Minimum Cost Maximum Flow Research Articles

An adaptive routing strategy in P2P-based Edge Cloud

P2P-based Edge Cloud (PEC) is widely used in Internet of Things (IoT). Inevitably, the sensor data routing technology has a significant impact on the performance of PEC. Due to its prevalence and complexity, the existing routing technologies in PEC need to be optimized. Specifically, key factors such as overall network traffic, user access latency, and resource utilization of edge nodes should be considered to adapt to the dynamic requirements of user request services and network topology. In order to address the challenges produced by these factors, an adaptive routing in P2P-based Edge Cloud is proposed, which is named ARPEC. In our approach, a target edge node selection scheme based on message activity and network topology is proposed, aiming to minimize the load on edge node and user access latency. Furthermore, to minimize system overhead, sensor data routing is mapped to minimum cost maximum flow (MCMF) graph. On this basis, a target edge node selection algorithm based on a grey linear regression combination prediction model is designed, and an incremental MCMF algorithm based on belief propagation (BP) is proposed. The evaluation results show that our approach can effectively improve PEC transmission performance and user experience.

최소비용 최대유량 알고리즘에 기반한 구급차의 환자이송 병원 배정 기법의 설계

This paper presents a design and analyzes the performance of a hospital assignment and reassignment scheme for emergency rescue services based on minimum cost maximum flow algorithm. It consists of flow graph building, link capacity updating, and allocation discovering steps. The efficiency of the algorithm makes it possible to reallocate hospitals even in case of dynamic changes in the number of patients or hospitals. The performance measurement result obtained from a prototype implementation shows that the proposed scheme can reduce the transport time requirement miss by up to 24%.

Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks

Benefiting from advanced satellite payload technologies, edge computing servers can be deployed on satellites to achieve orbital computing and reduce the mission processing delay. However, geostationary Earth orbit (GEO) satellites are hindered by long-distance communication, whereas low Earth orbit (LEO) satellites are restricted by time windows. Relying solely on GEO or LEO satellites cannot meet the strict quality of service (QoS) requirements of on-board missions while conserving energy consumption. In this paper, we propose a computation offloading strategy for GEO/LEO hybrid satellite networks that minimizes total energy consumption while guaranteeing the QoS requirements of multiple missions. We first innovatively transform the on-board partial computation offloading problem, which is a mixed-integer nonlinear programming (MINLP) problem, into a minimum cost maximum flow (MCMF) problem. Then, the successive shortest path-based computation offloading (SSPCO) method is introduced to obtain the offloading decision in polynomial time. To evaluate the effectiveness and performance of SSPCO, we conduct a series of numerical experiments and compare SSPCO with other offloading methods. The experimental results demonstrate that our proposed SSPCO outperforms the reference methods in terms of total energy consumption, QoS violation degree, and algorithm running time.

Identifying the critical factors of transmission efficiency loss in China’s natural gas network

China’s market-oriented reform supports the sustainable development of energy mix and the low-carbon target, and natural gas has bridged the transition from traditional fossil energy to clean and renewable energies. The third-party access policy, launched recently by China’s natural gas market, drives the decouple between gas trade and transport. The decouple might lead to the transmission resources of physical network not optimally used, which is caused by the contractual arrangement between entry and exit capacities in commercial network. Aiming at this issue, we established a mathematical programming with equilibrium constraints (MPEC) to integrate the allocations of commercial capacity and physical flows, based on a minimum cost maximum flow problem (MCMF) abstracted from China’s existing gas network. The MPEC model was then used to strategically evaluate the transmission efficiency, and identify the critical factors of its loss. Our results show that there is transmission efficiency loss of China’s gas network from the shortage of geospatial gas supply and the invisible segmentation of gas network due to interdicted cost of pipeline, bottleneck of pipeline capacity and economic radius of gas supply chains to transport gas. Therefore, the critical factor of the loss to be identified will be helpful for strategically reducing the cost of decoupling gas trade and transport.

基于激光雷达的农业车辆安全预警系统研究

Intelligent agricultural vehicles have been widely used in the process of farming and harvesting in the field, which has brought great convenience to agricultural production. However, there are also safety issues such as accidental collision of agricultural vehicles or other agricultural machinery during operation. The use of sensing technology for the timely and accurate detection and pre-warning of obstacles during the operation of agricultural machinery is critically important for ensuring safety. In this paper, a two-dimensional lidar is used to detect obstacles in front of tractors with the Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm and the Minimum Cost Maximum Flow algorithm(MCMF). A method to judge whether the obstacle is static or dynamic and a classification model of different security warning levels for obstacles in different states is proposed. Actual vehicle tests were conducted, with static obstacles tested repeatedly, and dynamic obstacles tested at different directions and speeds. The results showed that the overall average warning accuracy rate is 89.95%. Prediction results were robust for obstacles in different states, indicating that this system is able to ensure the safety of agricultural vehicles during their operation and promoted the development of agricultural mechanization.

