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Dependency-aware task collaborative offloading and resource allocation in UAV enabled edge computing

PEER-TO-PEER NETWORKING AND APPLICATIONS 2025年第3期18卷 1-19页

作者： Huang, Zhenqi Kuang, Zhufang Xu, Bin Bi, Yuanguo Liu, Anfeng Cent South Univ Coll Comp & Math Shaoshan South Rd Changsha 410004 Hunan Peoples R China Northeastern Univ Sch Comp Sci & Engn Wenhua Rd Shenyang 110819 Peoples R China Cent South Univ Sch Elect Informat Lushan South Rd Changsha 410010 Hunan Peoples R China

Edge computing, a new wireless communications technology, can provide abundant resources for computing and storage. Recently, it is a challenging problem to jointly consider the offloading and resource allocation of generally dependent tasks in multi Unmanned Aerial Vehicles (UAVs) multi Terminal Devices (TDs) edge computing system. In this paper, the joint problem of offloading decisions, collaborative decisions and resource allocation of general dependent tasks is investigated, considering and highlighting the cooperation between TDs. The corresponding optimization problem, which is a mixed integer nonlinear programming problem, is formulated. To make this problem solved, an iterative method based on Deep Reinforcement Learning (DRL) and Convex optimization is proposed to decompose the original problem into two subproblems. Given the resource allocation scheme, the Deep Q network (DQN) algorithm is employed to solve the offloading and collaborative decisions of all tasks. Then, given the offloading decision and cooperation decision of all tasks, the convex optimization algorithm is used to solve the optimal resource allocation scheme in the UAV enabled edge computing system. Two subproblems iterative alternately. The simulation results demonstrated that our proposed method can significantly reduce the system energy consumption compared to other schemes.

关键词： Deep reinforcement learning mixed integer nonlinear programming UAV Task dependency Resource allocation

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A QoE and Availability-Aware Framework for Network Slice Placement and Resource Allocation

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IEEE ACCESS 2025年 13卷 1481-1495页

作者： Dobreff, Gergely Bader, Attila Pasic, Alija Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Telecommun & Artificial Intelligence H-1111 Budapest Hungary Ericsson Hungary H-1117 Budapest Hungary

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator's profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a mixed integer nonlinear programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework's effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suita

关键词： Quality of experience Resource management Quality of service Substrates Telecommunication network reliability Network slicing Virtual links Measurement Bandwidth Telecommunications Availability mixed integer nonlinear programming E2E network slicing placement quality of experience (QoE) quality of service (QoS) resource allocation service function chaining (SFC)

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Integrated train timetable with supply-demand interactions at the Chinese high-speed railway

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 131卷

作者： Yuan, Jiawei Gao, Yuan Ke, Jiannan Yang, Lin Beijing Inst Technol Sch Management Beijing 100081 Peoples R China Beijing Inst Technol Digital Econ & Policy Intelligentizat Key Lab Mini Beijing 100081 Peoples R China Beijing Inst Technol Jiaxing Key Lab Digital Econ & Data Operat Yangtze Delta Reg Acad Jiaxing 314019 Peoples R China Wuhan Univ Sch Econ & Management Wuhan 430072 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China

In traditional Chinese railway operations, train timetable and unit assignment are usually separated from pricing and seat allocation, resulting in a decrease in revenue and an increase in operating costs. In this paper, we study the integrated problem of train timetable, unit assignment, pricing, and seat allocation, where passenger choice behavior is modeled by the multinomial logit (MNL) model. This integration enables us to explicitly capture supply-demand interactions. The problem is formulated as a mixed integer nonlinear programming model with the objective of maximizing total profit. By converting the MNL model into its equivalent convex form, we recast the model as a tractable reformulation, allowing the use of a general-purpose solver to solve practical-sized instances. Based on real data from high-speed railway lines in China, three sets of case studies are conducted to validate the effectiveness and efficiency of the proposed approach.

关键词： Train timetable Pricing Passenger choice mixed integer nonlinear programming

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A planar single facility location and border crossing problem

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COMPUTERS & OPERATIONS RESEARCH 2012年第12期39卷 3156-3165页

作者： Canbolat, Mustafa S. Wesolowsky, George O. SUNY Coll Brockport Brockport NY 14420 USA McMaster Univ DeGroote Sch Business Hamilton ON L8S 4L8 Canada

In this study, we tackle the problem of locating a facility in a region where a fixed line barrier divides the region into two. The resulting subregions communicate with each other through a number of passage points located on the line barrier. Our contribution is threefold. First, we formulate the problem as a mixed integer nonlinear programming (MINLP) model and provide an optimal solution methodology based on an Outer Approximation (OA) algorithm. Second, we discuss the minimax version of this problem for locating an emergency facility and use the OA algorithm to solve the problem. We then provide simple example problems and extensive computational results for both problems. Finally, we propose a one-infinity approximation approach for the latter problem which yields a linear model. Practical uses of the models have been discussed in the border crossing context. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Planar facility location Barriers to travel Passage points Border crossing Optimization mixed integer nonlinear programming Outer approximation

