Network Flow Optimization Research Articles

Cycle flow formulation of optimal network flow problems and respective distributed solutions

In this paper, we use the cycle basis from graph theory to reduce the size of the decision variable space of optimal network flow problems by eliminating the aggregated flow conservation constraint. We use a minimum cost flow problem and an optimal power flow problem with generation and storage at the nodes to demonstrate our decision variable reduction method. The main advantage of the proposed technique is that it retains the natural sparse&#x002F decomposable structure of network flow problems. As such, the reformulated problems are still amenable to distributed solutions. We demonstrate this by proposing a distributed alternating direction method of multipliers &#x0028 ADMM &#x0029 solution for a minimum cost flow problem. We also show that the communication cost of the distributed ADMM algorithm for our proposed cycle-based formulation of the minimum cost flow problem is lower than that of a distributed ADMM algorithm for the original arc-based formulation.

Perbandingan Algoritma A* Dengan Algoritma Greedy Pada Penentuan Routing Jaringan

The increasing development of the Internet today is in line with the complexity of on line on the internet. So the Network flow optimization became the main problems related to the election of the shortest route (routing protocol). Focus on research is to find out and compare the process with the shortest route in the search algorithm is Greedy algorithm and A * in order to reduce the workload of the network. Model comparison algorithm that is done is look at the workings of each algorithm against the determination of the routing path from the sender to the receiver. On the basis of the implementation of the experiment it was found that the algorithm a * greedy algorithm finds its way with the same. but it is clear for the a * would be more effective if applied on a broad network as well as complicated. Because the calculation uses a * definite while greedy simply looked at what is the most short front side towards the next node that was selected when the end result can be greater than the calculation of a

Cross-Layer Network Lifetime Maximization in Underwater Wireless Sensor Networks

A cross-layer design methodology that jointly optimizes the link-layer network flow rate and the physical layer transmission power wherein rate is developed for energy-constrained underwater wireless sensor networks. The objective of the design is to maximize the lifetime of the network operation for a given network topology and a bound of the total transmission time of all links. Initially, each node is set to transmit at the most energy-efficient transmission rate, which is derived using an underwater acoustic channel model. A constrained least square problem then is formulated which yields an analytical solution of the optimal network flow at each link that maximizes the network lifetime. If the timing constraint is not met, a new iteration will begin with the restriction on per-node energy consumption incrementally relaxed. Usually, the algorithm converges within a couple of iterations. Two design examples, a linear configured network, and a rhombus configured network are used to demonstrate the design procedures. Extensive simulations have been performed and superior performance (longer network lifetime) of this proposed design has been observed.

MILP for Optimizing Water Allocation and Reservoir Location: A Case Study for the Machángara River Basin, Ecuador

The allocation of water flowing through a river-with-reservoirs system to optimally meet spatially distributed and temporally variable demands can be conceived as a network flow optimization (NFO) problem and addressed by linear programming (LP). In this paper, we present an extension of the strategic NFO-LP model of our previous model to a mixed integer linear programming (MILP) model to simultaneously optimize the allocation of water and the location of one or more new reservoirs; the objective function to minimize only includes two components (floods and water demand), whereas the extended LP-model described in this paper, establishes boundaries for each node (reservoir and river segments) and can be considered closer to the reality. In the MILP model, each node is called a “candidate reservoir” and corresponds to a binary variable (zero or one) within the model with a predefined capacity. The applicability of the MILP model is illustrated for the Machángara river basin in the Ecuadorian Andes. The MILP shows that for this basin the water-energy-food nexus can be mitigated by adding one or more reservoirs.

