Efficient network architectures for sensor reachback

A cycle augmentation algorithm for minimum cost multicommodity flows on a ring

A primal partitioning approach for single and non-simultaneous multicommodity flow problems

Decomposition based approaches are recalled from primal and dual point of view. The possibility of building partially disaggregated reduced master problems is investigated. This extends the idea of aggregated-versus-disaggregated formulation to a gradual choice of alternative level of aggregation. Partial aggregation is applied to the linear multicommodity minimum cost flow problem. The possibility of having only partially aggregated bundles opens a wide range of alternatives with different trade-offs between the number of iterations and the required computation for solving it. This trade-off is explored for several sets of instances and the results are compared with the ones obtained by directly solving the natural node-arc formulation. An iterative solution process to the route assignment problem is proposed, based on the well-known Frank Wolfe algorithm. In order to provide a first feasible solution to the Frank Wolfe algorithm, a linear multicommodity min-cost flow problem is solved to optimality by using the decomposition techniques mentioned above. Solutions of this problem are useful for network orientation and design, especially in relation with public transportation systems as the Personal Rapid Transit. A single-commodity robust network design problem is addressed. In this, an undirected graph with edge costs is given together with a discrete set of balance matrices, representing different supply/demand scenarios. The goal is to determine the minimum cost installation of capacities on the edges such that the flow exchange is feasible for every scenario. A set of new instances that are computationally hard for the natural flow formulation are solved by means of a new heuristic algorithm. Finally, an efficient decomposition-based heuristic approach for a large scale stochastic unit commitment problem is presented. The addressed real-world stochastic problem employs at its core a deterministic unit commitment planning model developed by the California Independent System Operator (ISO).

The author describes a formulation of National Airspace System (NAS) traffic management problem as a multicommodity minimum cost flow problem over a network in space-time. It is a formulation of the multidestination delay minimization problem of managing traffic in the NAS. The model's potential for improving over present practice is described in terms of six assumptions of present practice. The space-time network model can address uncertainty in capacity values, restrictive airways capacities, and hubbing. The minimum cost flow formulation further assumes that attempted entries into the system are deterministic, not Poisson, and that actual entries are controllable. The formulation specifically allows dynamic capacities and possesses good potential for short execution time. >

A less-than-truckload carrier collaboration planning problem under dynamic capacities

Traffic management and route guidance are optimization problems by nature. In this article, we consider algorithms for centralized route guidance and discuss fairness aspects for the individual user resulting from optimal route guidance policies. The first part of this article deals with the mathematical aspects of these optimization problems from the viewpoint of network flow theory. We present algorithms which solve the constrained multicommodity minimum cost flow problem (CMCF) to optimality. A feasible routing is given by a flow x, and the cost of flow x is the total travel time spent in the network. The corresponding optimum is a restricted system optimum with a globally controlled constrained or fairness factor L > 1. This approach implements a compromise between user equilibrium and system optimum. The goal is to find a route guidance strategy which minimizes global and community criteria with individual needs as constraints. The fairness factor L restricts the set of all feasible routes to the subset of acceptable routes. This might include the avoidance of routes which are much longer than shortest routes, the exclusion of certain streets, preferences for scenic paths, or restrictions on the number of turns to be taken. Most remarkably is that the subset of acceptable routes can also be interpreted as a mental map of routes. In the second part we apply our CMCF algorithms in a large scale multi-agent transportation simulation toolkit, which is called MATSIM-T. We use as initial routes the ones computed by our CMCF algorithms. This choice of initial routes makes it possible to exploit the optimization potential within the simulation much better then it was done before. The result is a speed up of the iteration process in the simulation. We compare the existing simulation toolkit with the new integration of CMCF to proof our results.

We present a new model and a column generation algorithm for the linear minimum cost multicommodity flow problem. The new model has two differences when compared to the classic formulation based on flows on paths: it has an additional set of variables and an additional set of inequalities. The extra variables are associated with flows on circuits where some arcs are reversed. The extra inequalities assure that an optimal solution to the extended model can be converted to an optimal solution to the path formulation. In order to obtain an optimal solution to the extended model, we use column generation. The extra variables are explicitly considered in the restricted master problem, from the beginning of the column generation process. The subproblem remains a set of shortest path problems. Our present work is related to the use of dual–optimal inequalities in the sense that we extend a model with additional primal variables/dual inequalities. However, our extended model also has additional primal inequalities/dual variables. We present computational results of our method in planar instances and compare them with standard column generation, a bundle method, and a general‐purpose solver. For the tested instances, there is an effective improvement in computational time of the column generation method when the extended model is used. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(1), 3–16 2007

A problem of finding multicommodity minimum-cost flows in tree-like networks is considered. To solve this problem, an algorithm based on the reduction to a problem of finding a single-commodity flow in a network of arbitrary structure is proposed. For single-commodity flows in tree-like networks, an algorithm using a procedure of border reduction is used.

We present the implementation and evaluation of an MPEG-4 multi-source streaming system, in which each receiver can simultaneously retrieve different parts of a streaming object. Unlike the traditional single-source streaming system that may have poor perceived quality at the receiver site due to the network congestion or the heavy-loaded server, our multi-source system aims to maximize the usage of available resources on both end-hosts and the network to provide better quality for the users. In order to achieve this objective, our system employs three key components: a) select appropriate servers, b) distribute load to those selected servers to maximize the overall throughput, and c) avoid self-congestion between different object flows that are requested by different users at the same time. We apply a minimum-cost multicommodity flow (MCMCF) (J. Castro and N. Nabona, 1996) based approach to deal with the above issues. We have extensively evaluated the performance of our system and other different streaming systems through the simulation. The simulation results show that our system can achieve better end-to-end throughput than greedy-based or min-cost-flow-based streaming systems does.

