage systems [36], and oil and gas lines [36]. This paper focuses on design of minimum cost reliable ...

This paper describes a general approach to the optimal design of communications networks when considering both economics and reliability. The approach uses a genetic algorithm to identify the best topology of network arcs to collectively meet cost and network reliability considerations. This approach is distinct because it is highly flexible and can readily solve many versions of the network design problem, including formulations not previously seen in the literature that more closely reflect actual design scenarios. The method is shown to be effective, computationally efficient and flexible on a suite of diverse test problems.