Discretization-Free Particle-Based Taxi Dispatch Methods With Network Flow Decomposition

The increasing popularity of on-demand ride-hailing services has introduced research problems, including those related to fleet management. Many existing fleet management approaches discretize a spatiotemporal distribution to reduce the problem complexity. Although spatial discretization greatly simplifies the problems, this approximation results in a suboptimal solution. To address this limitation, we present a novel spatial discretization-free, particle-based formulation of fleet management. Our model requires only temporal discretization. The nodes in our spatiotemporal network model represent the positions of the fleet and ride requests over the time horizon. Under this representation, a taxi dispatch problem can be solved by the minimum-cost maximum-flow (MCMF) algorithm. We show some structural properties that disclose a type of monotonicity by applying the MCMF algorithm to our problem. The obtained dispatch solution maximizes the number of served requests from these structural properties at least at the most current time step, regardless of the demand prediction quality. Moreover, these properties enable the decomposition of the MCMF algorithm without sacrificing optimality, and the decomposed multistage maximum-flow algorithm is significantly faster than the MCMF algorithm. Furthermore, we propose an efficient method that alleviates the dearth of input data for service providers who lack sufficient computing power to calculate substantial travel time information. We demonstrate the performance of our method by using data collected in New York City, USA. Our simulation studies indicate that a service provider can fulfill 74–85% of the ride requests within 2 minutes.

Consumption Coupons, Consumption Probability and Inventory Optimization: An Improved Minimum-Cost Maximum-Flow Approach

The issuance of consumption coupons during the epidemic period to stimulate the economy must take full account of the level of probabilistic consumption and inventory optimization. In this paper, an improved minimum-cost maximum-flow model is constructed to dynamically adjust the inventory capacity of node enterprises with the change of probabilistic consumption level, and three scenarios are simulated by numerical assumptions. The results show that: (1) The model can better solve the problem of consumption coupons, probabilistic consumption and inventory optimization; (2) Consumer welfare remains unchanged, the largest number of government consumption coupons is issued, and the number of enterprise inventories reaches the lowest; (3) Enterprise inventories are minimized with different decisions on consumer probability consumption, and the government’s issuance of consumption coupons and the satisfaction of consumer demand have reached a dynamic balance. Corresponding suggestions are put forward, hoping to better help the government to implement the consumption coupons policy to stimulate the economy.

Emergency response for tackling major accidental toxic gas releases: What should be done and when?

When there are toxic gas leaks, rapid emergency response planning is vital to protect public safety. In this study, an emergency response trade-off model to assist decision-makers in taking focused action for different personnel is developed. First, a modified Dijkstra algorithm and a minimum cost maximum flow algorithm are employed to determine the optimal evaluation routes, after which an as low as reasonably practical criterion is applied to evaluate the emergency response risk levels and identify the multiple emergency response windows of opportunity. Finally, a case study based on a real incident is given to illustrate the applicability of our method. It was found that an immediate evacuation of all members of the public in a target area would expose some of them to excessive risk. It was also discovered that there is a close and complex relationship between the emergency response risk and the shelter-in-place duration and the public emergency response. Another interesting finding is that the evacuation routes in the windows of opportunities differ significantly depending on the location, and the emergency response risks associated with using the same path to evacuate at different times. These interesting findings, which were based on the scientific assessment of emergency response risks, have a massive practical impact and could assist in more accurately formulating public protection strategies.

Online Optimization of Energy-Efficient User Association and Workload Offloading for Mobile Edge Computing

This paper presents a new stochastic optimization framework for user association and task offloading in mobile edge computing (MEC) networks with spatial and temporal variations of computing power, channel quality and connection capacity between different MEC servers. The new framework minimizes a quadratic penalty function which balances the energy consumption and fairness of the devices. Lyapunov optimization is first applied to eliminate the time coupling of the framework, leading to a mixed-integer program (MIP) of user association and offloading scheduling at every time slot. While solving the scheduling using linear programming, we convert the user association to a minimum cost maximum flow problem by interpreting edge servers and devices as two disjoint vertexes. We solve the minimum-cost maximum flow problem efficiently by using the Ford-Fulkerson algorithm. Corroborated by simulations, the proposed approach is asymptotically optimal and outperforms alternative approaches in terms of energy saving and fairness.

