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Optimal Mission Planning for Multiple Agile Satellites Using Modified Dynamic programming

JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第3期21卷 279-289页

作者： Lee, Kimoon Kim, Dong-Jin Chung, Dae-Won Lee, Seonho Univ Sci & Technol Daejeon 34113 South Korea Korea Aerosp Res Inst Daejeon 34133 South Korea Korea Aerosp Res Inst Sch Dept Aerosp Syst Engn Gajung Ro South Korea

This paper aims to optimize imaging mission planning for multiple agile Earth observation satellites using a modified dynamic programming (MDP) algorithm. The mission planning problem is mathematically modeled using mixed-integer linear programming, which defines the objective function and constraints. An MDP algorithm is proposed to address the challenge of extended computation time inherent in typical deterministic dynamic programming, aiming to optimize the imaging scheduling problem. Numerical simulations are conducted under various target numbers and satellites in a relatively narrow mission area with a high target density. The simulation results illustrate the stage configuration and target observation sequence over time, and this paper investigates the achievement rate, measured as the ratio of obtained profit and target observations to the total. The trend of computation time is also analyzed across all mission scenarios. Ultimately, the validity of the MDP algorithm is confirmed, as it demonstrates superior performance compared to the genetic algorithm, the branch-and-bound method, and the greedy algorithm.

关键词： Mission Planning Optimization Modified Dynamic programming Agile Earth Observation Satellite mixed integer linear programming

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Distributed User Association and Computation Offloading in UAV-Assisted Mobile Edge Computing Systems

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IEEE ACCESS 2024年 12卷 63548-63567页

作者： Wang, Tong You, Chuanchuan Hubei Univ Econ Sch Informat Engn Wuhan Hubei Peoples R China Hubei Univ Econ Hubei Internet Finance Informat Engn Technol Res C Wuhan 430205 Hubei Peoples R China Hubei Univ Econ Comp Applicat Technol Res Ctr Wuhan 430205 Hubei Peoples R China

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems have emerged as promising solutions for enhancing the computational capabilities and reducing latency in next-generation wireless networks. However, the finite energy capacity of UAVs presents a significant challenge. In this study, we formulate a joint user association, computing resource allocation, and task offloading problem, called User Association and Computation Offloading (UACO), to minimize the energy consumption of both mobile devices and UAVs by considering the computational resource limitations of UAVs and the minimum user offloading data rate requirements. The UACO problem is a mixed-integer linear programming (MILP) problem that makes it NP-hard. We transform UACO into an Offload Exact Constrained Potential Game (OFECPG) based on game theory, which facilitates distributed execution. We propose the Best Response dynamics based on Local Decomposition (BR-LD) and Better Response dynamics based on the Local Switch operator (BR-LS) to enhance computational efficiency. We prove the existence of a pure strategy Nash Equilibrium (NE) and the convergence of the proposed algorithms. Extensive simulations demonstrated the effectiveness of the OFECPG, BR-LD, and BR-LS, showing significant improvements in system energy consumption compared to the baseline schemes. Our approach offers valuable insights into the design of efficient association and offloading schemes for multi-UAV MEC networks.

关键词： Autonomous aerial vehicles Servers Task analysis Games Computational modeling Multi-access edge computing Energy consumption Nash equilibrium mixed integer linear programming Constrained potential game unmanned aerial vehicle (UAV) multi-access edge computing mixed-integer linear programming (MILP) computation offloading user Association

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MILP and PSO approaches for solving a hydropower reservoirs intraday economic optimization problem

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2024年 1-24页

作者： Castro-Freibott, Rodrigo Gerboles, Carlos Garcia-Castellano Garcia-Sanchez, Alvaro Ortega-Mier, Miguel Univ Politecn Madrid Ind Engn Business Adm & Stat Dept Jose Gutierrez Abascal 2 Madrid 28006 Spain

Short-term hydropower generation with several water reservoirs requires deciding, for each moment in time, the volume of water (outflow) that is released from every reservoir to be turbined and generate energy. Knowing the price of energy at every time period, the objective is to maximize the income earned from the generated energy. In this paper, we present (1) a Hydropower Reservoirs Operation Optimization problem with a higher level of detail than those found in the literature, encompassing temporal delays, water hammer effects, and increased temporal discretization, among others features, and (2) two distinct approaches for addressing this problem: MILP and PSO. These methods are compared across instances of varying nature to evaluate their performance. We make our code available on GitHub: https://***/baobabsoluciones/flowing-basin.

