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Variable-structure copula model for multi-scenario bi-level transmission and generation expansion planning

IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第21期16卷 4386-4399页

作者： Wang, Yurong Yang, Ruolin Li, Fangxing Tang, Yi Zhao, Xin Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China Shenzhen Power Supply Bur Co Ltd Shenzhen Guangdong Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN USA

With the large-scale integration of renewable energy into power systems, the strong correlation of wind power output as an important feature will inevitably bring huge impact on power grid planning and operation. However, due to the proliferation of planning and operation scenarios, the traditional power grid planning method can no longer guarantee the operation flexibility and stability under the fluctuation of new energy. In this work, a bi-level transmission and generation expansion planning (TGEP) model considering the correlation of wind power based on mixed integer linear programming (MILP) is proposed. Firstly, a variable-structure point diagnosis method is proposed to improve the fitting accuracy of the correlation. Secondly, a scenario reduction method considering the correlation, joint probability distribution and geometric characteristics is proposed, which effectively provides a more matching operating scenarios basis for planning. Thirdly, a multi-scenario bi-level TGEP model is proposed. The upper-level investment decision model aims at the economical goal, while the lower-level multi-scenario coordinated optimization operation model aims to minimize the weighted operation index. The impact of the correlation on the results of TGEP is examined and verified in case study.

关键词： weighted operation index wind power output lower-level multiscenario scenario reduction method power grids generation expansion planning structure copula model matching operating scenarios basis integer programming linear programming mixed integer linear programming multiscenario bi-level TGEP model traditional power grid planning method bi-level transmission upper-level investment decision model power systems power generation economics optimization operation model probability operation flexibility variable-structure point diagnosis method operation scenarios stability power generation planning investment large-scale integration renewable energy

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Unmanned Aerial Vehicle Routing Problem for Integrated Manned and Unmanned Aircraft Operations

Unmanned Aerial Vehicle Routing Problem for Integrated Manne...

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AIAA Aviation Forum

作者： Matsuno, Yoshinori Andreeva-Mori, Adriana Japan Aerosp Explorat Agcy Tokyo 1810015 Japan

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

Small unmanned aerial vehicles (sUAVs) can potentially be used in a wide range of industrial applications, with a main one being last-mile package delivery systems. This study considers the sUAV-based package delivery systems under integrated operations of manned and unmanned aircraft. A multi-trip time-dependent vehicle routing problem is proposed to address the problem of generating optimal routes for sUAVin the integrated operation. Through numerical simulations of the integrated operation in which helicopter and sUAV missions are performed simultaneously in the same airspace, the optimal routes of sUAV are effectively generated without affecting the helicopter mission.

关键词： Aircraft Operations Helicopters Numerical Simulation Restricted Airspace Industrial Applications Japan Aerospace Exploration Agency Unmanned Aircraft System Urban Air Mobility Path Planning mixed integer linear programming

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An Innovative Formulation Tightening Approach for Job-Shop Scheduling

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第3期19卷 2526-2539页

作者： Yan, Bing Bragin, Mikhail A. Luh, Peter B. Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

Job shops are an important production environment for low-volume high-variety manufacturing. Its scheduling has recently been formulated as an integer linear programming (ILP) problem to take advantages of popular mixed-integer linear programming (MILP) methods, e.g., branch-and-cut. When considering a large number of parts, MILP methods may experience difficulties. To address this, a critical but much overlooked issue is formulation tightening. The idea is that if problem constraints can be transformed to directly delineate the problem convex hull in the data preprocessing stage, then a solution can be obtained by using linear programming (LP) methods without combinatorial difficulties. The tightening process, however, is fundamentally challenging because of the existence of integer variables. In this article, an innovative and systematic approach is established for the first time to tighten the formulations of individual parts, each with multiple operations, in the data preprocessing stage. It is a major advancement of our previous work on problems with binary and continuous variables to integer variables. The idea is to first link integer variables to binary variables by innovatively combining constraints so that the integer variables are uniquely determined by the binary variables. With binary and continuous variables only, it is proved that the vertices of the convex hull can be obtained based on vertices of the LP problem after relaxing binary requirements. These vertices are then converted to tightened constraints for general use. This approach significantly improves our previous results on tightening individual operations. Numerical results demonstrate significant benefits on solution quality and computational efficiency. This approach also applies to other complex ILP and MILP problems with similar characteristics and fundamentally changes the way how such problems are formulated and solved.

