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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Akshay Jaitly Siavash Farzan Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optimization methods for long-horizon, dynamically feasible motion planning in robotics tackle challenging nonconvex and discontinuous optimization problems. Traditional methods often falter due to the nonlinear characteristics of these problems. We introduce a technique that utilizes learned representations of the system, known as Polytopic Action Sets, to efficiently compute long-horizon trajectories. By employing a suitable sequence of Polytopic Action Sets, we transform the long-horizon dynamically feasible motion planning problem into a linear Program. This reformulation enables us to address motion planning as a mixed integer linear Program (MILP). We demonstrate the effectiveness of a Polytopic Action-Set and Motion Planning (PAAMP) approach by identifying swing-up motions for a torque-constrained pendulum as fast as 0.75 milliseconds. This approach is well-suited for solving complex motion planning and long-horizon Constraint Satisfaction Problems (CSPs) in dynamic and underactuated systems such as legged and aerial robots.

关键词： Dynamics Optimization methods Transforms Autonomous aerial vehicles Planning Trajectory mixed integer linear programming Dynamic programming Intelligent robots

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A mixed integer linear programming based optimal operational and sizing approach for a Hybrid Local Microgrid

A Mixed Integer Linear Programming based optimal operational...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Mohammad Shafayet Hossain Mohammad Iftekhar Bin Ashraf Saif Ahmed Department of Energy University of Lorraine Department of Electrical & Computer Engineering North South University Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Optimal sizing of microgrids is achieving higher importance in the current era of energy transition driven by renewable sources. Due to the intermittence of the renewable sources i.e. PV, wind assisted by energy storages i.e. battery the optimal sizing of the microgrids alonf with cost minimization is a great challenge. This study presents a methodological framework for determining the optimal configuration of a hybrid microgrid, integrating various energy resources such as photovoltaic (PV) generation, battery storage, a genset, and grid interconnection. Employing Pyomo optimization tool with a mixed-integer linear programming algorithm, the research focuses on sizing components within a 20 kW PV microgrid. Through meticulous examination of load demand and installed PV capacity, the algorithm strategically optimizes the sizing of PV panels, batteries, gensets, and grid interactions to minimize both capital expenditure (CAPEX) and operational expenditure (OPEX). The microgrid's performance is analyzed under three pricing schemes: net metering, selling excess energy to the grid (selling price less than buying price), and giving it away (zero price). Additionally, in off-grid mode, the total CO2 emissions are minimized. The proposed methodology offers a systematic approach for designing economically viable and efficient hybrid microgrids, essential for sustainable energy systems.

关键词： Renewable energy sources Wind Costs Wind energy Microgrids Pricing Hybrid power systems Batteries mixed integer linear programming Carbon emissions

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Neural Network-Based Lyapunov Stability Verification for Continuous-Time Nonlinear Systems via mixed integer linear programming

Neural Network-Based Lyapunov Stability Verification for Con...

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IEEE International Conference on Control and Robotics Engineering (ICCRE)

作者： Jingya Wang Yongbin Yu Xiangxiang Wang Xiao Feng Xinyi Han Jingye Cai School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

This paper discusses Lyapunov stability verification methods for continuous-time nonlinear systems. Traditional mathematical methods require a lot of manual calculations, which consume a lot of time and energy. To address the problem of the low efficiency in traditional methods, this paper introduces neural networks into the design of the Lyapunov function to achieve independent verification. First, a neural network is used to represent the Lyapunov function. Then, the Lyapunov stability condition is converted into a mixed integer linear programming (MILP) problem, and the solution to the optimization problem is solved through the MILP solver to verify whether the output of the neural network satisfies the Lyapunov stability condition. In addition, this paper gives the training loss function of the Lyapunov neural network, which mainly consists of optimization problems. Finally, a simulation example is given to illustrate the effectiveness of this method.

关键词： Training Deep learning Neural networks Manuals Stability analysis mixed integer linear programming Nonlinear systems

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Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

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ENERGY CONVERSION AND MANAGEMENT-X 2024年 22卷

作者： Holtwerth, Alexander Xhonneux, Andre Mueller, Dirk Forschungszentrum Julich Inst Energy & Climate Res Energy Syst Engn IEK 10 Wilhelm Johnen Str D-52428 Julich Germany Rhein Westfal TH Aachen Inst Energy Efficient Bldg & Indoor Climate E ON Energy Res Ctr Mathieustr 10 D-52074 Aachen Germany

