Maximum Flow Algorithm Research Articles

MAXIMIZING LIFETIME OF HETEROGENEOUS WIRELESS TURNABLE CAMERA SENSOR NETWORKS ENSURING STRONG BARRIER COVERAGE

Barrier coverage in wireless camera sensor networks (WCSNs) has drawn the attention of research community since it promises an extremely large potential in applications involve movement detection and surveillance. As the battery resources are limited, improving the efficiency of energy is one of the key drivers for prolonging the lifetime of barrier coverage. However, most prior studies on this problem only worked on the networks with homogeneous sensors as well as omni-directional sensing coverage that have not been taken into account in WCSNs research. This paper thus investigates the problem of maximizing the network lifetime to ensure strong barrier coverage for heterogeneous case (MLBC-HWCSN). We formulate the problem, and then propose a Modify Maximum Flow Algorithm (MMFA) consisting of three stages: constructing the flow-network, finding the maximum flow and refining the solution to solve this problem. Experimental results on extensive instances show that the proposed methodology is suitable for the studied problem and more efficient than existing algorithms.

Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand

A car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. The data preprocessing is presented in order to automatically remove fake demands caused by search failures and application errors. Then, the real demand is forecasted using a deep learning approach, Bidirectional Gated Recurrent Unit. Finally, the Minimum Cost Maximum Flow algorithm is deployed to maximize forecasted demands, while minimizing the amount of relocations. Furthermore, the system is deployed in the real use case, entitled “CU Toyota Ha:mo,” which is a car-sharing system in Chulalongkorn University. It is based on a web application along with rule-based notification via Line. The experiment was conducted based on the real vehicle usage data in 2019. By comparing in real environment in November of 2019, the results show that our model even outperforms the manual relocation by experienced staff. It achieved a 3% opportunity loss reduction and 3% less relocation trips, reducing human effort by 17 man-hours/week.

Ultimate Pit Limit Optimization using Boykov-Kolmogorov Maximum Flow Algorithm

The ultimate pit limit optimization (UPLO) serves as an important step in the mine planning process. Various approaches of maximum flow algorithms such as pseudo-flow and push-relabel have been used for pit optimization, and have given good results. The Boykov-Kolmogorov (BK) maximum flow algorithm has been used in solving the computer vision problems and has given great practical results but it has never been applied in UPLO. In this work, we formulate and use the BK maximum flow algorithm and the push-relabel maximum flow algorithm in MATLAB Boost Graph Library within the MATLAB software in order to perform UPLO in two case studies. Comparing both case studies for the BK maximum flow algorithm and push-relabel maximum flow algorithm gives the same maximum pit values but the BK maximum flow algorithm reduces the time consumed by 12% in the first case and 16% in the second case. This successful application of the BK maximum flow algorithm shows that it can also be used in UPLO.

Optimization of an Offshore Oilfield Multi-Platform Interconnected Power System Structure

In recent years, with regional integrated seabed resource exploitation continuing to expand, offshore oilfield power systems have gradually become networked, forming an offshore oilfield multi-platform interconnected power system. In this paper, a two-level optimization model for offshore oilfield multi-platform interconnected power system structure is developed. The lower-level model uses a minimum cut-load cost model that considers load priority to calculate the power outage losses of all N-1 fault conditions. The reliability assessment method helps to achieve a balance between economy and reliability. Additionally, an optimization model simplification method based on graph theory is proposed, and it uses the minimum cost maximum flow (MCMF) algorithm to optimize the power flow of the grid and narrows the problem's solution space. At the end of modeling, a parallel global taboo search algorithm is used to reduce the computational time and improve the probability of finding an optimal solution. Investigations of a real offshore oilfield multi-platform power system verify the accuracy of the model, the simplification method and the parallel global taboo search algorithm.

Wave function analysis with a maximum flow algorithm

An efficient algorithm for computing the maximum-flow path in a network is applied to the identification of the dominant configuration state functions (CSFs) in a graphically contracted function (GCF), configuration interaction, wave function. The flow network is a space of spin-adapted CSFs represented by a Shavitt graph, wherein the nodes correspond to orbital occupations and spin quantum numbers. The graph nodes are connected by arcs, and an arc density is defined as sums of the associated squared CSF coefficients. A max–min approach determines an upper bound to the maximum possible incoming flow for each graph node. A backtracking step generates a candidate walk and is followed by a limited search of alternative branching paths for the dominant CSF. The arc density contributions are removed from the graph, and the algorithm is reapplied to the updated graph. This list of generated walks can be partitioned in order to guarantee that the dominant CSFs have been identified. All of the steps in this algorithm are computationally efficient and do not depend on the potentially large dimension of the underlying linear CSF expansion space. An analysis of low-lying valence states of C2 illustrates the method.

