Network Flow Method Research Articles

Pre-Coded Inter-Satellite Routing Algorithm with Load Balancing for Mega-Constellation Networks

In order to achieve global multiple seamless coverage, space-based internet usually adopts low Earth orbit (LEO) mega-constellation networks structure, which has the characteristics of high network topology dynamics, limited on-board computing and storage capacity, and uneven distribution of ground traffic. Such features may cause problems such as high transmission delay, network congestion and link interruption. Establishing a stable, efficient and balanced satellite communication link can effectively alleviate the performance of the transmission delay, load balancing, and network throughput. Taking advantage of the regularity of network topology, a pre-coded inter-satellite routing algorithm with load balancing is proposed, which includes 3 parts: (a) the routing sequence coding method and the concept of gateway satellite Service Region (GSSR) are proposed; (b) the initial routing sequence of GSSR is generated based on the maximum network flow method under the ideal situation of uniform satellite traffic distribution; (c) aiming at the uneven distribution of traffic, the Sinkhorn algorithm is used to improve the load balancing performance of inter-satellite links. Simulation results show that, for the Starlink Group-4 constellation, the proposed method can maintain a low transmission delay and improve the load balancing together with the network throughout performance with minimal hops and low time complexity.

Berezinskii-Kosterlitz-Thouless transition from neural network flows.

We adopt the neural network (NN) flow method to study the Berezinskii-Kosterlitz-Thouless (BKT) phase transitions of the two-dimensional q-state clock model with q≥4. The NN flow consists of a sequence of the same units that proceed with the flow. This unit is a variational autoencoder trained by the data of Monte Carlo configurations in unsupervised learning. To gauge the difference among the ensembles of Monte Carlo configurations at different temperatures and the uniqueness of the ensemble of NN-flow states, we adopt the Jensen-Shannon divergence (JSD) as the information-distance measure "thermometer." This JSD thermometer compares the probability distribution functions of the mean spin value of two ensembles of states. Our results show that the NN flow will flow an arbitrary spin state to some state in a fixed-point ensemble of states. The corresponding JSD of the fixed-point ensemble takes a unique profile with peculiar features, which can help to identify the critical temperatures of BKT phase transitions of the underlying Monte Carlo configurations.

Optimal road matching by relaxation to min-cost network flow

• Proposed an optimization model to achieve the global optimal road network matching; • Proposed a multivariate matching relation adaptive min-cost network flow method; • Constraints-based iterative relaxation is imposed to reduce matching ambiguities. Road matching is the prerequisite for integrating different data to obtain a complete, correct road network. The growth of multi-source data calls for the advancement of matching methods. There are many methods of road matching, ranging from traditional methods using threshold or weighted judgment to methods based on statistical and optimization models. This article provides an optimal matching method by relaxation to min-cost network flow. The matching problem is considered an optimization problem, i.e., finding a plan to match features in two datasets to minimize the total difference. The optimization-based matching method can overcome traditional method’s suboptimal nature. Referencing more road information than the other methods make our method superior in its flexibility and applicability to different matching relationships. Additionally, the iterative relaxation by angle, flow, and shortest path-based constraints is introduced to reduce ambiguous matching. We demonstrate the model in real-world experiments. Our method achieves a better result than the other four methods: it has a more stable performance for matching pairs of more complicated correspondence relations and easily confused situations. The average matching precision, recall, and f-score were 0.985 in two test sites.

ECO routing based on network flow method

In this paper, a new ECO routing approach to the problem of resolving pin inaccessibility on standard cells is presented. The conventional ECO routing approaches conceptually divide the rerouting path search problem for a set of target nets to rip-up and reroute into two interdependent tasks: (i) (Off-cell routing) repairing the routing path between the net terminal outside of the cell and the cell boundary and (ii) (On-cell routing) repairing the routing path between the cell boundary and the pin on the cell where they have conventionally performed tasks (i) and (ii) iteratively for each target net. Yet, there is no easy way to accurately predict the final result of task (ii) for all nets during task (i). In our approach, we place the primary importance on resolving pin inaccessibility on cells. Consequently, we perform on-cell routing for all ripped-up nets simultaneously to resolve the pin inaccessibility first by utilizing flexible cell boundary access points and then to adjust the sub-paths outside of the cell. For a number of benchmark circuits, it is shown that our approach of early consideration of entire on-cell routing is very effective, resolving the pin inaccessibility of 71.09% out of all nets for which a commercial ECO router has failed to find legal routes.

