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.

Modeling and heuristic worst-case performance analysis of the two-level network design problem

Abstract: This paper studies a multi-facility network synthesis problem, called the Two-level Network Design (TLND) problem, that arises in the topological design of hierarchical communication, transportation, and electric power distribution networks. We are given an undirected network containing two types of nodes—primary and secondary—and fixed costs for installing either a primary or a secondary facility on each edge. Primary nodes require higher grade interconnections than secondary nodes, using the more expensive primary facilities. The TLND problem seeks a minimum cost connected design that spans all the nodes, and connects primary nodes via edges containing primary facilities; the design can use either primary or secondary edges to connect the secondary nodes. The TLND problem generalizes the well-known Steiner network problem and the hierarchical network design problem. In this paper, we study the relationship between alternative model formulations for this problem (e.g., directed and undirected models), an...

Network design for information networks

Abstract: We define a new class of network design problems motivated by designing information networks. In our model, the cost of transporting flow for a set of users (or servicing them by a facility) depends on the amount of information requested by the set of users. We assume that the aggregation cost follows economies of scale, that is, the incremental cost of a new user is less if the set of users already served is larger. Naturally, information requested by some sets of users might aggregate better than that of others, so our cost is now a function of the actual set of users. not just their total demand.We provide constant-factor approximation algorithms to two important problems in this general model. In the Group Facility Location problem, each user needs information about a resource. and the cost is a linear function of the number of resources involved (instead of the number of clients served). The Dependent Maybecast Problem extends the Karger-Minkoff maybecast model to probabilities with limited correlation and also contains the 2-stage stochastic optimization problem as a special case. We also give an O(ln n)-approximation algorithm for the Single Sink Information Network Design problem.We show that the Stochastic Steiner Tree problem can be approximated by dependent maybecast, and using this we obtain an O(1)-approximation algorithm for the k-stage stochastic Steiner tree problem for any fixed k. This is the first approximation algorithm for multi-stage stochastic optimization. Our algorithm allows scenarios to have different inflation factors, and works for any distribution provided that we can sample the distribution.

Knowledge flow network planning and simulation

Abstract: Organizations and communities are held together by knowledge flow networks whether people are aware of them or not. To plan such a network is to describe a formal and optimal flow of knowledge as the basis for effective teamwork. The difficulty is that the result of such planning depends greatly on the planners' experience. This paper proposes a pattern-based approach to knowledge flow design for more effective and efficient planning. The approach starts from basic concepts, uses a knowledge spiral to model knowledge flow patterns and operations, and lays down principles for knowledge flow network composition and evolution. Tools for planning, simulation and management of resource-mediated knowledge flow have been developed and experimentally applied to the work of research teams. The planning tool can help users to define, modify and verify a knowledge flow network and to integrate its components. The simulation tool enables users to study knowledge flow in a visualized network and to develop strategies for adapting networks to changing conditions. The basic idea is to adapt and control logistical processes for knowledge flow within teams.

Benders Decomposition for Discrete–Continuous Linear Bilevel Problems with application to traffic network design

Abstract: We propose a new fast solution method for linear Bilevel Problems with binary leader and continuous follower variables under the partial cooperation assumption. We reformulate the Bilevel Problem into a single-level problem by using the Karush–Kuhn–Tucker conditions. This non-linear model can be linearized because of the special structure achieved by the binary leader decision variables and subsequently solved by a Benders Decomposition Algorithm to global optimality. We illustrate the capability of the approach on the Discrete Network Design Problem which adds arcs to an existing road network at the leader stage and anticipates the traffic equilibrium for the follower stage. Because of the non-linear objective functions of this problem, we use a linearization method for increasing, convex and non-linear functions based on continuous variables. Numerical tests show that this algorithm can solve even large instances of Bilevel Problems.