A fast maximum flow algorithm

AbstractIn 2013, Orlin proved that the max flow problem could be solved in O(nm) time. His algorithm ran in O(nm + m1.94) time, which was the fastest for graphs with fewer than n1.06 arcs. If the graph was not sufficiently sparse, the fastest running time was an algorithm due to King, Rao, and Tarjan. We describe a new variant of the excess scaling algorithm for the max flow problem whose running time strictly dominates the running time of the algorithm by King et al. For graphs in which m = O(nlog n), the running time of our algorithm dominates that of King et al. by a factor of O(loglog n). Moreover, our algorithm achieves this improved performance without reliance on dynamic trees.

Vulnerability analysis of a natural gas pipeline network based on network flow

A vulnerability analysis of the transportation capacity of a natural-gas pipeline network based on network flow was proposed. This analysis focused on the inherent properties instead of the environment and probabilities, which are usually considered in conventional risk assessments. The topology of the pipeline network was established in the form of a graph, and maximum-flow algorithm was applied to identify crucial pipelines that affect the transportation capacity of the natural-gas pipeline network. From the perspective of network efficiency and flow-based performance, three vulnerability indicators of the transportation capacity of the network were established. These indicators can better identify the critical pipelines, especially those with low failure probability but potential gas-transportation vulnerability. We investigated the development of a pipeline network and that of the natural-gas market at different stages to plan an optimal pipeline network route and determine the transmission capacity, in order to improve the reliability and economy of the entire pipeline network.

Traffic Model of LTE Using Maximum Flow Algorithm with Binary Search Technique

Inrecent time a rapid increase in the number of smart devices and user applications have generated an intensity volume of data traffic from/to a cellular network. So the Long Term Evaluation(LTE)network is facing some issuesdifficulties ofthebase station and infrastructure in terms of upgrade and configuration becausethere is no concept of BSC (Base Station Controller) of 2G and RNC (Radio Network Controller) of 3G to control several BTS/NB. Only 4G (LTE) all the eNBs areinterconnected for traffic flow from UE (user equipment) to core switch. Determination of capacity of a linkof such a network is a challenging job since each node offers its own traffic andat the same time conveys traffic of other this http URL this paper, we apply maximum flow algorithm including the binary search techniqueto solve the traffic flow of radio networkandinterconnected eNBs of the LTE network. The throughput of the LTE network shown graphically under the QPSK and 16-QAM

Performance Evaluation of LTE Network using Maximum Flow Algorithm

In this paper, we propose a new traffic flow model of the Long Term Evaluation (LTE) network for the Evolved Universal Terrestrial Radio Access Network (E-UTRAN). Here only one Evolve Node B (eNB) nearest to the Mobility Management Entity (MME) and Serving Gateway (S-GW) will use the S1 link to bridge the E-UTRAN and Evolved Packet Core (EPC). All the eNBs of a tracking area will be connected to each other by the X2 link. Determination of capacity of a links of such a network is a challenging job since each node offers its own traffic and at the same time conveys traffic of other nodes. In this paper, we apply maximum flow algorithm including superposition theorem to solve the traffic flow of radio network. Using the total flow per subcarrier, a new traffic model is also developed in the paper. The relation among the traffic parameters: ‘blocking probability’, ‘offered traffic’, ‘instantaneous capacity’, ‘average holding time’, and ‘number of users’ are shown graphically under both QPSK and 16-QAM. The concept of the network will be helpful to improve the SINR of the received signal ofeNBslocated long distance relative to MME/S-GW.

Evaluation of Water Resources Utilization Effectiveness Based on Eco-economics Theory

Chen, Y.; Li, Z.; Li, P.; Gao, H.; Jin, T.; Du, P., and Zhang, Y., 2020. Evaluation of water resources utilization effectiveness based on eco-economics theory. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 148-150. Coconut Creek (Florida), ISSN 0749-0208.Facing the increasingly severe water resources situation, how to manage water resources reasonably and effectively, and improve the utilization efficiency and efficiency of water resources is an inevitable requirement for the overall development of China's economic and social water resources ecological environment system. Based on this, the effect of water resources utilization is evaluated based on the theory of ecological economics. The SVM correlation model algorithm is proposed. The dynamic analysis and simulation evaluation are carried out through SD simulation technology, and the total system flow diagram of regional water resources utilization evaluation is established. Based on the maximum flow algorithm, the incentive degree of water resources ecological compensation mechanism is studied. InVEST model and household survey are used to calculate the new water conservation and land use conversion costs. After considering the risk of water resources ecosystem loss, the welfare cost method is adopted. On the county scale, the potential objects of ecological compensation for water resources in the Weihe River Basin are identified. The results show that there are obvious spatial differences in water resources ecological compensation efficiency coefficients in Gansu section of Weihe River Basin. Weiyuan County with the highest runoff is 1.26, Gangu County with the lowest runoff is 0.46. The efficiency of the project can be increased by 40.54% after spatial screening of regional compensation objects. Through the evaluation of water resources utilization and water resources policy simulation, it provides a reference for the coordinated development of water resources and social economy in China.

Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

Simultaneous stochastic optimization of production sequence and dynamic cut-off grades in an open pit mining operation

An open pit mining operation is a complex system that constitutes several components or processes. An optimal production sequence that defines timing of extraction and a dynamic cut-off grade policy that defines the supply of materials from sources to destinations within the system are crucial to the success of an operation. In current practice, separate sequencing and cut-off grade models achieve these important milestones as part of strategic planning. This paper presents a mathematical model that derives the optimal extraction sequence and cut-off grade policy simultaneously considering grade uncertainty and stockpiling. A framework of genetic, maximum flow and cut-off grade algorithms solves this complex non-linear problem. An application of the method at realistic copper and gold mining operations reveals the value (up to 29% increase in discounted value) of stockpiling as well as risk quantification under uncertainty.

Research on abundance adaptive frequency control strategy of household temperature-control load

Based on the method of space state, state division of household temperature-control load group is proposed. State sequence method is used to analyze the influence of load group abundance on aggregated power. Based on the minimum cost and maximum flow algorithm, the temperature-control load group’s abundance recovery strategy is proposed, which could achieves the goal of fast and smooth recovery load group abundance. The simulation of household temperature-control load respond to the power system primary frequency adjustment is carried out under the circumstances of good abundance, excess abundance and lack of abundance. The simulation results show that the effect of temperature control load on the power grid primary frequency adjustment is fine. and the effectiveness of the proposed frequency adjustment control strategy is verified.

Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization

In this paper, we discuss a large-scale fleet management problem in a multi-objective setting. We aim to seek a receding horizon taxi dispatch solution that serves as many ride requests as possible while minimizing the cost of relocating vehicles. To obtain the desired solution, we first convert the multi-objective taxi dispatch problem into a network flow problem, which can be solved using the classical minimum cost maximum flow (MCMF) algorithm. We show that a solution obtained using the MCMF algorithm is integer-valued; thus, it does not require any additional rounding procedure that may introduce undesirable numerical errors. Furthermore, we prove the time-greedy property of the proposed solution, which justifies the use of receding horizon optimization. For computational efficiency, we propose a linear programming method to obtain an optimal solution in near real time. The results of our simulation studies using real-world data for the metropolitan area of Seoul, South Korea indicate that the performance of the proposed predictive method is almost as good as that of the oracle that foresees the future.

Solving the Max-Flow Problem on a Quantum Annealing Computer

This article addresses the question of implementing a maximum flow algorithm on directed graphs in a formulation suitable for a quantum annealing computer. Three distinct approaches are presented. In all three cases, the flow problem is formulated as a quadratic unconstrained binary optimization (QUBO) problem amenable to quantum annealing. The first implementation augments a graph with integral edge capacities into a multigraph with unit-capacity edges and encodes the fundamental objective and constraints of the maximum flow problem using a number of qubits equal to the total capacity of the graph $\sum _i{c_i}$ . The second implementation, which encodes flows through edges using a binary representation, reduces the required number of qubits to $\mathcal {O}(|E| \log C_{\max })$ , where $|E|$ and $C_{\max }$ denote the number of edges and maximum edge capacity of the graph, respectively. The third implementation adapts the dual minimum cut formulation and encodes the problem instance using $|V|$ qubits, where $|V|$ is the number of vertices in the graph. Scaling factors for penalty terms and coupling matrix construction times are made explicit in this article.

A Service Function Chain Deployment Method Based on Network Flow Theory for Load Balance in Operator Networks

Network function virtualization technology makes the deployment and management of network service more flexible and elastic by decoupling network function from dedicated hardware. The service requests of network function virtualization are usually deployed in the form of a service function chain. In order to solve the problems of load imbalance, unreasonable utilization of substrate resources, and the high delay of the service function chain deployment in operator networks, a service function chain deployment method based on the network flow theory is proposed in this paper. First, on the basis of perceiving the substrate network resources and topology with a software-defined network controller in real time, a candidate node set is determined according to the resource constraints and the locations of ingress/egress switch nodes that service flow flows in/out. Second, the candidate node set, the ingress/egress switch nodes and the connection between them are used to form a directed network, and the service function chain deployment problem is transformed into an optimal path selection problem. Then, a node disassembling method is used to transform the directed network into a capacity-flow-cost network. Finally, a minimum-cost maximum-flow algorithm is used to find the optimal deployment path and complete the service function chain deployment. Experiments show that the method proposed in this paper can guarantee the load balance of operator networks, reduce the average transmission delay of service flow, and make the utilization of substrate resources more reasonable.

