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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Wenke Yuan Zixuan Yin Linxiao Xia Guizhou University Guiyang China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

The purpose of this paper is to explore the application of K-Means cluster analysis and linear programming model in agricultural production. Firstly, the linear programming model is used to formulate the planting strategy in the region, taking the actual situation in North China as an example. Secondly, on the basis of the optimization of the linear programming model, a preliminary crop planting strategy is given. A robust optimization model is introduced to construct the uncertainty set and incorporate it into the objective function to obtain the optimal planting strategy in the worst case. Finally, the objective function was improved by K-Means cluster analysis model and the model was refined by integrating the substitutability and complementarity among crops. The model aims to improve the economic efficiency of local farmers and provide a reference for agricultural planning in other regions.

关键词： Analytical models Adaptation models Solid modeling Uncertainty Accuracy Volume measurement Crops linear programming Resource management Optimization

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Data accumulation in WSNs using a mobile sink: A linear programming approach

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Measurement: Sensors 2023年 27卷

作者： Sulakshana, Guduri Kamatam, Govardhan Reddy Andhra Pradesh Kurnool India Department of Computer Science and Engineering G. Pulla Reddy Engineering College Andhra Pradesh Kurnool India

Sensor Nodes (SNs) in Wireless sensor networks (WSNs) continuously monitor the environment and accumulate data. But due to the limited memory and processing capabilities of the sensor nodes, the data must be transmitted to the central server for further analysis. To do this, routing via multi-hop channel is used which consumes a lot of particular SN's energy and also utilizes the resources, including the battery and memory of all the SN's involved in routing. This process drains the SN's energy rapidly and further leading to disconnection from the central server. To address this issue, mobile sink concept is introduced. The mobile sink collects the data from the SN's and hands it over to the central server which reduces the SN's involvement in data transmitting, but this process involves computationally complex tasks. In this context, we use linear programming to find the best visiting order to collect the information from a set of prioritized SN's, where other nodes transmit their data to them. We confirm that this process is computationally lightweight. In addition, this approach meat the performance criteria such as the lifespan of WSNs, the usage of buffer, throughput, and delay. We simulate the proposed model using Python, and they show improvement in efficiency when tested with different metrics of WSNs. © 2023

关键词： linear programming

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Exploring Enhanced Dominant Resource Fairness Using linear programming Calculated Weights

Exploring Enhanced Dominant Resource Fairness Using Linear P...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Bo Yan Angel Beltre Pankaj Saha Madhusudhan Govindaraju Binghamton University State University of New York (SUNY) Binghamton NY Sandia National Laboratories

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Maintaining resource fairness while achieving optimization for various performance metrics such as resource utilization, turnaround time and job latency is a well-known resource scheduling challenge in cloud computing. Despite the significant progress made with the introduction of dominant resource fairness by Ghodsi et al., which ensures major allocation properties such as sharing incentive, strategy-proofness, envy-freeness and Pareto efficiency to be achieved, in practice, strict adherence to fairness may still lead to non-optimized utilization of resources when a series of jobs are scheduled on a shared computing infrastructure. On the other hand, prioritizing optimization without consideration for fairness is not acceptable. Therefore, there is a need for an approach that can address both fairness and optimization. In this paper, we present LPWD, an enhanced Dominant Resource Fairness based scheduling approach that utilizes linear programming based calculated weights to balance fairness with user-specific configurable optimization objective. LPWD maintains relative and proportional fairness and employs linear programming to conduct dynamic resource allocation planning for arriving jobs on-the-fly and generate weights for each job based on a multi-objective optimization. These calculated weights are then applied to the weighted dominant resource fairness policy that guides the reordering of the jobs in the queue before deployment to the cloud. We have implemented and evaluated LPWD in a Kubernetes cluster hosted on the Chameleon cloud infrastructure, which is a configurable platform widely used by scientific communities. Our workloads are based on normalized Google trace data and Alibaba trace data and capture characteristics of resource demands in a real-world production setting. The experimentation with LPWD shows that it leads to resource utilization improvement up to 7.62% and overall turnaround time improvement up to 8.21% while maintaining job latency.