Bioenergy production to improve value-creation potential of strategic forest management plans in mixed-wood forests of Eastern Canada

The relationship between forest management and biomass sourcing is fundamental to the achievement of sustainable bioenergy production. Few biomass-sourcing studies have directly addressed the complex interactions between sustainable forest management and additional demand created by bioenergy production. The first objective of this research was to provide a method for estimating profits for a value chain that harvests volumes scheduled in a sustainable forest management plan. The second objective was to measure the financial and sustainable benefits generated by integrating bioenergy production into this value chain. Using data from a large management unit, a forest management plan was developed to generate an annual flow of timber that can be produced sustainably. We then proposed a network flow optimization model that links the optimal solution of a long-term wood supply optimization model with the processing capacity of an industrial network. Results revealed that the weak demand for lower-quality trees could be a serious impediment to achieving full value-creation potential. Simulations of the implementation of a hardwood pellet mill improved the value-creation potential for hardwood pulp (direct effect) and also for other wood products by providing access to otherwise unprofitable mixed-wood forest strata (synergistic effect). Our experiment showed that integrating a lower-quality timber user, such as a bioenergy production unit, could lead to higher financial profits (up to three-fold), and improve alignment between the sustainable forest management objectives and the industrial network capacity (harvested scheduled area increased from 79% to 98%). Implementing a bioenergy mill also grants a higher return on capital employed compared to historical values for the manufacturing sector. Strengthening the link between strategic planning and industrial processing capacity provides new insights into the impact on value-creation of bioenergy production from forest biomass.

Simulation-assisted machine learning.

MotivationIn a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.ResultsWe demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems—three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.Availability and implementationThe Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.Supplementary information Supplementary data are available at Bioinformatics online.

Business intelligence for service parts logistics management

Many leading companies now offer global customers better spare parts services for system maintenance through a complex service logistics network, which extends traditional on-site stocking management to boost profit margins. The major challenge that spare parts service providers face is how to achieve desired service levels at a low cost and minimise excess inventory in the global spare parts supply chain. To address this issue, we demonstrate an inventory redeployment strategy that transforms a conventional spare parts supply chain into a closed-loop, multi-echelon service network capable of redeploying inventory from overstocked to understocked facilities, and thus reducing purchases of high-value spare parts. To assess the quality of our novel approach, we used a network flow optimisation model to analyse the proposed excess inventory redeployment strategy based on an international company's service parts operations. Significant inventory cost savings were identified.

High-Precision Camera Localization in Scenes with Repetitive Patterns

This article presents a high-precision multi-modal approach for localizing moving cameras with monocular videos, which has wide potentials in many intelligent applications, including robotics, autonomous vehicles, and so on. Existing visual odometry methods often suffer from symmetric or repetitive scene patterns, e.g., windows on buildings or parking stalls. To address this issue, we introduce a robust camera localization method that contributes in two aspects. First, we formulate feature tracking, the critical step of visual odometry, as a hierarchical min-cost network flow optimization task, and we regularize the formula with flow constraints, cross-scale consistencies, and motion heuristics. The proposed regularized formula is capable of adaptively selecting distinctive features or feature combinations, which is more effective than traditional methods that detect and group repetitive patterns in a separate step. Second, we develop a joint formula for integrating dense visual odometry and sparse GPS readings in a common reference coordinate. The fusion process is guided with high-order statistics knowledge to suppress the impacts of noises, clusters, and model drifting. We evaluate the proposed camera localization method on both public video datasets and a newly created dataset that includes scenes full of repetitive patterns. Results with comparisons show that our method can achieve comparable performance to state-of-the-art methods and is particularly effective for addressing repetitive pattern issues.