Target control and expandable target control of complex networks

Target control of complex networks, which aims to control only a target subset of network nodes instead of the entire network, is an outstanding challenge faced in various real world applications. Recently one fundamental issue regarding how to allocate a minimum number of control sources to guarantee the target controllability of a given target node set S was solved. This issue is shown to be essentially a path cover problem and it can be converted to a maximum network flow problem. In this work, we address another fundamental issue which is to further find the maximum (minimum) controllable node set to cover S using directed paths and circles based on the allocated minimum number of control sources. We show that such an issue can be solved by applying the maximum (minimum) cost maximum flow algorithm after introducing a cost for each edge in the flow network. Based on the obtained maximum (minimum) target controllable node set, we further propose a new index termed “expandable target controllability” to characterize complex networks that are expanding all the time. It is shown that “expandable target controllability” is an intrinsic property of various networks. We anticipate that this work would serve wide applications in target control and expandable target control of real-life networks.

Application of the Ford-Falkerson algorithm to detect excess information

The object of the article is the network maximum flow algorithm, mainly the Ford-Fulkerson algorithm. The algorithm began to be developed by two scientists: Ford and Fulkerson. This algorithm was proposed in order to find the maximum flow in the network. They began to be actively studied by scientists from the middle of the last century. The first report of "Maximum Network Flow" dates back to 1954. The authors of the report, Ford and Fulkerson had proved the theorem on the maximum flow and the minimum cross section for non-oriented graphs: the value of the maximum flow in the network is equal to the minimum throughput capacity of the section. The interest in the solution of these tasks was primarily due to practical needs, for that time construction of routes for the transportation of raw materials was not optimal and transported more raw materials than can transfer the connection between points. Such problems often arise when constructing connections that transport oil through pipes or transport coal through special excavators. The subject of the article is the problem of finding the maximum flow in the network. In graph theory, the transport network is an indicative graph in which each arc has no negative throughput and flow. Two peaks are distinguished: source and drain - such that any other vertex in the network lies on the way from source to drain. The article consists of two sections. In the first section we consider the mathematical formulation of the problem and concrete examples of problems. The second section examines the classic Ford-Fulkerson algorithm, the modified Ford-Fulkerson algorithm to find excess information on the network, and the work of a modified algorithm on a specific example from the first section. The considered problems are relevant both from a theoretical and a practical point of view.

Fairness and efficiency in pedestrian emergency evacuation: Modeling and simulation

The goal of this paper is to study different algorithms for planning a large scale pedestrian evacuation process. A pedestrian (walking) mode of evacuation is an efficient mode of transportation that is often overlooked in the literature. Fairness in the evacuation process is another aspect that is rarely studied. In this paper, we study efficiency and fairness of pedestrian mode of emergency evacuation. We model the evacuation problem as a dynamic network flow problem, which captures the time-dependency of the network attributes such as edge and vertex capacities and edge travel times. To evaluate the efficiency of the algorithms, we consider the clearance time of the network. To investigate fairness, we consider the Gini coefficient and Lorenz curve that are popular indexes in economy to measure wealth distribution in a society. We compute the Gini coefficient and Lorenz curve to measure the fairness in travel time distribution of the evacuees in the emergency evacuation process. We develop a new algorithm called PEPA (Pedestrian Evacuation Planning Algorithm) to find the minimum clearance time of the network. PEPA is compared to two commonly studied graph algorithms in the literature, shortest path and maximum flow algorithms. We compared the algorithms in terms of computation time of the algorithm, clearance time of the network and Gini coefficient. Our results show that PEPA beats both shortest path and maximum flow in terms of efficiency and fairness, although it is slightly slower in terms of computation time. To implement PEPA, shortest path and maximum flow algorithms, we develop a new software named PEMSS (Pedestrian Evacuation Modeler, Solver, and Simulator). PEMSS is written in the Java programming language and is capable of reading and showing maps in open street map XML format.

Open pit mine production schedule optimization using a hybrid of maximum-flow and genetic algorithms

Production scheduling is a critical activity for the long-term production planning of open pit mining operations. It deals with the effective management of resources and maximizes cash flows to generate higher profits over the life of a mine. Production scheduling problems determine that blocks be mined and processed over a number of periods subjected to mining and processing constraints, which makes the problem more complex. The complexity is further increased due to the uncertainty in the input parameters. In this study, the maximum flow algorithm with a genetic algorithm is used to generate the long-term production schedule. The graph structure for maximum flow is created for multiple periods under uncertainty, and the flow in the arcs is controlled by a genetic algorithm to develop a production schedule. Numerical results for realistic instances are provided to indicate the efficiency of the solutions.