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A multi-level vehicle-to-grid optimal scheduling approach with EV economic dispatching model

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ENERGY REPORTS 2021年 7卷 22-37页

作者： Guo, Song Qiu, Zejing Xiao, Chupeng Liao, Hui Huang, Yuping Lei, Ting Wu, Dan Jiang, Qian Nari Grp Corp State Grid Elect Power Res Inst Luoyu Rd 134 Wuhan 430074 Peoples R China Wuhan Efficiency Evaluat Co Ltd State Grid Elect Power Res Inst Wuhan 430074 Peoples R China Chinese Acad Sci Guangzhou Inst Energy Convers Nengyuan Rd 2 Guangzhou 510640 Peoples R China State Grid Shanghai Elect Power Co Yuanshen Rd 1122 Shanghai 201204 Peoples R China

As electric vehicles (EVs) are eco-friendly and have the feature of flexible power storage, the electric vehicle development is strongly supported by many governments. The market share of EVs has been rising steadily in recent years to promote the long-term growth of the available EV battery resources to participate in vehicle to grid (V2G). When a large number of electric vehicles can connect to grids randomly for charging or discharging, this inevitably brings large load fluctuation in regions and new challenges to the operation scheduling and control of power systems. Therefore, this paper proposed a new multi-level optimal V2G scheduling approach, ensuring the smooth operation and control from the V2G control center to the EV users. This paper also introduced a new EV economic dispatch optimization model to minimize the operating costs of regional V2G systems. A case analysis of the IEEE 33-bus system with 100 EVs verified the feasibility of the proposed model and indicated that the proper size control of total EV battery capacities with ramp adjustment can reduce additional load fluctuations caused by large-scale vehicles to grid. Moreover, the multi-stage TOU pricing mode is another effective measures that can trigger a next round of EV charging and discharging service. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Electric vehicle V2G operation Multi-level scheduling approach EV economic dispatching mixed integer nonlinear programming

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OPTIMIZATION STRATEGIES FOR THE VULNERABILITY ANALYSIS OF THE ELECTRIC POWER GRID

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SIAM JOURNAL ON OPTIMIZATION 2010年第4期20卷 1786-1810页

作者： Pinar, Ali Meza, Juan Donde, Vaibhav Lesieutre, Bernard Sandia Natl Labs Dept Informat & Decis Sci Livermore CA 94550 USA Univ Calif Berkeley Lawrence Berkeley Lab High Performance Comp Res Dept Berkeley CA 94720 USA ABB Inc Corp Res Ctr Raleigh NC 27606 USA Univ Calif Berkeley Lawrence Berkeley Lab Environm Energy Technol Div Berkeley CA 94720 USA Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA

Identifying small groups of lines, whose removal would cause a severe blackout, is critical for the secure operation of the electric power grid. We show how power grid vulnerability analysis can be studied as a bilevel mixed integer nonlinear programming problem. Our analysis reveals a special structure in the formulation that can be exploited to avoid nonlinearity and approximate the original problem as a pure combinatorial problem. The key new observation behind our analysis is the correspondence between the Jacobian matrix (a representation of the feasibility boundary of the equations that describe the flow of power in the network) and the Laplacian matrix in spectral graph theory (a representation of the graph of the power grid). The reduced combinatorial problem is known as the network inhibition problem, for which we present a mixed integer linear programming formulation. Our experiments on benchmark power grids show that the reduced combinatorial model provides an accurate approximation, to enable vulnerability analyses of real-sized problems with more than 16,520 power lines.

关键词： mixed integer nonlinear programming network inhibition network flow mixed integer linear programming electric power flow network vulnerability graph theory

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A train dispatching model in case of segment blockages by integrating the prediction of delay propagation

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NEURAL COMPUTING & APPLICATIONS 2024年第7期36卷 3595-3611页

作者： Yang, Han Hu, Wenfeng Ma, Shan Peng, Tao Cent South Univ Sch Automat Changsha Peoples R China

In the high-speed railway system, trains' original timetable is often disturbed by some emergencies including geological disasters and equipment failures, which brings great influence to passengers. This paper proposes a real-time high-speed train dispatching model in case of segment blockages, where a railway network is considered. The model includes the following two parts. First, if the trains are not cancelled or decelerated after a blockage occurs, the scope of the affected trains and stations is roughly estimated via the prediction of delay propagation model. Second, with the overall delay as the objective function, this paper constructs a mixed integer nonlinear programming (MINLP) model by considering the following three adjustment strategies: cancellation, delayed departure and deceleration, where the safe headway of the train operation is guaranteed by the moving blocking principle. Furthermore, to reduce the computation complexity, the solution of the model is only considered within the scope obtained in the first stage. The model is verified by using a small railway network with Nanjing as the hub station, which shows that the model is useful for reducing the effect of a disruption on original timetable, especially in comparison with the First Scheduled First Served (FSFS) rule used in practice.

