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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.


Papers
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Journal ArticleDOI
TL;DR: In this article, a stochastic programming based approach to account for the design of sustainable logistics network under uncertainty is proposed, where a solution approach integrating the sample average approximation scheme with an importance sampling strategy is developed.

134 citations

Proceedings ArticleDOI
TL;DR: This paper studies the minimum number of node failures needed to cause total blackout, and shows that in the case of unidirectional interdependency between the networks the problem is NP-hard, and develops heuristics to find a near-optimal solution.
Abstract: In this paper, we study the robustness of interdependent networks, in which the state of one network depends on the state of the other network and vice versa. In particular, we focus on the interdependency between the power grid and communication networks, where the grid depends on communications for its control, and the communication network depends on the grid for power. A real-world example is the Italian blackout of 2003, when a small failure in the power grid cascaded between the two networks and led to a massive blackout. In this paper, we study the minimum number of node failures needed to cause total blackout (i.e., all nodes in both networks to fail). In the case of unidirectional interdependency between the networks we show that the problem is NP-hard, and develop heuristics to find a near-optimal solution. On the other hand, we show that in the case of bidirectional interdependency this problem can be solved in polynomial time. We believe that this new interdependency model gives rise to important, yet unexplored, robust network design problems for interdependent networked infrastructures.

134 citations

Journal ArticleDOI
TL;DR: A generic modeling framework is proposed that continues and extends a recent stream of research aimed at integrating insights from modern inventory theory into the supply chain network design domain and is flexible and general enough to incorporate a variety of important side constraints into the problem.

133 citations

Journal ArticleDOI
TL;DR: It is shown that with careful network design, the backpropagation learning procedure is an effective way of training neural networks for time series prediction, and it is evaluated both for long-term forecasting without feedback, and for short- term forecasting with hourly feedback.
Abstract: This paper describes a non trivial application in forecasting currency exchange rates, and its implementation using a multi-layer perceptron network. We show that with careful network design, the backpropagation learning procedure is an effective way of training neural networks for time series prediction. The choice of squashing function is an important design issue in achieving fast convergence and good generalisation performance. We evaluate the use of symmetric and asymmetric squashing functions in the learning procedure, and show that symmetric functions yield faster convergence and better generalisation performance. We derive analytic results to show the conditions under which symmetric squashing functions yield faster convergence, and to quantify the upper bounds on the convergence improvement. The network is evaluated both for long-term forecasting without feedback (i.e. only the forecast prices are used for the remaining trading days), and for short- term forecasting with hourly feedback. The network learns the training set near perfect, and shows accurate prediction, making at least 22% profit on the last 60 trading days of 1989.

133 citations

Proceedings ArticleDOI
14 Oct 2001
TL;DR: This work considers a directed network in which every edge possesses a latency function specifying the time needed to traverse the edge given its congestion, and proves that for networks with n nodes and continuous, nondecreasing latency functions, there is no approximation algorithm for this problem with approximation ratio less than n/2.
Abstract: We consider a directed network in which every edge possesses a latency function specifying the time needed to traverse the edge given its congestion. Selfish, noncooperative agents constitute the network traffic and wish to travel from a source s to a sink t as quickly as possible. Since the route chosen by one network user affects the congestion (and hence the latency) experienced by others, we model the problem as a noncooperative game. Assuming each agent controls only a negligible portion of the overall traffic, Nash equilibria in this noncooperative game correspond to s-t flows in which all flow paths have equal latency. We give optimal inapproximability results and approximation algorithms for several network design problems of this type. For example, we prove that for networks with n nodes and continuous, nondecreasing latency functions, there is no approximation algorithm for this problem with approximation ratio less than n/2 (unless P = NP). We also prove this hardness result to be best possible by exhibiting an n/2-approximation algorithm. For networks in which the latency of each edge is a linear function of the congestion, we prove that there is no (4/3 - /spl epsi/)-approximation algorithm for the problem (for any /spl epsi/ > 0, unless P = NP); the existence of a 4/3-approximation algorithm follows easily from existing work, proving this hardness result sharp.

132 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202390
2022195
2021432
2020493
2019570
2018573