Special Issue on Infrastructures and Algorithms for Scalable Computing

We are happy to present this special issue of the scientific journal Scalable Computing: Practice and Experience. In this special issue on Infrastructures and Algorithms for Scalable Computing (Volume 19, No 3 June 2018), we have selected four papers out of submitted nine, which gone through a peer review according to the journal policy. All papers represent novel results in the fields of distributed algorithms and infrastructures for scalable computing.
 The first paper presents present a novel approach for efficient data placement, which improves the performance of workflow execution in distributed datacenters. The greedy heuristic algorithm, which is based on a network flow optimization framework, minimizes the total storage cost, including efforts to move and store the data from different source locations and dependencies. The second paper evaluated the significance of different clustering techniques viz. k-means, Hierarchical Agglomerative Clustering and Markov Clustering in groupingawaredata placement for data-intensive applications with interest locality. The evaluation in Azure reported that Markov Clustering-based data placement strategy improves the local map execution and reduces the execution time compared to Hadoops Default Data Placement Strategy and other evaluated clustering techniques. This is more emphasized for data-intensive applications that have interest locality. The third paper presents an experimental evaluation of the openMP thread-mapping strategies in different hardware environments (IntelXeon Phi coprocessor and hybrid CPU-MIC platforms). The paper shows the optimal choice of thread affinity, the number of threads and the execution mode that can provide optimal performance of the LU factorization. In the fourth paper, the authors study the amount of memory occupied by sparse matrices split up into same-size blocks. The paper considers and statistically evaluates four popular storage formats and combinations among them. The conclusion is that block-based storage formats may significantly reduce memory footprints of sparse matrices arising from a wide range of application domains.
 We use this opportunity to thank all contributors to this Special Issue: all authors who submitted the results of their latest research and all reviewers for their valuable comments and suggestions for improvement. We would like to express our special gratitude for the Editor-in-Chief, Professor Dana Petcu, for her constant support during the whole process of this Special Issue.

Exact and Heuristic Data Workflow Placement Algorithms for Big Data Computing in Cloud Datacenters

Several big data-driven applications are currently carried out in collaboration using distributed infrastructure. These data-driven applications usually deal with experiments at massive scale. Data generated by such experiments are huge and stored at multiple geographic locations for reuse. Workflow systems, composed of jobs using collaborative task-based models, present new dependency and data exchange needs. This gives rise to new issues when selecting distributed data and storage resources so that the execution of applications is on time, and resource usage-cost-efficient. In this paper, we present an efficient data placement approach to improve the performance of workflow processing in distributed data centres. The proposed approach involves two types of data: splittable and unsplittable intermediate data. Moreover, we place intermediate data by considering not only their source location but also their dependencies. The main objective is to minimise the total storage cost, including the effort for transferring, storing, and moving that data according to the applications needs. We first propose an exact algorithm which takes into account the intra-job dependencies, and we show that the optimal fractional intermediate data placement problem is NP-hard. To solve the problem of unsplittable intermediate data placement, we propose a greedy heuristic algorithm based on a network flow optimisation framework. The experimental results show that the performance of our approach is very promising. We also show that even with divergent conditions, the cost ratio of the heuristic approach is close to the optimal solution.

Cut Redistribution and Insertion for Advanced 1-D Layout Design via Network Flow Optimization

Given a 1-D layout with horizontal wires, cut redistribution technique is used for sliding the line-end cuts in order to merge them vertically or resolve spacing conflicts. A pair of movable cuts could have three possible relations, left-of, right-of, and merge-into. We observe that if the left-right-merge orderings of cuts are fixed, the cut redistribution can be formulated as a network flow problem, which can be solved efficiently. We also find that inserting cuts can resolve the spacing conflicts in some circumstances. According to the observations, we proposed a network-flow-based cut redistribution and insertion method for 1-D layout design. First, the orderings of the cuts are generated according to a predefined distribution. The hyperparameters of the distribution are tuned with Bayesian optimization. In order to resolve the conflicts, the cuts are redistributed and extra cuts are also inserted with network-flow-based method. Afterward, we adopt the complementary e-beam lithography to eliminate the unresolved conflicts. Finally, two wire length reduction techniques have been proposed for two different processes of e-beam, respectively. Experimental results show that compared with the existing integer linear programming-based method that does not consider the cut insertion, our method can achieve $132\times $ speedup and higher quality.