Residue Classification-Based Robust Phase Unwrapping in High-Noise Region

Synthetic aperture radar interferometry can obtain information on the surface of the earth. In fact, the accuracy of phase unwrapping directly affects the accuracy of the digital elevation model, but the problem that has been puzzling people is the low accuracy of unwrapping in high-noise regions. To solve this problem, we propose a new approach based on an improved branch-cut method, which classifies the branch cut. First, connect the short branch cuts and then use the network-flow method to connect the long branch cuts. By categorized branch cuts, each type of branch cut processes part of the residues and divides the region with high residue density into two regions with low residue density for unwrapped, which solves the problem of low phase unwrapping accuracy in high-noise regions. The experimental results of simulation and measured data show that RCBC method shows high unwrapping accuracy in high-noise regions, very short total length of connected branch cuts, and strong robustness.

A branch and bound algorithm for optimal television commercial scheduling.

In the current era of multimedia, television (TV) plays an important role in transmitting advertising messages. In addition, advertising is the primary source of revenue for the TV industry. Thus, a critical issue for TV stations is the scheduling of commercials in suitable advertising breaks on different TV channels to maximize revenue and minimize penalties. This type of TV commercial scheduling problem is similar to the machine scheduling problem, and both have availability constraints. However, the literature on TV commercial scheduling has not considered this perspective. Motivated by this, we consider the problem of scheduling commercials with specific service-level requirements on TV channels while minimizing the maximum lateness. The lateness of a commercial is defined to be its completion time minus its due date, and the maximum lateness is the maximum value of lateness among all commercials. We propose an exact branch and bound algorithm based on the LFJ (least flexible job first)/EDD (earliest due date first) rules and network flow methods, which were developed to solve the machine scheduling problem with availability constraints. Computational analysis shows that the bounding scheme proposed is highly effective, and a very low percentage of nodes is generated by the branch and bound algorithm. The algorithm can obtain an optimal solution for the problem.

Coupling potential-forced network flow analysis based on potential energy conservation rules and network flow method

An integrated energy system that uses various networks to transfer electrical power, natural gas, heating, and cooling energy has been applied widely in recent decades. Heating energy flow is a typical coupling network flow consisting of pressure flow and thermal energy flow. It will be attractive to develop a similar network flow method for analyzing the general energy networks, including coupling network flows. In this work, two thermodynamic state properties in the energy networking transmission process, pressure, and temperature are proved as fundamental physical potentials in the network transmission process. It is found that the potential energy is conserved at the mixing node, and the generic network flow can be classified as fundamental potential-forced network flows or decoupled as the combination of potential-forced network flows. By comparing the transmission speed differences between different potentials, the unilateral continuity problem is found in the coupling network flows. The directional nodal potential method (DNPM) for modeling potential-forced network flows that capture the physical characteristics of pipeline transmission and nodal mixing for networking transmission is developed. The results show that the DNPM can unify modeling and analyzing all 3 types of typical potential-forced network flows, then directly obtain the state properties at the mixing nodal. These are instantiated in analyzing districted thermal energy networks. The case study on district heating networks shows that the unilateral continuity problem at the mixing node for coupling network flows can be well solved. The number of equations of the presented DNPM is 7 less than the traditional BFM, and 3 iterations less than BFM, which show the ability of effective formulation and solution. The present work gives researchers an efficient method of formulating, analyzing, and optimizing energy networks.