关键词： Hands Cloud computing Processor scheduling Production linear programming Dynamic scheduling Planning Resource management Optimization Queueing analysis

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An Almost Feasible Sequential linear programming Algorithm

An Almost Feasible Sequential Linear Programming Algorithm

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European Control Conference (ECC)

作者： David Kiessling Charlie Vanaret Alejandro Astudillo Wilm Decré Jan Swevers Dept. of Mechanical Engineering MECO Research Team KU Leuven and Flanders Make@Ku Leuven Leuven Belgium Mathematical Algorithmic Intelligence Division Zuse-Institut Berlin Berlin Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper proposes an almost feasible Sequential linear programming (afSLP) algorithm. In the first part, the practical limitations of previously proposed Feasible Sequential linear programming (FSLP) methods are discussed along with illustrative examples. Then, we present a generalization of FSLP based on a tolerance-tube method that addresses the shortcomings of FSLP. The proposed algorithm afSLP consists of two phases. Phase I starts from random infeasible points and iterates towards a relaxation of the feasible set. Once the tolerance-tube around the feasible set is reached, phase II is started and all future iterates are kept within the tolerance-tube. The novel method includes enhancements to the originally proposed tolerance-tube method that are necessary for global convergence. afSLP is shown to outperform FSLP and the state-of-the-art solver IPOPT on a SCARA robot optimization problem.

关键词： Europe linear programming Optimization Robots Convergence

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Recoverable Possibilistic linear programming

Recoverable Possibilistic Linear Programming

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Adam Kasperski Paweł Zieliński Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

In this paper, a recoverable version of a linear programming problem under a possibilistic model of uncertainty is considered. It is assumed that a feasible first-stage solution can be modified to some extent in the second stage by applying a limited recovery action. The first-stage solution costs are known exactly, while the second-stage solution costs are uncertain. To model this uncertainty, the possibility theory is applied. Namely, possibility distributions for the uncertain second-stage costs, in the form of fuzzy intervals, are provided. Solution concepts, based on the necessity measure, are adopted. Efficient methods of solving the resulting problems are shown.

关键词： Uncertainty Costs Possibility theory Atmospheric measurements Measurement uncertainty linear programming Particle measurements

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Positive Filtering of Positive Complex Networks Using linear programming

Positive Filtering of Positive Complex Networks Using Linear...

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Chinese Control and Decision Conference, CCDC

作者： Qingquan Xu Xiaohan Lin Wei Xing Chengyuan Zhan Junfeng Zhang School of Information and Communication Engineering Hainan University Haikou China Department of Instrumentation and Automation Changjiang River Scientific Research Institute Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper investigates the filtering design of positive complex networks in both discrete- and continuous-time contexts. A positive filtering is proposed for discrete-time complex networks. The filtering gain matrices are designed based on a matrix decomposition approach. All positivity and stability conditions are described in terms of linear programming. A novel positivity analysis framework is constructed for complex networks and the corresponding error systems and a gain filtering design is solved using copositive Lyapunov function. The presented filtering design approach is developed for continuous-time complex networks. Finally, an example is provided to verify the validity of the proposed filtering.

关键词： Maximum likelihood detection Upper bound Filtering Complex networks Nonlinear filters programming linear programming

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Strategic Management for Enhancing Water Quality in the Barito River Basin Through Cost Optimization Using linear programming

Strategic Management for Enhancing Water Quality in the Bari...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Eddy Susanto Eddy Setiadi Soedjono Harmin Sulistiyaning Titah Departement of Technology Management School of Interdisciplinary Management and Technology Sepuluh Nopember Institute of Technology Surabaya East Java Departement of Enviromental Engineering Sepuluh Nopember Institute of Technology Surabaya East Java

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

The Barito River Basin, a crucial water resource in Indonesia, is facing significant environmental challenges due to rapid urbanization, industrialization, and agricultural activities. These activities have led to the degradation of water quality, posing threats to both the ecosystem and the communities reliant on the river. This study proposes a strategic management framework aimed at enhancing water quality in the Barito River Basin through cost optimization using linear programming (LP). By integrating LP techniques, the study seeks to identify the most cost-effective measures for pollution control and resource allocation, ensuring compliance with water quality standards. The approach involves formulating an objective function to minimize the total cost of implementing water treatment and pollution mitigation strategies while satisfying constraints related to pollutant levels such as pH, Total Suspended Solids (TSS), Iron (Fe), Manganese (Mn), Dissolved Oxygen (DO), and Biological Oxygen Demand (BOD). The study demonstrates the application of LP in selecting optimal combinations of treatment methods, such as the use of Lime and Caustic Soda, to achieve the desired water quality at minimal costs. The results underscore the potential of LP in facilitating sustainable water management practices, balancing economic and environmental objectives, and supporting decision-makers in developing effective policies for the preservation of the Barito River Basin.