关键词： Daily optimization Hydropower generation Particle swarm optimization Multireservoir mixed integer linear programming

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An interregional optimization approach for time series aggregation in continent-scale electricity system models

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ENERGY 2025年 324卷

作者： Brown, Patrick R. Cole, Wesley J. Mai, Trieu Natl Renewable Energy Lab Golden CO 80401 USA

Modeling electric power systems with high shares of weather-dependent resources requires tradeoffs between temporal, spatial, and operational resolution. Many studies perform time series aggregation using clustering algorithms to reduce the temporal dimension, but when modeling continent-scale electricity systems that are large enough to contain multiple independent weather systems, this approach requires large numbers of representative periods to minimize errors in regional wind and solar capacity factors. Here, a new optimization-based approach for representative period selection and weighting is introduced that minimizes regional errors in average renewable capacity factors and electricity demand. The method delivers higher regional fidelity with fewer representative periods than alternative clustering methods when applied to wind, solar, and demand profiles for the contiguous United States. When representative periods are selected from multiple weather years, the optimized method reproduces regional averages with lower error than a complete 365-day time series from any single weather year. The method identifies only representative (as opposed to outlying) periods but can be combined with an iterative "stress period" identification approach to guide efficient decision-making considering both average and high-risk weather conditions.

关键词： Time series aggregation mixed integer linear programming Temporal resolution Representative days Energy system modeling

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Task Assignment for Multiple Multi-purpose Unmanned Aerial Vehicles Using Greedy Algorithm

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2024年第4期25卷 1380-1394页

作者： Jeon, Ha-Min Lim, Jae-Woo Ryoo, Changkyung Inha Univ Dept Aerosp Engn Incheon South Korea

Unmanned aerial vehicle fleets demand effective task assignment since the task scope of each vehicle depends on the task-specific equipment it carries. Here, we propose real-time task assignment for multiple multipurpose unmanned aerial vehicles using a greedy algorithm. The greedy algorithm does not require complex mathematical modeling and can respond to real-time situational changes due to its fast computation. The proposed algorithm uses the total flight time of all unmanned aerial vehicles as the objective function so that each vehicle completes all tasks within its available battery level. To increase the diversity of solutions, the algorithm is dually computed in the forward and backward directions from the starting and destination points, respectively. We apply the algorithm to water resource management and compare the results with those of mixed integer linear programming and the genetic algorithm. While the flight-time cost is similar across algorithms, computation is 3-8 times faster for the proposed algorithm than for the genetic algorithm. This advantage increases as the number of tasks and/or unmanned aerial vehicles increases. The proposed algorithm's consideration of remaining battery levels when selecting randomly unmanned aerial vehicles also results in a higher average remaining battery level than the other algorithms. These results suggest that the proposed algorithm shows promise and feasibility for application to real-time task assignment scenarios for multiple multipurpose unmanned aerial vehicles.

关键词： Task assignment Greedy algorithm mixed integer linear programming Genetic algorithm Multipurpose unmanned aerial vehicle Water resource management

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Evaluation of effect of providing balancing reserves to power system in Japan through operational control of water pumps

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ENERGY REPORTS 2024年 11卷 3424-3435页

作者： Akimoto, Yushi Negishi, Shintaro Ikegami, Takashi Tokyo Univ Agr & Technol Grad Sch Bio Applicat & Syst Engn 2-24-16 Naka Cho Koganei Shi Tokyo 1848588 Japan Kanagawa Univ Dept Elect Elect & Informat Engn 3-27-1 RokkakubashiKanagawa ku Yokohama 2218686 Japan