Methods for assigning a temporary storage inventory, which arrives dynamically at storage areas, to storage space are discussed. It was assumed that no storage location was dedicated to a single type of inventory and that inventory positioned at a storage area could be relocated to another storage area. An attempt was made to minimize the total transportation, storage and handling costs of inventory for a given schedule of arrivals and departures. It is shown that the space allocation problem can be formulated as a minimum cost multi-commodity flow problem. A subgradient optimization algorithm was applied to solve the problem. A numerical experiment was conducted to evaluate the performance of the algorithm.

Is sewer-based wastewater treatment really the optimal technical solution in urban water management? This paradigm is increasingly being questioned. Growing water scarcity and the insight that water will be an important limiting factor for the quality of urban life are main drivers for new approaches in wastewater management. Source Separation and Decentralization for Wastewater Management sets up a comprehensive view of the resources involved in urban water management. It explores the potential of source separation and decentralization to provide viable alternatives to sewer-based urban water management. During the 1990s, several research groups started working on source-separating technologies for wastewater treatment. Source separation was not new, but had only been propagated as a cheap and environmentally friendly technology for the poor. The novelty was the discussion whether source separation could be a sustainable alternative to existing end-of-pipe systems, even in urban areas and industrialized countries. Since then, sustainable resource management and many different source-separating technologies have been investigated. The theoretical framework and also possible technologies have now developed to a more mature state. At the same time, many interesting technologies to process combined or concentrated wastewaters have evolved, which are equally suited for the treatment of source-separated domestic wastewater. The book presents a comprehensive view of the state of the art of source separation and decentralization. It discusses the technical possibilities and practical experience with source separation in different countries around the world. The area is in rapid development, but many of the fundamental insights presented in this book will stay valid. Source Separation and Decentralization for Wastewater Management is intended for all professionals and researchers interested in wastewater management, whether or not they are familiar with source separation. ISBN: 9781780401072 (eBook) ISBN: 9781843393481 (Print)

Chapter 1 Applications of network optimization

The minimum-cost multicommodity flow problem involves simultaneously shipping multiple commodities through a single network so that the total flow obeys arc capacity constraints and has minimum cost. Multicommodity flow problems can be expressed as linear programs, and most theoretical and practical algorithms use linear-programming algorithms specialized for the problems'' structures. Combinatorial approximation algorithms yield flows with costs slightly larger than the minimum cost and use capacities slightly larger than the given capacities. Theoretically, the running times of these algorithms are much less than that of linear-programming-based algorithms. We combine and modify the theoretical ideas in these approximation algorithms to yield a fast, practical implementation solving the minimum-cost multicommodity flow problem. Experimentally, the algorithm solved our problem instances (to 1% accuracy) two to three orders of magnitude faster than the linear-programming package CPLEX and the linear-programming based multicommodity flow program PPRN.

We present a new approach to independent component analysis (ICA) by extending the formulation of univariate source signals to multivariate source signals. The new approach is termed independent vector analysis (IVA). In the model, we assume that linear mixing model exists in each dimension separately, and the latent sources are independent of the others. In contrast to ICA, the sources are random vectors, not just single variables, which means the elements of a random vector are closely related to the others. Thus, we assume the dependency between the elements of a source vector. In this manner, we define dependence between vectors as Kullback-Leibler divergence between the total joint probability of vectors and the product of marginal probabilities of vectors. Then, the model allows independence between multivariate source signals represented as random vectors, and dependence between the source signals within the vector representation. The proposed vector density model can for example capture variance dependencies within a vector source signal. There are several applications of this new formulation. In the separation of acoustic sources, the algorithm mitigates the permutation problem, i.e. the usual ICA algorithms applied in to the frequency domain mixture data suffer from the unknown permutation of the output signals. Although there are several engineering solutions to fix this problem after the ICA stage, the proposed method provides a natural solution to the problem by capturing the inherent dependencies of the acoustic signals. It therefore avoids the permutation problem and allows the separation of sources in very challenging environments for many sound sources.

We consider the problem of multiterminal source-channel communication where a number of distributed and possibly correlated sources are transmitted through an orthogonal multiple access channel to a common destination. We provide a characterization of the optimal tradeoff between the transmission cost Gamma and the distortion vector D as measured against individual sources. Our approach consists of two steps: 1) a multiple-letter characterization of the rate-distortion region of the multiterminal source coding and 2) a source-channel separation theorem ensuring that all achievable pairs of (Gamma, D) can be obtained by combining the rate-distortion region and the orthogonal multiple access channel capacity region. As a corollary, we determine the optimal power and distortion tradeoff in a quadratic Gaussian sensor network under orthogonal multiple access, and show that separate source and channel coding strictly outperforms the uncoded (amplify-forward) transmission, and is in fact optimal in this case. This result is in sharp contrast to the case of nonorthogonal multiple access for which separate source and channel coding is not only suboptimal but also strictly inferior to uncoded transmission.