关键词： Costs Pollution control Water quality linear programming Iron Rivers Pollution measurement Resource management Water resources Manganese

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Research on Agricultural Planting Strategy Combining Simulated Annealing Algorithm and linear programming

Research on Agricultural Planting Strategy Combining Simulat...

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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Yangliuliu Xia Hanling Mai Siqi Zhu Weiting Su Shanghai Normal University Shanghai China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper investigates an agricultural planting strategy that integrates simulated annealing algorithm and linear programming. Addressing the crop planting issues in mountainous regions of Northern China, an optimization model is proposed, aiming to optimize the cropping structure and enhance production efficiency and economic returns by comprehensively considering factors such as land types, crop adaptability, planting costs, and profits. Data on mountainous land and crops were collected, and the simulated annealing algorithm was employed to refine the initial solution obtained from linear programming, thereby addressing uncertainties and risks in actual production. The model predicts planting yields from 2024 to 2030, revealing a stable growth trend in net income, necessitating close monitoring of market dynamics to mitigate potential risks.

关键词： Uncertainty Heuristic algorithms Optimization models Crops Simulated annealing Production Predictive models Prediction algorithms linear programming Monitoring

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Goal Recognition via linear programming

arXiv

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arXiv 2024年

作者： Meneguzzi, Felipe Pereira, Ramon Fraga de A. Santos, Luísa R. Pereira, André G. University of Aberdeen United Kingdom Pontifical Catholic University of Rio Grande do Sul Brazil University of Manchester United Kingdom Federal University of Rio Grande do Sul Brazil

Goal Recognition is the task by which an observer aims to discern the goals that correspond to plans that comply with the perceived behavior of subject agents given as a sequence of observations. Research on Goal Recognition as Planning encompasses reasoning about the model of a planning task, the observations, and the goals using planning techniques, resulting in very efficient recognition approaches. In this article, we design novel recognition approaches that rely on the Operator-Counting framework, proposing new constraints, and analyze their constraints' properties both theoretically and empirically. The Operator-Counting framework is a technique that efficiently computes heuristic estimates of cost-to-goal using Integer/linear programming (IP/LP). In the realm of theory, we prove that the new constraints provide lower bounds on the cost of plans that comply with observations. We also provide an extensive empirical evaluation to assess how the new constraints improve the quality of the solution, and we found that they are especially informed in deciding which goals are unlikely to be part of the solution. Our novel recognition approaches have two pivotal advantages: first, they employ new IP/LP constraints for efficiently recognizing goals;second, we show how the new IP/LP constraints can improve the recognition of goals under both partial and noisy observability. © 2024, CC BY.

关键词： linear programming

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Waterflooding optimization based on the CRM and solving the linear programming problem

SOCAR PROCEEDINGS

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SOCAR PROCEEDINGS 2023年 59-+页

作者： Pyatibratov, P., V Zammam, M. Gubkin Univ Moscow Russia

Among the many technologies for increasing the efficiency of oil field development using waterflooding, the least economically expensive include hydrodynamic methods for increasing oil recovery, based on changing the directions of filtration flows. Thus, one of the key tasks of increasing the efficiency of waterflooding at a late stage of development is to reduce the volume of ineffective injection. The article discusses the problem of redistributing volumes of injected water between injection wells for a given total injection volume in order to increase oil production. An analytical relationship is proposed for assessing injection efficiency, based on known displacement characteristics and calculated well interference coefficients based on CRMP. The optimization problem is formulated as a linear programming problem. Using the example of a fragment of an oil deposit, the possibility of assessing the injectivity of injection wells is shown, which makes it possible to increase the predicted oil production by changing the direction of filtration flows in the formation. As a result of applying the developed algorithm, the predicted increase in cumulative oil production amounted to 19683 m(3) (9.5%) over 15 years.

关键词： waterflooding optimization capacitive-resistive model CRM linear programming injection efficiency interwell connectivity

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