Network flow methods for the minimum covariate imbalance problem

In an observational study, one is given disjoint samples of treatment units and control (untreated) units, and the goal is to compare outcomes between the two samples in order to estimate a treatment effect. A complication is that the treatment and control units often differ on important pre-treatment attributes, and these differences, referred to as covariate imbalance, can bias the estimate. One method to correct for covariate imbalance is to select a subset of the control sample that has minimum imbalance with respect to the treatment sample, and then use this control subset for estimating the treatment effect. While this optimization problem is NP-hard in general, certain special cases can be solved efficiently. Specifically, the variant of this optimization problem with one covariate is easy to solve, the variant with three or more covariates is NP-hard, and the variant with two covariates is solvable in polynomial time. We present several network flow formulations for the problem of minimizing imbalance on two nominal covariates. First, we present a minimum cost network flow formulation for solving the problem with the constraint that the control subset must have the same size as the treatment sample. We then derive an improved maximum flow formulation. For alternate size restrictions on the control subset, we use a proportional imbalance objective which leads to non-integral supplies and demands in the preceding network flow formulations. We then derive an alternate minimum cost network flow formulation that ensures integrality and solves the proportional imbalance problem in polynomial time.

DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks

Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains. DNF achieves more accurate driver-gene identification than other state-of-the-art methods when applied to four human datasets from The Cancer Genome Atlas and three single-cell RNA-seq datasets of murine neural and hematopoietic differentiation. Furthermore, we predict key regulators of crosstalk between separate networks underlying both neuronal differentiation and the progression of neurodegenerative disease, among which App is predicted as a driver gene of neural stem cell differentiation. Our method is a new approach for quantifying the essentiality of genes across networks of different biological states.

A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology

A monotone fuzzy rule relabeling (MFRR) algorithm has been introduced previously for tackling the issue of a non-monotone fuzzy rule base in the Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS). In this paper, we further propose a new three-stage framework to develop a computationally efficient MFRR algorithm. The first stage determines the combinations of fuzzy rules to be relabeled by exploiting the prior information derived from a given non-monotone fuzzy rule base. This prior information includes the minimum number of fuzzy rules to be relabeled (denoted as $k$ ), as well as the states of fuzzy rules that must be, must not be, or may be relabeled. The second stage relabels the consequent parts of multiple sets of $k$ noisy fuzzy rules obtained from the first stage, such that a monotone fuzzy rule base is produced. The third stage selects the most suitable relabeled fuzzy rule base among the potential monotone fuzzy rule bases obtained from the second stage, either objectively or subjectively. We provide insights into MFRR and discuss its practical implementation. In addition, a network flow method is fused with the proposed MFRR framework, resulting in an efficient computation scheme. The MFRR framework is applied to Failure Mode and Effect Analysis (FMEA) problems related to a sewage treatment plant and a public hospital. It is also evaluated with real FMEA information from a semiconductor plant. The results are analyzed and discussed, which positively demonstrate the effectiveness of the proposed MFRR framework in formulating a monotone TSK-FIS model for undertaking FMEA problems.

Coded multicasting in cache-enabled vehicular ad hoc network

Caching popular content at a wireless network edge has attracted significant attention since it can deal with massive content delivery in wireless networks. Furthermore, coded multicasting can impressively reduce network traffic by eliminating duplicate data transmission. In this paper, we study to minimize data traffic and redundant problem by jointly considering coded multicasting and edge caching in vehicular ad hoc networks (VANETs). For a single RSU scenario, a maximum degree priority algorithm is proposed to partition users into multiple multicast groups. And then, we formulate the problem of minimum data traffic and minimum redundant rate data for each multicast group as a multi-objective optimization problem, which is a multi-objective set coverage problem. Subsequently, we propose an efficient heuristic algorithm with a subfile code technique and coded multicasting in each multicast group to solve the problem. For a dense network with coverage of multiple RSUs overlapped with each other, the network flow method is used to reassign users to a specific RSU, and then an effective algorithm is proposed to address the general problem. The simulation results show that our proposed algorithm can improve the bandwidth usage and reduce the redundant rate effectively.