In Japan, renewable energy is being rapidly introduced. Hence, surplus power and the required balancing reserves will increase. In this study, to deal with this problem, we focus on a water distribution system (WDS), especially water pumps, which can contribute to supply-demand balancing by shifting their operation. To evaluate the current potential of water pumps in Japan, we modeled them and simulated their operation, in which they can shift their pumping schedule to reduce surplus power and provide balancing reserves to the power system, adhering to constraints such as the demand for water and the storage of water reservoirs. We then developed a numerical model that simultaneously optimizes the operation of generators and water pumps throughout Japan, and simulated their operation in 2040. The results showed that despite increasing their power consumption, pumps shifted their pumping schedule to contribute to the entire power system and reduced fossil generator fuel costs and renewable energy output curtailment.

关键词： Water pump Water distribution system Balancing reserves Supply-demand analysis mixed integer linear programming

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Optimizing spot intensity with lower bound constraints for IMPT: Exposing shortcomings and introducing an enhanced strategy

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MEDICAL PHYSICS 2024年第10期51卷 7523-7544页

作者： Han, Dong Tong, Jingdong Yang, Yu Liu, Hongcheng Liang, Xiaoying Yaddanapudi, Sridhar Park, Chunjoo Tan, Jun Furutani, Keith Beltran, Chris Lu, Bo Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Mayo Clin Florida Dept Radiat Oncol Jacksonville FL 32224 USA

BackgroundIntensity Modulated Proton Therapy (IMPT) is a sophisticated radiation treatment allowing for precise dose distributions. However, conventional spot selection strategies in IMPT face challenges, particularly with minimum monitor unit (MU) constraints, affecting planning quality and *** study introduces an innovative Two-Stage mixed integer linear programming (MILP) method to optimize spot intensity in IMPT with Lower Bound (LB) constraints. This method seeks to improve treatment planning efficiency and precision, overcoming limitations of existing *** approach evaluates prevalent IMPT spot selection strategies, identifying their limitations, especially concerning MU constraints. We integrated LB constraints into a MILP framework, using a novel three-phase strategy for spot pool selection, to enhance performance over traditional heuristic methods and L1 + L infinity strategies. The method's efficacy was tested in eight study cases, using Dose-Volume Histograms (DVHs), spot selection efficiency, and computation time analysis for benchmarking against established *** proposed method showed superior performance in DVH quality, adhering to LB constraints while maintaining high-quality treatment plans. It outperformed existing techniques in spot selection, reducing unnecessary spots and balancing precision with efficiency. Cases studies confirmed the method's effectiveness in producing clinically feasible plans with enhanced dose distributions and reduced hotspots, especially in cases with elevated LB *** Two-Stage MILP strategy signifies a significant advancement in IMPT treatment planning. By incorporating LB constraints directly into the optimization process, it achieves superior plan quality and deliverability compared to current methods. This approach is particularly advantageous in clinical settings requiring minimum spot number and high MU LB constraints, offering the potential for

关键词： intensity modulated proton therapy lower bound constraints mixed integer linear programming spot intensity optimization treatment planning

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Maintaining the Giant Component in Networks With Edge Weighted Augmentation Given Causal Failures

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IEEE ACCESS 2024年 12卷 136588-136598页

作者： Zhang, Zuyuan Radhakrishnan, Sridhar Barker, Kash Gonzalez, Andres D. Univ Oklahoma Sch Comp Sci Norman OK 73019 USA Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA

Understanding the relationship between various nodes of a network is critical for building a robust and resilient network. Studying and understanding the causes of network failures is vital to prevent electric grid blackouts, mitigate supply chain failures, and keep transportation systems functional, among others. Failure of one or more nodes may cause other nodes in the network to fail as well, that is, failures are causal. In general, these failure relationships extend beyond the immediate neighborhood of failed nodes. When any of the causal failures are applied (the nodes of the causal failures are removed), the network could be disconnected. One can add edges to the original network (augmentation problem) in such a way that the network remains connected after applying each of the causal failures, or the largest connected component in the disconnected network is at least a given specified size alpha x n ( alpha -giant component), where n is the number of nodes in the original network. By choosing this size, we guarantee that the network is active for a large population of entities represented by the nodes in the giant component. More formally, we consider the network augmentation problem when faced with causal failures as follows. Given a network G=(V, E) , its complement G=(V, E) with a cost function c: E -> R+ and the causality set C , find a subset of E with a minimum total cost such that the network maintains at least one alpha -giant component when each causal failure in C is applied to the augmented graph. We prove the NP-hardness of this problem and present a mixed integer linear programming model to provide the exact solution to the problem. Furthermore, we design a heuristic algorithm by checking the connected components when applying each causality. Finally, experiments are conducted on synthetic Erd & odblac;s-R & eacute;nyi networks, and we demonstrate the efficacy and efficiency of the heuristic algorithm relat

关键词： Cause effect analysis Robustness Approximation algorithms Heuristic algorithms Costs mixed integer linear programming Measurement Causal failure robustness connectivity augmentation NP-hardness heuristic algorithms

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A bottom-up multi-objective optimisation approach to dynamic facility layout planning

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第3期62卷 626-643页

作者： Perez-Gosende, Pablo Mula, Josefa Diaz-Madronero, Manuel Univ Politecn Valencia Res Ctr Prod Management & Engn CIGIP Alcoy Spain Univ Politecn Salesiana Ind Engn Dept Guayaquil Ecuador

Dynamic facility layout planning (DFLP) involves determining an appropriate arrangement scheme of the elements making up the production system for each time period into which the planning horizon is divided. When formulating the problem as an optimisation model, using the traditional top-down approach is usual, which firstly determines the block layout (BL) and then the detailed layout (DL) of each work cell. However by this approach, the BL generates area constraints in the detailed phase, which sometimes limit its implementation. In this context, the present paper presents a multi-objective mixed integer non-linear programming (MOMINLP) model that allows the problem to be addressed by considering an alternative approach, known in the literature as the bottom-up approach. The proposed model, called bottom-up mDFLP, considers three objective functions: (1) minimise the total material handling cost (TMHC) and the total rearrangement cost (TRAC);(2) maximise the total closeness rating (TCR) between departments;(3) maximise the area utilisation ratio (AUR). The original MOMINLP is transformed into a more computationally efficient multi-objective mixed integer linear programming (MOMILP) model. The proposed model is applied and validated in a case study of a company in the metal-mechanic sector with 12 departments for three 4-month periods.

关键词： Facility layout facility planning multicriteria decision making non-linear programming mixed integer linear programming

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A matheuristic-oriented iterated greedy algorithm for multi-mode resource-constrained project scheduling problem under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 193卷

作者： Gao, Zheng Zhang, Liping Yu, Puliang Zhang, Zikai Li, Zixiang Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Peoples R China

Multi-mode resource-constrained project scheduling problem is one of the most important branches of combinatorial optimization problems. The aim is to find a feasible solution consisting of activity start time and execution mode so that the project makespan is minimized. However, there are a lot of uncertain events, that bring great challenges to project scheduling. We first propose a mixed integer linear programming model where the activity duration is uncertain. Then two uncertain parameters are introduced to describe the disturbance degree of uncertain activity duration and the allowable violation degree of constraints respectively, and the MILP model with uncertain activity duration is converted to its deterministic robust counterpart model. A matheuristic local optimization approach is proposed to balance computational time with the optimization of the computational result. A matheuristic-oriented iterated greedy algorithm that combines a matheuristic local optimization approach and an iterated greedy algorithm is proposed to solve this problem in this paper. Experimental results indicate that the robust counterpart model can obtain robust optimal solutions for small-scale instances in accepted computational time. Meanwhile, the proposed matheuristic-oriented iterated greedy algorithm can obtain robust near-optimal solutions for large-scale instances and is superior to other compared algorithms.

关键词： Project scheduling Uncertainty Robust counterpart model mixed integer linear programming Matheuristic

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