The derivation of an efficient operational strategy for storing intermittent renewable energies using a hybrid battery-hydrogen energy storage system is a difficult task. One approach for deriving an efficient operational strategy is using mathematical optimization in the context of model predictive control. However, mathematical optimization derives an operational strategy based on a non-exact mathematical system representation for a specified prediction horizon to optimize a specified target. Thus, the resulting operational strategies can vary depending on the optimization settings. This work focuses on evaluating potential improvements in the operational strategy for a hybrid batteryhydrogen energy storage system using mathematical optimization. To investigate the operation, a simulation model of a hybrid energy storage system and a tailor-made mixed integer linear programming optimization model of this specific system are utilized in the context of a model predictive control framework. The resulting operational strategies for different settings of the model predictive control framework are compared to a rulebased controller to show the potential benefits of model predictive control compared to a conventional approach. Furthermore, an in-depth analysis of different factors that impact the effectiveness of the model predictive controller is done. Therefore, a sensitivity analysis of the effect of different electricity demands and resource sizes on the performance relative to a rule-based controller is conducted. The model predictive controller reduced the energy consumption by at least 3.9 % and up to 17.9% compared to a rule-based controller. Finally, Pareto fronts for multi-objective optimizations with different prediction and control horizons are derived and compared to the results of a rule-based controller. A cost reduction of up to 47 % is achieved by a model predictive controller with a prediction horizon of 7 days and perfect foresight.

关键词： Model predictive control Optimization mixed integer linear programming Hybrid battery-hydrogen energy storage system

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A mixed integer linear programming model for quarantine-based home healthcare scheduling under uncertainty

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Healthcare Analytics 2024年 6卷 100356-100356页

作者： Nabavizadeh, Najmeh Kayvanfar, Vahid Rafiee, Majid Department of Industrial Engineering Sharif University of Technology Tehran Iran Division of Engineering Management and Decision Sciences College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha 34110 Qatar

Home healthcare companies (HHC) have emerged as vital alternatives to traditional hospitals, particularly in meeting the healthcare needs of individuals within the comfort of their homes. The COVID-19 pandemic has amplified the significance of HHC services, offering a crucial alternative for patients and the elderly to follow quarantine protocols while receiving essential healthcare at home. Consequently, HHC companies must align their planning strategies with the World Health Organization (WHO) health guidelines. This research introduces a mixed integer linear programming (MILP) model tailored for home healthcare services during COVID-19, aiming to ensure strict adherence to quarantine protocols while enhancing service efficiency and quality. The proposed vehicle routing problem with pickup/delivery and time window formulation incorporates critical elements such as patient and caregiver classification, work and break regulations adherence, workload balancing, and multi-depot capabilities. The model addresses uncertain demand and service times through a stochastic programming approach to enhance practicality. K-means clustering is applied to streamline scenarios, with a sensitivity analysis determining the optimal number of clusters. Additionally, measures intrinsic to stochastic programming, such as the Expected Value of Perfect Information (EVPI) and Value of Stochastic Solution (VSS), are computed for comprehensive analysis. © 2024 The Authors

关键词： COVID-19 Home healthcare mixed integer linear programming Pickup and delivery Stochastic programming Vehicle routing

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Adaptive Multi-Agent System and mixed integer linear programming Optimization Comparison for Grid Stability and Commitment Mismatch in Smart Grids 11

Adaptive Multi-Agent System and Mixed Integer Linear Program...

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11th IEEE-PES Innovative Smart Grid Technologies Europe (IEEE-PES ISGT Europe)

作者： Zafar, S. Maurya, V Blavette, A. Camilleri, G. Ben Ahmed, H. Gleizes, M-P ENS Rennes SATIE Rennes France Univ Toulouse IRIT Toulouse France ENS Rennes SATIE CNRS Rennes France

ISBN: (纸本)9781665448758

Existing electrical networks are going through a transition and distributed energy resource, if not managed properly, can hinder this transition. Uncontrolled introduction of photovoltaics and electric vehicles in distribution networks would lead to substantial issues such as commitment mismatches, line congestions, voltage deviations, etc. This paper presents the use of a classical approach, mixed integer linear programming optimization, and a novel approach, adaptive multi-agent system, to solve the highlighted distribution side challenges by utilizing electric vehicles' storage capacity. This comparison serves as a great tool to benchmark the performance of the under-development adaptive multi-agent system methodology.

关键词： Multi-Agent Systems mixed integer linear programming Optimality Gap Electric Vehicles Balance Responsible Party Distribution Network Stability

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A mixed-integer linear programming model and a metaheuristic approach for the selection and allocation of land parcels problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第4期30卷 1730-1754页

作者： Fernandez Gil, Alejandro Gomez Sanchez, Mariam Castro, Carlos Perez-Alonso, Alain Univ Tecn Federico Santa Maria Dept Informat Valparaiso Chile Univ Tecn Federico Santa Maria Dept Elect & Informat Concepcion Chile