关键词： High-speed train dispatching Delay propagation Train deceleration mixed integer nonlinear programming

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Accelerating Generalized Benders Decomposition for Wireless Resource Allocation

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第2期20卷 1233-1247页

作者： Lee, Mengyuan Ma, Ning Yu, Guanding Dai, Huaiyu Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27606 USA Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

Generalized Benders decomposition (GBD) is a globally optimal algorithm for mixed integer nonlinear programming (MINLP) problems, which are NP-hard and can be widely found in the area of wireless resource allocation. The main idea of GBD is decomposing an MINLP problem into a primal problem and a master problem, which are iteratively solved until their solutions converge. However, a direct implementation of GBD is time- and memory-consuming. The main bottleneck is the high complexity of the master problem, which increases over the iterations. Therefore, we propose to leverage machine learning (ML) techniques to accelerate GBD aiming at decreasing the complexity of the master problem. Specifically, we utilize two different ML techniques, classification and regression, to deal with this acceleration task. In this way, a cut classifier and a cut regressor are learned, respectively, to distinguish between useful and useless cuts. Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced. By using a resource allocation problem in device-to-device communication networks as an example, we validate that the proposed method can reduce the computational complexity of GBD without loss of optimality and has good generalization ability. The proposed method is applicable for solving various MINLP problems in wireless networks since the designs are invariant for different problems.

关键词： Resource management Acceleration Wireless networks Complexity theory programming Training Machine learning generalized Benders decomposition device-to-device communications resource allocation mixed integer nonlinear programming

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A new efficient GA-Benders' decomposition method: For power generation expansion planning with emission controls

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IEEE TRANSACTIONS ON POWER SYSTEMS 2007年第3期22卷 1092-1100页

作者： Sirikum, Jiraporn Techanitisawad, Anulark Kachitvichyanukul, Voratas Elect Generating Author Thailand Syst Planning Div Bang Krui 11130 Nonthaburi Thailand Asian Inst Technol Sch Engn Technol Ind Syst Engn Program Ind Engn & Management Dept Klongluang 12120 Pathumthani Thailand

The power generation expansion planning (PGEP) problem is a large-scale mixed integer nonlinear programming (MINLP) problem cited as one of the most complex optimization problems. In this paper, an application of a new efficient methodology for solving the power generation expansion planning problem is presented, A comprehensive planning production simulation model is introduced toward formulating into an MINLP model. The model evaluates the most economical investment planning for additional thermal power generating units of the optimal mix for long-term power generation expansion planning with emission controls, regarding to the incorporated environmental costs, subject to the integrated requirements of powers demands, power capacities, loss of load probability (LOLP) levels, locations, and environmental limitations for emission controls. A. GA-heuristic-based method called GA-Benders' decomposition (GA-BD) is proposed for solving this complex problem. Finally, an application of the proposed GA-BD method is discussed and concluded.

关键词： Benders' decomposition emission modeling GA-Benders' decomposition genetic algorithms mixed integer nonlinear programming power generation expansion planning

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Optimal processing route for the utilization and conversion of municipal solid waste into energy and valuable products

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JOURNAL OF CLEANER PRODUCTION 2018年 174卷 857-867页

作者： Rizwan, Muhammad Saif, Yousef Almansoori, Ali Elkamel, Ali Khalifa Univ Sci & Technol Dept Chem Engn Petr Inst POB 2533 Abu Dhabi U Arab Emirates Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada

A systematic design of municipal solid waste (MSW) management system can lead to identify a promising and/or sustainable way of handling MSW by processing it into energy and valuable products. In this study, a systematic framework is developed for the superstructure-based optimization of MSW processing routes. The proposed superstructure includes the potential technological alternatives (such as recycling, composting, anaerobic digestion with electricity generation, gasification followed by catalytic transformation, gasification with electricity generation, plasma arc gasification with electricity generation, pyrolysis with electricity generation, incineration with electricity generation, and landfill with electricity generation) for producing valuable products from MSW. Based on the developed superstructure, a mixed integer nonlinear programming (MINLP) model is developed to identify the optimal MSW processing pathways considering two different MSW handling scenarios. For ease of the solution, the MINLP model is linearized to its equivalent MILP form, and solved in GAMS. The solution to the optimization problem provides the optimal/promising route for the synthesis of useful products from MSW under chosen economic objective function. The developed framework is applied on a case study of Abu Dhabi Emirate to find the optimal processing pathway for handling and processing of MSW into energy and value-added products. The optimization results show that an integrated pathway comprising of recycling the recyclable components of MSW along with the production of bioethanol from the rest of the waste via gasification followed by catalytic transformation can provide potential economic benefits. A sensitivity analysis is also executed to investigate the effect of key economic and technical parameters on the optimization results. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Municipal solid waste Superstructure-based optimization Sustainable management Waste-to-energy mixed integer nonlinear programming

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