关键词： Job shop scheduling Processor scheduling Manufacturing Data preprocessing Systematics Production mixed integer linear programming Formulation tightening job-shop scheduling manufacturing mixed-integer linear programming (MILP)

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A branch-and-cut algorithm for hub network design problems with profits

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NAVAL RESEARCH LOGISTICS 2022年第4期69卷 622-639页

作者： Gao, Yuan Xia, Jun Ke, Hua Beijing Inst Technol Sch Management & Econ Beijing Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Sino US Global Logist Inst Shanghai 200030 Peoples R China Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China

Hub network design problems with profits (HNDPPs) extend the classical hub location problems (HLPs), with the profit maximization objective. HNDPPs aim to design a service network with hub nodes and hub links, select the commodities to be served, and determine the transportation routes for the selected commodities. In this article, we allow the transportation route of each commodity to contain more than one hub link, which generalizes existing HNDPPs and is known to be more profitable. A new path-based formulation is proposed, which is then reformulated in a Benders decomposition fashion, and an exact branch-and-cut algorithm is developed to solve the Benders master problem. At the nodes of the enumeration tree, Benders decomposition algorithm is used to solve a linear relaxation of the Benders master problem. When implementing the Benders decomposition algorithm, the special structure of the subproblem is explored, based on which an efficient method is proposed to generate Benders cuts. In addition, refinements and a heuristic strategy are used to speed up the branch-and-cut algorithm. Extensive numerical experiments on benchmark instances have been carried out to verify the effectiveness and efficiency of the proposed solution method.

关键词： benders decomposition branch-and-cut algorithm hub network design problems with profits mixed integer linear programming

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A milk transportation problem with milk collection centers and vehicle routing

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ISA TRANSACTIONS 2022年 122卷 294-311页

作者： Paredes-Belmar, German Montero, Elizabeth Leonardini, Omar Pontificia Univ Catolica Valparaiso Sch Ind Engn Valparaiso Chile Univ Andres Bello Fac Engn Santiago Chile Univ Andres Bello Ind Engn Program Santiago Chile

In this work, we study, model, and propose two approaches to solve a raw milk transportation problem inspired by a real case of a milk company in Chile. The milk is produced by a set of farms scattered in a large rural area. The company must collect all the production daily using a truck fleet. We address the location of milk collection centers to reduce transportation costs. Each center has a limited capacity and a reduced truck fleet, composed of small trucks, to collect a substantial proportion of the produced milk. Once the milk is accumulated in the collection centers, a fleet of big trucks, traveling from a processing plant, collects the milk of each collection center and some large farms. We propose a mixed-integer linear programming model, a three-stage approach based on mathematical models, and an iterated local search approach to face this problem. We evaluate these approaches' performance using a small case and several real-world examples, including a clustering approach to divide the instance into small sub-instances. The results obtained for the real-world instance show improvements of up to 10% percent when milk collection centers are allowed. (c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Milk collection problem Milk collection centers Vehicle routing mixed integer linear programming Iterated local search

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Towards Peer-to-Peer Split Learning 16

Towards Peer-to-Peer Split Learning

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Vyas, Jayant Nikose, Ritesh Dhananjay Ramicetty, Pallavi Mohan, Shravan Savagaonkar, Milind Maitra, Anutosh Sengupta, Shubhashis Indian Institute of Technology Jodhpur India Accenture Labs Bengaluru India

ISBN: (纸本)9798350383119

In this paper, the N-split learning scheme is presented. This scheme allows for learning a feed-forward deep neural network on devices with limited memory for model and data. The central idea in this scheme is to divide the model and data over different devices. The model is split layer-wise (in subsets of consecutive layers), while the data is split homogeneously. The device with the first few layers starts the forward pass of the backpropagation algorithm for a training sample from its data subset and passes the output to the device with the next subset of layers. This process continues till the last layers are computed. The last device to compute the forward pass then computes the backward pass, and the process proceeds in the reverse direction, thereby calculating a part of the gradients. The gradients are accumulated for a batch of samples, and the parameters are updated. After completing an epoch, the devices perform a swap of the layers, and the above process starts again. It continues till the model is trained satisfactorily. In an improved variant, called Fast N-split learning, the forward and backward passes are considered separately and possibly done on different devices to gain from pipeline parallelism. The layers performing the forward pass store the output in a buffer, which will be used by the devices performing the backward pass. The devices performing backward passes communicate the update for parameters to the devices doing forward passes. As earlier, this continues till the model is trained satisfactorily. Analysis, routines for determining the optimal split for both schemes and simulations are presented for corroboration. © 2024 IEEE.

关键词： Deep Neural Networks Edge Computing mixed integer linear programming Split Learning

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Investigating the Use of linear programming and Evolutionary Algorithms for Multi-Objective Electric Vehicle Charging Problem

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IEEE ACCESS 2022年 10卷 115322-115337页

作者： Singh, Hemant Kumar Ray, Tapabrata Rana, Md Juel Limmer, Steffen Rodemann, Tobias Olhofer, Markus Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Honda Res Inst Europe D-63073 Offenbach Germany

With the increasing uptake of electric vehicles (EVs), the need for efficient scheduling of EV charging is becoming increasingly important. A charging station operator needs to identify charging/discharging power of the client EVs over a time horizon while considering multiple objectives, such as operating costs and the peak power drawn from the grid. Evolutionary algorithms (EAs) are a popular choice when faced with problems involving multiple objectives. However, since the objectives and constraints of this problem can be expressed using linear functions, it is also possible to come up with improvised multi-objective formulations which can be solved with exact techniques such as mixed-integer linear programming (MILP). With both approaches having their potential strengths and pitfalls, it is worth investigating their use to inform the algorithmic choices, which this study aims to address. In doing so, it makes a number of contributions to the topic, including extension of an existing EV charging problem to a multi-objective form;observing some interesting properties of the problem to improve both the MILP and EA solution approaches;and comparing the performance of MILP and EA. The study provides some useful insights into the problem, initial results and quantitative basis for selecting solution approaches, and highlights some areas of further development.