Multi-Objective Emergency Material Vehicle Dispatching and Routing under Dynamic Constraints in an Earthquake Disaster Environment

Emergency material vehicle dispatching and routing (EMVDR) is an important task in emergency relief after large-scale earthquake disasters. However, EMVDR is subject to dynamic disaster environment, with uncertainty surrounding elements such as the transportation network and relief materials. Accurate and dynamic emergency material dispatching and routing is difficult. This paper proposes an effective and efficient multi-objective multi-dynamic-constraint emergency material vehicle dispatching and routing model. Considering travel time, road capacity, and material supply and demand, the proposed EMVDR model is to deliver emergency materials from multiple emergency material depositories to multiple disaster points while satisfying the objectives of maximizing transport efficiency and minimizing the difference of material urgency degrees among multiple disaster points at any one time. Furthermore, a continuous-time dynamic network flow method is developed to solve this complicated model. The collected data from Ludian earthquake were used to conduct our experiments in the post-quake and the results demonstrate that: (1) the EMVDR model adapts to the dynamic disaster environment very well; (2) considering the difference of material urgency degree, the material loss ratio is −10.7%, but the variance of urgency degree decreases from 2.39 to 0.37; (3) the EMVDR model shows good performance in time and space, which allows for decisions to be made nearly in real time. This paper can provide spatial decision-making support for emergency material relief in large-scale earthquake disasters.

Reliability evaluation of voltage‐source converter‐based multi‐terminal direct current integrated offshore wind plants

A novel analytical approach to reliability evaluation of voltage-source converter-based multi-terminal direct current (VSC-MTDC) integrated offshore wind plants is presented in this study. To treat the multi-terminal grid precisely and efficiently, the approach combines the state evaluation method and network flow method. A DC line equivalent component is also proposed to consider the operation modes, failure clean measures as well as interactions between different DC lines of VSC-MTDC. Employing this approach, the authors can obtain both frequency and probability indexes of reliability. The usefulness and effectiveness of the proposed method are verified by case studies. The comparison between three popular schemes is also carried out.

Optimal Control of the Flow of Risk on Networks

Abstract A new network flow method is proposed in this work for optimal distribution of risk between the different sections (arcs) of the network. It is supposed at that that the maximum admissible risks on the arcs are known in advance. This provides a possibility these values to be considered as arc capacities and the risk itself as a network flow. Such an approach lets the maximal admissible risk to be determined on the network on the base of the well-known mincut-maxflow theorem as well as the minimal cut. Three different numerical examples are given through which the possibility of the method being proposed is confirmed in the real practices.

Robust allocation of weighted dependency links in cyber–physical networks

Interdependent network models are often used to show how one network has an effect on another network through dependencies. In this paper, we propose a novel interdependent network model which consists of two individual networks with unequal numbers of nodes and one-to-multiple weighted dependency links between the two networks. Based on realistic assumptions, this model differs from previous works that considered equal numbers of nodes in the two networks and identical dependency links. We formulate an optimization problem to allocate dependency links using least resources. This novel model enhances the practicability of traditional cyber–physical system structures, but it makes the dependency link deployment problem more complex and the optimization problem cannot be solved in large networks. To overcome this problem, we propose a new algorithm based on a revised network flow method. Extensive simulations on random networks and real networks show that our deployment method produces topologies that are more robust than the ones obtained by other deployment techniques. Results indicate that our algorithm is efficient and cost-effective in designing robust interdependent networks, and our deployment method is suitable for networks of any size.

Coupled Self-Adaptive Multiobjective Differential Evolution and Network Flow Algorithm Approach for Optimal Reservoir Operation

AbstractThis paper presents a coupled self-adaptive multiobjective differential evolution and network flow algorithm for the optimal operation of complex multipurpose reservoir systems. The developed algorithm (i.e., self-adaptive multiobjective differential evolution) is compared to nondominated sorting genetic algorithm II using a set of common test problems and a real-world case study. An out-of-kilter method for minimal-cost flow problems is used to optimize the water resource system from self-adaptive multiobjective differential evolution inputs driven by the evolutionary process. Self-adaptive multiobjective differential evolution is then used to evaluate objective functions based on the outputs from out-of-kilter algorithm and the process continues until the stop criterion is met. The advantages of the proposed approach include (1) flexible evolutionary algorithms for solving highly complex objective function, and (2) efficient network flow method for dealing with large and highly constrained probl...

