Network planning and design

About: Network planning and design is a research topic. Over the lifetime, 12393 publications have been published within this topic receiving 229776 citations. The topic is also known as: network design.

Hybrid wireless-optical broadband access network (WOBAN): network planning using Lagrangean relaxation

Abstract: The concept of a hybrid wireless-optical broadband access network (WOBAN) is a very attractive one. This is because it may be costly in several situations to run fiber to every home (or equivalent end-user premises) from the telecom central office (CO); also, providing wireless access from the CO to every end user may not be possible because of limited spectrum. Thus, running fiber as far as possible from the CO toward the end user and then having wireless access technologies take over may be an excellent compromise. How far should fiber penetrate before wireless takes over is an interesting engineering design and optimization problem, which we address in this paper. We propose and investigate the characteristics of an analytical model for network planning, namely optimum placements of base stations (BSs) and optical network units (ONUs) in a WOBAN (called the primal model, or PM). We develop several constraints to be satisfied: BS and ONU installation constraints, user assignment constraints, channel assignment constraints, capacity constraints, and signal-quality and interference constraints. To solve this PM with reasonable accuracy, we use ldquoLagrangean relaxationrdquo to obtain the corresponding ldquoLagrangean dualrdquo model. We solve this dual problem to obtain a lower bound (LB) of the primal problem. We also develop an algorithm (called the primal algorithm) to solve the PM to obtain an upper bound (UB). Via simulation, we compare this PM to a placement heuristic (called the cellular heuristic) and verify that the placement problem is quite sensitive to a set of chosen metrics.

Heuristic optimization of network design considering all-terminal reliability

Abstract: 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.

Construction of a real-world bilevel linear programming model of the highway network design problem

Abstract: The formulation of the highway network design problem (NDP) as a bilevel linear program (BLP) allows more realistic solutions taking into account the reaction of users to improvements made by the system. In this paper, a conceptual framework for the optimization of investments in inter-regional highway networks in developing countries is proposed. The model is applied to the Tunisian network using actual data. Considerable effort was expended to make the implementation as realistic as possible, taking into consideration travel times, operating costs, accident costs, improvement costs, conservation laws, and effects of intra-regional flows. A new formulation allowing the incorporation of any improvement cost functions, including non-convex and non-concave functions, is introduced.

Large-Scale, Less-than-Truckload Service Network Design

Abstract: We present a novel formulation for the service network design problem in the context of large-scale, less-than-truckload (LTL) freight operations. The formulation captures the basic network design constraints; the load-planning requirement that all freight at a location, irrespective of the freight's origin, loads to the same next terminal; and other important LTL-specific requirements. Our modeling scheme fragments the underlying massive network design model with up to 1.3 million 0--1 variables and 1.3 million rows into a separate and efficient integer programming (IP) problem for each destination terminal along with a coordinating master network design problem. We produce high-quality solutions in very reasonable CPU times (∼2 hours) using slope scaling and load-planning tree generation with corresponding potential annual savings of $20--25 million dollars for the target company for which the research was conducted.

An information criterion for optimal neural network selection

Abstract: The choice of an optimal neural network design for a given problem is addressed. A relationship between optimal network design and statistical model identification is described. A derivative of Akaike's information criterion (AIC) is given. This modification yields an information statistic which can be used to objectively select a 'best' network for binary classification problems. The technique can be extended to problems with an arbitrary number of classes. >