An open-source Python implementation of California's hydroeconomic optimization model

This short communication describes a new open-source implementation of the CALVIN model (CALifornia Value Integrated Network), a large-scale network flow optimization model of California's water supply system. The model is cross-platform, uses common data formats, and connects to several freely available linear programming solvers. Given inputs including hydrology, urban/agricultural demand curves, and variable operating costs, the model minimizes the systemwide cost of water scarcity and operations including surface and groundwater reservoirs, wastewater reuse, desalination, environmental flow requirements, and hydropower. Key outputs include water shortage costs and marginal economic values of water and infrastructure capacity. We benchmark the scalability of different solvers up to roughly 5 million decision variables, using shared-memory parallelization on a high performance computing cluster. Runtimes are reduced by two orders of magnitude relative to the original model when no initial solution is provided, in addition to the benefits such as accessibility and transparency that come with an open-source platform. While this model is specific to California, the data and model structure are separated, so a similar framework could be used in any system where water allocation has been formulated as a network flow problem.

Cloud Based Simulation Model for Traffic Flow Optimization in Online Social Networks

Cloud Based Simulation Model for Traffic Flow Optimization in Online Social Networks

Reservoir Management Using a Network Flow Optimization Model Considering Quadratic Convex Cost Functions on Arcs

The allocation of water resources between different users is a hard task for water managers because they must deal with conflicting objectives. The main objective is to obtain the most accurate distribution of the resource and the associated circulating flows through the system. This induces the need for a river basin optimization model that provides optimized results. This article presents a network flow optimization model to solve the water allocation problem in water resource systems. Managing a water system consists in providing water in the right proportion, at the right place and at the right time. Time expanded network allows to take into consideration the temporal dimension in the decision making. Since linear cost functions on arcs present many limitations and are not realistic, quadratic convex cost functions on arcs are considered here. The optimization algorithm developed herein extend the cycle canceling algorithm developed for linear cost functions. The methodology is applied to manage the three reservoirs of La Haute-Vilaine’s watershed located in the north west of France to protect a three vulnerable areas from flooding. The results obtained with the algorithm are compared to a reference scenario which consists in considering reservoirs transparent. The results show that the algorithm succeeds in managing the reservoir releases efficiently and keeps the flow rates below the vigilance flow in the vulnerable areas.

Hybridized encoding for evolutionary multi-objective optimization of air traffic network flow: A case study on China

This paper presents a novel hybridized indirect and direct encoding (HybrID) genetic algorithm for solving air traffic network flow optimization problems. A heuristic, which uses the Dijkstra algorithm for generating different types of shortest paths on a graph while controlling the weights on each arc, is proposed for selecting optimal flight routes based on current air traffic. A novel HybrID chromosome representation is employed along with the proposed heuristic and a genetic algorithm for optimization. Experiments on synthetic problems and real data of the Chinese airspace show the proposed method outperforms the direct encoding method on efficiency and efficacy metrics.

Dynamic Atomic Congestion Games with Seasonal Flows

We propose a model of discrete time dynamic congestion games with atomic players and a single source-destination pair. The latencies of edges are composed of free-flow transit times and possible queuing time due to capacity constraints. We give a precise description of the dynamics induced by the individual strategies of players and of the corresponding costs, either when the traffic is controlled by a planner, or when players act selfishly. In parallel networks, optimal and equilibrium behavior eventually coincide, but the selfish behavior of the initial players has consequences that cannot be undone and are paid by all future generations. In more general topologies, our main contributions are threefold. First, we illustrate a new dynamic version of Braess paradox: the presence of initial queues in the network may decrease the long-run costs in equilibrium. This paradox can arise in networks for which no Braess paradox was previously known. Second, we show that equilibria are not unique and can induce very different long-run costs. In particular, we give a sequence of networks such that the price of stability is equal to 1, and the price of anarchy is equal to n − 1, where n is the number of vertices. Third, we propose an extension to model seasonalities by assuming that departure flows fluctuate periodically over time. We introduce a measure that captures the queues induced by periodicity of inflows. For optimal and equilibrium flows in parallel networks this measure is the increase in cost compared to uniform departures. The electronic companion is available at https://doi.org/10.1287/opre.2017.1683 .