A new method of enhancing reliability for transmission expansion planning

The reliability plays a significant role in power systems and it is an important objective or constraint in transmission expansion planning. Firstly, a DC optimization model was proposed to calculate the maximum arrival power at each load point. Compared to the network flow method, DC model is closer to the actual power flow and it is able to obtain more realistic reliability assessment results. Furthermore, a novel sensitivity index (SI) was also proposed to choose the most effective line so as to enhance the nodal and/or system reliability. The Monte Carlo simulation is used to simulate the system components state. This improved reliability evaluation method and SI can be used for transmission expansion planning or maintenance scheduling. Tests are performed using 6-bus system derived from the Garver’s system and the IEEE 10-machine 39-bus system. The results show the effectiveness of the method.

Integrated scheduling of ready-mixed concrete production and delivery

Abstract This study presents an approach for improving the operations of production and delivery in ready-mixed concrete (RMC) plants. A network flow method is applied to formulate the integrated scheduling problem of ready-mixed concrete production and delivery with trucks and pumps, where the demands of construction sites are in certain time windows. A method is developed that applies a genetic algorithm in which the chromosome consists of three sequences (construction sites, delivery order and vehicle IDs); operators work on the sequences of construction sites. The approach is evaluated by simulation of real cases. Comparison with combinations of other priority rules for scheduling production and vehicles demonstrates the effectiveness of the genetic algorithm. Sensitivity analysis reveals the effects of the fleet size of an available vehicle, the cost rates and the time windows of construction sites. The model and algorithm may be helpful for practical integrated operations for operation management at RMC plants.

Complexity Analysis of New Task Allocation Problem Using Network Flow Method on Multicore Clusters

The task allocation problem (TAP) generally aims to minimize total execution cost and internode communication cost in traditional parallel computing systems. New TAP (NTAP) considering additive intranode communication cost in emerging multicore cluster systems is investigated in this paper. We analyze the complexity of NTAP with network flow method and conclude that the intranode communication cost is a key to the complexity of NTAP, and prove that (1) the NTAP can be cast as a generalized linear network minimum cost flow problem and can be solved inO(m2n4)time if the intranode communication cost equals the internode communication cost, and (2) the NTAP can be cast as a generalized convex cost network minimum cost flow problem and can be solved in polynomial time if the intranode communication cost is more than the internode communication cost. More in particular, the uniform cost NTAP can be cast as a convex cost flow problem and can be solved inO(m2n2log(m+n))time. Furthermore, solutions to the NTAP are also discussed. Our work extends currently known theoretical results and the theorems and conclusions presented in this paper can provide theoretical basis for task allocating strategies on multicore clusters.

Underground stope optimization with network flow method

A new algorithm to optimize stope design for the sublevel stoping mining method is described. The model is based on a cylindrical coordinate defined around the initial vertical raise. Geotechnical constraints on hanging wall and footwall slopes are translated as precedence relations between blocks in the cylindrical coordinate system. Two control parameters with clear engineering meaning are defined to further constrain the solution: (a) the maximum distance of a block from the raise and (b) the horizontal width required to bring the farthest block to the raise. The graph obtained is completed by the addition of a source and a sink node allowing to transform the optimization program to a problem of maximum flow over the graph. The (conditional) optimal stope corresponding to the current raise location and height is obtained. The best location and height for the raise are determined by global optimization. The performance of the algorithm is evaluated with three simple synthetic deposits and one real deposit. Comparison is made with the floating stope technique. The results show that the algorithm effectively meets the geotechnical constraints and control parameters, and produce realistic optimal stope for engineering use.