关键词： Electric vehicle charging Optimization Modulation mixed integer linear programming Evolutionary computation Costs Batteries Evolutionary algorithms electric vehicle charging mixed-integer linear programming multi-objective optimization

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Robust QoT Assured Resource Allocation in Shared Backup Path Protection Based EONs

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IEEE ACCESS 2022年 10卷 105528-105545页

作者： Chebolu, Venkatesh Behera, Sadananda Das, Goutam Indian Inst Technol Kharagpur GS Sanyal Sch Telecommun Kharagpur 721302 W Bengal India Univ Cyprus KIOS Res & Innovat Ctr Excellence CY-2109 Nicosia Cyprus

Survivability is mission-critical for elastic optical networks (EONs) as they are expected to carry an enormous amount of data. In this paper, we consider the problem of designing shared backup path protection (SBPP) based EON that facilitates the minimum quality-of-transmission (QoT) assured allocation against physical layer impairments (PLIs) under any single link/shared risk link group (SRLG) failure for static and dynamic traffic scenarios. In general, the effect of PLIs on lightpath varies based on the location of failure of a link as it introduces different active working and backup paths. To address these issues in the design of SBPP EON, we formulate a mixed integer linear programming (MILP) based robust optimization framework for static traffic with the objective of minimizing overall fragmentation. In this process, we use the efficient robust bitloading technique for spectrum allocation where each frequency slot of a request is assigned with different modulation format. In addition, we propose novel SBPP-impairment aware (SBPP-IA) algorithm considering the limitations of MILP for larger networks. For this purpose, we introduce a novel sorting technique named most congested working-least congested backup first (MCW-LCBF) to sort the given set of requests. Next, we employ our SBPP-IA algorithm for dynamic traffic scenario and compare it with existing algorithms in terms of different QoT parameters. Further, we guarantee the QoT of existing requests while allocating each new request without reserving any margin. We demonstrated through simulations that our study provides 33.94-41.02% more QoT guaranteed requests compared to existing ones at 70 Tbps traffic load.

关键词： Resource management Signal to noise ratio Optical fiber networks Crosstalk Heuristic algorithms Optimization Bandwidth mixed integer linear programming Power transmssion Physical layer Elastic optical networks fragmentation mixed integer linear programming physical layer impairments quality of transmission shared backup path protection

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Greedy Target Assignment with Interference Constraints Between Defensive Weapons

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第4期19卷 271-282页

作者： Lee, Daniel Shin, Min Kyu Choi, Han-Lim Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 34141 South Korea

This paper addresses assignment of defensive weapons against a number of incoming targets, particularly when the targets are aiming to a relatively small local area in a high-density manner. The major issue this work tries to deal with is potential interference between the defensive weapons due to short distance between them and/or inclusion within seeker sensing range. The problem is formulated in a time-dependent static weapon-target assignment with operational and interference constraints;a greedy algorithm that features approximate prediction of potential interference. A numerical case study on assignment of defensive interceptor against incoming ballistic objects is presented to demonstrate the efficacy of the proposed method.

关键词： Particle Swarm Optimization Weapon Target Assignment Predicted Impact Point mixed integer linear programming Heading Angle Numerical Simulation Aerodynamic Interference Proportional Navigation Monte Carlo Simulation Target Missiles

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Bus evacuation during no-notice disasters in downtown areas: A case study of the Zhongguancun area, Beijing

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Journal of Safety Science and Resilience 2022年第3期3卷 235-242页

作者： Xingang Li Xiaodan Cui Rui Jiang Bin Jia Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China

This paper focuses on the bus evacuation problem with pedestrians’short-distance walking ability between bus stations during no-notice disasters in downtown areas.A mixed-integer linear programming model is proposed to solve this *** objective function is to minimize the evacuation time and number of *** model obtains the flow of evacuees and buses on each arc to the route of buses in the process of ***,a real-time bus evacuation demand estimation method is proposed based on smart card ***,the example of Zhongguancun area in Beijing is used to verify the practicality and validity of the *** results show that pedestrian short-distance walking can effectively reduce casualties and improve the utilization rate of buses.

关键词： No-notice disaster Bus evacuation Short-distance walking mixed integer linear programming

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