This article is about the current agricultural scenario, where large-scale production causes large amounts of food to be transported to various points of consumption, causing substantial emissions of so-called greenhouse gases and increasing the carbon footprint. Land use optimization and land parcel allocation are essential areas of agriculture research that currently represent relevant challenges and are classified as combinatorial optimization problems. In this context, the Selection and Allocation of Land Parcels Problem (SA-LPP) is proposed;its goal is to optimize the selection and allocation of land parcels with rectangular shapes in small areas available for food production. We propose a reformulation of the SA-LPP as a variant of the two-dimensional orthogonal packing problem (2OPP), called Group-2OPP. This problem was solved through a mixed-integer linear programming (MILP) model, but due to the model complexity, we also propose a Greedy Randomized Adaptive Search Procedure metaheuristic approach. Some sensitivity analyses were performed as well to evaluate the impact of parameters on the solutions. Computational results show that the proposed metaheuristic outperforms the MILP model in terms of solution quality and computational times.

关键词： selection and allocation of land parcels problem agriculture combinatorial optimization mixed integer linear programming metaheuristic GRASP packing problem

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mixed integer linear programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones

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ENERGIES 2019年第11期12卷

作者： Feng, Zhongkai Niu, Wenjing Wang, Sen Cheng, Chuntian Song, Zhenguo Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Hubei Peoples R China Minist Water Resources Key Lab Pearl River Estuarine Dynam & Associated Guangzhou 510611 Guangdong Peoples R China Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Hubei Peoples R China

Due to booming economic development over the past decades, energy demands in most of China's provincial power grids have increased sharply, and it has become challenging to guarantee the energy balance at peak periods. In many provincial electric systems of China, gas-fired generators are one of the most important peaking power sources to respond the load change at peak periods. To meet this practical necessity, a novel mixed integer linear programming model is proposed in this paper for the peak operation of gas-fired generating units with disjoint-prohibited operating zones. In the developed model, the objective function is chosen to minimize the peak-valley difference of the remaining load series that is obtained by subtracting the total generation of all the gas-fired units from the original load curve. The real-world simulations in several cases show that the developed model is able to generate satisfying scheduling results by reasonably allocating the power outputs of all the gas-fired generators in the scheduling horizon. Then, the management implications obtained lie in the fact that it is necessary to increase the share of peak power sources in the mid- to long-term planning of an electrical power system;and in the daily operation of the power grid, greater flexibility should be given to the gas-fired units to reduce peak pressure.

关键词： mixed integer linear programming peak shaving operation gas-fired generating unit disjoint prohibited operating zones provincial electric system

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Non-iterative division circuit design with accuracy and performance trade-off based on mixed integer linear programming approach

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MICROELECTRONICS JOURNAL 2022年 127卷

作者： Wu, Lei Jong, Ching Chuen Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 659798 Singapore

A wide range of applications such as image/video processing involve arithmetic division and demand high computing performance but can tolerate some degree of errors or allow small deviations from their theoretical results. This paper presents an approach for non-iterative arithmetic division circuit design, leveraging computation accuracy for high performance. The quotient is modeled as a curved surface and the surface is partitioned into many small rectangular planar stripes for linearity. mixed integer linear programming is adopted to optimize the worst case error due to approximation in each stripe. As a result, an architecture for the division circuits consisting of simple additions and a small look-up table is achieved. The accuracy achieved in terms of the maximum absolute error percentage (MAEP) ranges from 0.44% to 0.11%. Comparing the proposed designs with the existing counterparts with similar MAEP, the area-delay product (ADP) is reduced by more than 40%. Compared to the counterpart with the closest ADP by the single stage logarithmic method, the proposed circuit has a smaller MAEP with 45% reduction. Comparing with the existing non-iterative division circuit by the curve fitting method, the proposed circuit has lowered MAEP and ADP by 21% and 27% respectively.

关键词： Non-iterative divider mixed integer linear programming Approximation

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Optimising aircraft arrivals in terminal airspace by mixed integer linear programming model

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AERONAUTICAL JOURNAL 2020年第1278期124卷 1129-1145页

作者： Cecen, R. K. Cetek, F. Aybek Anadolu Univ Eskisehir Turkey Eskisehir Tech Univ Eskisehir Turkey

Air traffic flow becomes denser and more complex within terminal manoeuvering areas (TMAs) due to rapid growth rates in demand. Effective TMA arrival management plays a key role in the improvement of airspace capacity, flight efficiency and air traffic controller performance. This study proposes a mixed integer linear programming model for aircraft landing problems with area navigation (RNAV) route structure using three conflict resolution and sequencing techniques together: flexible route allocation, airspeed reduction and vector manoeuver. A two-step mixed integer linear programming model was developed that minimises total conflict resolution time and then total airborne delay using lexicographic goal programming. Experimental results demonstrate that the model can obtain conflict-free and time optimal aircraft trajectories for RNAV route structures.

关键词： Air traffic flow management conflict-free arrival sequencing mixed integer linear programming goal programming extended terminal airspace

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