Minimum Cost of Capacity Expansion for Time-Limited Transportation Problem On-Demand

The minimum cost of capacity expansion for time-limited transportation problem on-demand (MCCETLTPD) is to find such a practicable capacity expansion transportation scheme satisfying the time-limited T along with all origins’ supply and all destinations’ demands as well as the expanding cost is minimum. Actually, MCCETLTPD is a balance transportation problem and a variant problem of minimum cost maximum flow problem. In this paper, by creating a mathematical model and constructing a network with lower and upper arc capacities, MCCETLTPD is transformed into searching feasible flow in the constructed network, and consequently, an algorithm MCCETLTPD-A is developed as MCCETLTPD’s solution method basing minimum cost maximum flow algorithm. Computational study validates that the MCCETLTPD-A algorithm is an efficient approach to solving the MCCETLTPD.

Two-timescale online learning of joint user association and resource scheduling in dynamic mobile edge computing

For the mobile edge computing network consisting of multiple base stations and resource-constrained user devices, network cost in terms of energy and delay will incur during task offoading from the user to the edge server. With the limitations imposed on transmission capacity, computing resource, and connection capacity, the perslot online learning algorithm is first proposed to minimize the time-averaged network cost. In particular, by leveraging the theories of stochastic gradient descent and minimum cost maximum flow, the user association is jointly optimized with resource scheduling in each time slot. The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment. Moreover, to alleviate the high network overhead incurred during user handover and task migration, a two-timescale optimization approach is proposed to avoid frequent changes in user association. With user association executed on a large time scale and the resource scheduling decided on the single time slot, the a symptotic optimality is preserved. Simulation results verify the effectiveness of the proposed online learning algorithms.

Internet of UAVs Based Remote Health Monitoring: An Online eHealth System

The recent COVID-19 pandemic has brought significant challenges to the traditional healthcare industry. A novel eHealth system is required to cope with infectious and chronic diseases. This article proposes an online eHealth system to monitor patients and provides edge computing services based on the Internet of Unmanned Aerial Vehicles (UAVs). In order to minimize the health monitoring latency and guarantee the resource utilization efficiency of UAVs, the Lyapunov optimization method is utilized to decompose the long-term optimization problem into a series of instantaneous optimization problems. Then the decomposed sub-problem is formulated as a minimum cost maximum flow problem by constructing a bipartite graph that maps medical analysis requests to target UAVs. Extensive experimental results demonstrate the effectiveness of our system. Finally, system analyses illustrate that the robustness and scalability of our eHealth system is favorable, and our system can provide agile edge computing services for dispersed patients.

Target Controllability in Multilayer Networks via Minimum-Cost Maximum-Flow Method.

In this article, to maximize the dimension of controllable subspace, we consider target controllability problem with maximum covered nodes set in multiplex networks. We call such an issue as maximum-cost target controllability problem. Likewise, minimum-cost target controllability problem is also introduced which is to find minimum covered node set and driver node set. To address these two issues, we first transform them into a minimum-cost maximum-flow problem based on graph theory. Then an algorithm named target minimum-cost maximum-flow (TMM) is proposed. It is shown that the proposed TMM ensures the target nodes in multiplex networks to be controlled with the minimum number of inputs as well as the maximum (minimum) number of covered nodes. Simulation results on Erdős-Rényi (ER-ER) networks, scale-free (SF-SF) networks, and real-life networks illustrate satisfactory performance of the TMM.

Shortest path reoptimization vs resolution from scratch: a computational comparison

The Shortest Path Problem (SPP) is among the most studied problems in Operations Research, for its theoretical aspects but also because it appears as sub-problem in many combinatorial optimization problems, e.g. Vehicle Routing and Maximum Flow-Minimum Cost problems. Given a sequence of SPPs, suppose that two subsequent instances solely differ by a slight change in the graph structure: that is the set of nodes, the set of arcs or both have changed; then, the goal of the reoptimization consists in solving the SPP of the sequence by reusing valuable information gathered in the solution of the one. We focused on the most general scenario, i.e. multiple changes for any subset of arcs, for which, only the description of a dual-primal approach has been proposed so far [S. Pallottino and M.G. Scutell‘a, A new algorithm for reoptimizing shortest paths when the arc costs change, Oper. Res. Lett. 31 (2003), pp. 149-160.]. We implemented this framework exploiting efficient data structures, i.e. the Multi Level Bucket. In addition, we compare the performance of our proposal with the well-known Dijkstra's algorithm, applied for solving each modified problem from scratch. In this way, we draw the line – in terms of cost, topology, and size – among the instances where the reoptimization approach is efficient from those that should be solved from scratch.