A Detection Method for Anomaly Flow in Software Defined Network

As a new type of network structure, the Software Defined Network (SDN) provides a new solution for network flow management and optimization, which has made the accurate detection of anomaly SDN flows a hot research topic. This paper presents an SDN-based flow detection method, builds structures for detecting anomaly SDN flows and performs classification detection on the flows using the double P-value of transductive confidence machines for K-nearest neighbors algorithm. The experimental results show that the algorithm proposed achieves a lower false positive rate, higher precision, and better adaptation to the SDN environment than do other algorithms of the same type.

Multiple Objects Tracking With Improved Sparse Representation and Rank Based Dynamic Estimation

This paper presents a novel multiple objects tracking method by constructing an improved tracklet affinity function to enhance the performance of multiple object tracking (MOT) within a network flow optimization framework. The aims of this paper are to improve the appearance affinity model and motion affinity model of the tracklet affinity function, both models being key factors to limit MOT performance in tracking by detection framework. In the proposed method, the tracklet association is considered as a generalized linear assignment problem relied on tracklets affinity that is calculated by both improved sparse representation appearance model and rank-based motion model. This paper uses the detection confidence to identify the important targets and, hence, to form the templates set. Then, a weighted target templates set is used for sparse representation of the appearance model. The rank-based motion model exploits motion estimation based on spatial information to interpolate the missing objects during tracklets association. Furthermore, we consider the inconsistency of two cues in different situations and set weights for them based on their properties to describe affinity between two tracklets. Our method has been evaluated on three challenging data sets, and the experimental results show that the proposed method has a good tracking performance compared with several state-of-the-art multi-object trackers.

Fast Online Tracking With Detection Refinement

Most of the existing multiple object tracking (MOT) methods employ the tracking-by-detection framework. Among them, the min-cost network flow optimization techniques become the most popular and standard ones. In these methods, the graph structure models the MOT problem and finds the optimal flow in a connected graph of detections to encode the accurate track trajectories. However, the existing network flow is not suitable for directly online tracking, where the tracking results depend too much on the initial detections. To solve these problems, we present a fast online MOT algorithm by introducing the minimum output sum of squared error filter. The proposed method can adaptively refine the tracking targets according to the proposed rules of correcting the detection mistakes. Furthermore, we introduce an alternative targets hypotheses to reduce the dependence on detections and adaptively refine the object detection boxes. The experimental results on the MOT 2015 benchmark demonstrate that our method achieves comparable or even better results than previous approaches.

Simultaneous Optimization of Airspace Congestion and Flight Delay in Air Traffic Network Flow Management

Air traffic flow management (ATFM) aims to facilitate the utilization of airspace and airport resources and is critical in air transportation systems. During the past decades, several challenging problems have arisen from this domain and attracted intensive studies. This paper addresses the problem of alleviating the airspace congestion and reducing the flight delays in ATFM simultaneously. We formulate this problem as a multi-objective air traffic network flow optimization (MATNFO) problem. In this MATNFO model, comprehensive ATFM actions, for instance, ground-holding, airborne-holding, rerouting, and speed control, are considered. Meanwhile, a systematic approach, namely route and time-slot assignment (RTA) algorithm, is developed to solve the MATNFO problem. The idea of divide-and-conquer is embedded in the algorithm by sequentially applying both route searching module and time refinement module. Furthermore, for the sake of efficiency, a pre-selection operator is proposed as one heuristic strategy to identify promising solutions and reduce the search space by defining a sector equilibrium metric. Experiments on real data of the Chinese airspace show that the RTA algorithm outperforms an existing competitor and three related multi-objective evolutionary algorithms. In addition, RTA is competent for high-quality real-time air traffic network flow assignment.