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.

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.

A Self-Adaptive Quantum Genetic Algorithm for Network Flow Vehicle Scheduling Problem

Bicycle sharing scheduling is a complex mathematical optimization problem, and it is challenging to design a general algorithm to solve it well due to the uncertainty of its influencing factors. This paper creatively establishes a new mathematical model to determine the appropriate number of vehicles to be placed at each placement point by calculating the traffic weights of the placement points and optimizes the hyperparameters in the algorithm by adaptive quantum genetic algorithm, and at the same time combines the network flow algorithm in graph theory to calculate the most suitable scheduling scheme for shared bicycles by establishing the minimum cost maximum flow network. Through experimental validation, the network flow-based algorithm proposed in this paper allows for a more convenient calculation of the daily bike-sharing scheduling scheme compared to previous algorithms. An adaptive quantum genetic algorithm optimizes the hyperparameters appearing in the algorithm. The experimental results show that the algorithm achieves good results as the transportation cost is only 1/15th of the GA algorithm and 1/9th of the QGA algorithm.

An Iterative Solution Technique to Minimize the Average Transportation Cost of Capacitated Transportation Problem with Bounds on Rim Conditions

The average transportation cost minimization of capacitated transportation problem with bounds on rim conditions (CTPBRC) is an important optimization problem due to the requirement of low unit cost consumption in production system. In the literature, there is only one approach to solving a special case of this problem, but it is not applicable to the general case. In this paper, this problem is reduced to a series of finding the minimum cost maximum flow in a network with lower and upper arc capacities, and two iterative algorithms are proposed as more generalized solution method for this problem as compared to the existing approach. Computational experiments on randomly generated instances validate that the two iterative algorithms are generally able to find the minimum average transportation cost solution to CTPBRC efficiently for the general case, in which one iterative algorithm has higher efficiency than the other for large size instances.

Reinforcement learning–enabled efficient data gathering in underground wireless sensor networks

Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil medium, while the channel quality of WUSNs is greatly impacted by the underground environment, such as soil moisture and composition. Due to the precipitation and harsh weather, the underground environments change frequently, which make wireless communication in WUSNs much complicated than that in terrestrial over-the-air wireless sensor networks. To achieve reliable and energy-efficient data gathering in dynamic WUSNs, this article proposes an optimal transmission policy, where path loss of sensory data transmission, energy constraint, and network load balancing are the factors to be considered. Specifically, we capture the effect of underground environments on wireless communications, and evaluate path probability, energy consumption, and load balancing factor with respect to reliability and efficiency of transmission paths. The transmission topology can be reduced to a multi-objective and multi-constrained optimization problem and solved through an improved maximum flow minimum cost algorithm. By using reinforcement learning, we derive an adaptive transmission policy for underground sensors to efficiently use their energy and avoid transmitting sensory data in unreliable paths under a dynamic environment. Through simulations and comparison upon publicly available real data, our technique achieves more reliable wireless communication with significant reduction of packet loss, and enables more energy-efficient data gathering than other techniques, especially when soil moisture varies frequently.

A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas

It is still a completely new and challenging task to register extensive, enormous and sparse satellite light detection and ranging (LiDAR) point clouds. Aimed at this problem, this study provides a ridgeline-based terrain co-registration method in preparation for satellite LiDAR point clouds in rough areas. This method has several merits: (1) only ridgelines are extracted as neighbor information for feature description and their intersections are extracted as keypoints, which can greatly reduce the number of points for subsequent processing, and extracted keypoints is of high repeatability and distinctiveness; (2) a new local-reference frame (LRF) construction method is designed by combining both three dimensional (3D) coordinate and normal vector covariance matrices, which effectively improves its direction consistency; (3) a minimum cost–maximum flow (MCMF) graph-matching strategy is adopted to maximize similarity sum in a sparse-matching graph. It can avoid the problem of “many-to-many” and “one to many” caused by traditional matching strategies; (4) a transformation matrix-based clustering is adopted with a least square (LS)-based registration, where mismatches are eliminated and correct pairs are fully participated in optimal parameters evaluation to improve its stability. Experiments on simulated satellite LiDAR point clouds show that this method can effectively remove mismatches and estimate optimal parameters with high accuracy, especially in rough areas.