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  • Open Access

    ARTICLE

    Two-Stage Scheduling Model for Flexible Resources in Active Distribution Networks Based on Probabilistic Risk Perception

    Yukai Li1,*, Ruixue Zhang1, Yongfeng Ni1, Hongkai Qiu1, Yuning Zhang2, Chunming Liu2

    Energy Engineering, Vol.122, No.2, pp. 681-707, 2025, DOI:10.32604/ee.2024.058981 - 31 January 2025

    Abstract Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network (ADN) and the difficulty of security assessment of distribution network, this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception. First, a full-cycle probabilistic trend sequence is constructed based on the source-load historical data, and in the day-ahead scheduling phase, the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend, with the probability of the security boundary as the security constraint, and with… More >

  • Open Access

    ARTICLE

    An Iterated Greedy Algorithm with Memory and Learning Mechanisms for the Distributed Permutation Flow Shop Scheduling Problem

    Binhui Wang, Hongfeng Wang*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 371-388, 2025, DOI:10.32604/cmc.2024.058885 - 03 January 2025

    Abstract The distributed permutation flow shop scheduling problem (DPFSP) has received increasing attention in recent years. The iterated greedy algorithm (IGA) serves as a powerful optimizer for addressing such a problem because of its straightforward, single-solution evolution framework. However, a potential draw-back of IGA is the lack of utilization of historical information, which could lead to an imbalance between exploration and exploitation, especially in large-scale DPFSPs. As a consequence, this paper develops an IGA with memory and learning mechanisms (MLIGA) to efficiently solve the DPFSP targeted at the mini-mal makespan. In MLIGA, we incorporate a memory… More >

  • Open Access

    ARTICLE

    Federated Learning’s Role in Next-Gen TV Ad Optimization

    Gabriela Dobrița, Simona-Vasilica Oprea*, Adela Bâra

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 675-712, 2025, DOI:10.32604/cmc.2024.058656 - 03 January 2025

    Abstract In the rapidly evolving landscape of television advertising, optimizing ad schedules to maximize viewer engagement and revenue has become significant. Traditional methods often operate in silos, limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing. This article introduces a novel approach that leverages Federated Learning (FL) to enhance TV ad schedule optimization, combining the strengths of local optimization techniques with the power of global Machine Learning (ML) models to uncover actionable insights without compromising data privacy. It combines linear programming for initial ads scheduling optimization with ML—specifically,… More >

  • Open Access

    ARTICLE

    A Latency-Aware and Fault-Tolerant Framework for Resource Scheduling and Data Management in Fog-Enabled Smart City Transportation Systems

    Ibrar Afzal1, Noor ul Amin1,*, Zulfiqar Ahmad1,*, Abdulmohsen Algarni2

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1377-1399, 2025, DOI:10.32604/cmc.2024.057755 - 03 January 2025

    Abstract The deployment of the Internet of Things (IoT) with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses, smart cities, and smart transportation systems. Fog computing tackles a range of challenges, including processing, storage, bandwidth, latency, and reliability, by locally distributing secure information through end nodes. Consisting of endpoints, fog nodes, and back-end cloud infrastructure, it provides advanced capabilities beyond traditional cloud computing. In smart environments, particularly within smart city transportation systems, the abundance of devices and nodes poses significant challenges related… More >

  • Open Access

    ARTICLE

    Offload Strategy for Edge Computing in Satellite Networks Based on Software Defined Network

    Zhiguo Liu1,#, Yuqing Gui1,#, Lin Wang2,*, Yingru Jiang1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 863-879, 2025, DOI:10.32604/cmc.2024.057353 - 03 January 2025

    Abstract Satellite edge computing has garnered significant attention from researchers; however, processing a large volume of tasks within multi-node satellite networks still poses considerable challenges. The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers, making it necessary to implement effective task offloading scheduling to enhance user experience. In this paper, we propose a priority-based task scheduling strategy based on a Software-Defined Network (SDN) framework for satellite-terrestrial integrated networks, which clarifies the execution order of tasks based on their priority. Subsequently, we More >

  • Open Access

    ARTICLE

    Bilevel Optimal Scheduling of Island Integrated Energy System Considering Multifactor Pricing

    Xin Zhang*, Mingming Yao, Daiwen He, Jihong Zhang, Peihong Yang, Xiaoming Zhang

    Energy Engineering, Vol.122, No.1, pp. 349-378, 2025, DOI:10.32604/ee.2024.057676 - 27 December 2024

    Abstract In this paper, a bilevel optimization model of an integrated energy operator (IEO)–load aggregator (LA) is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system (IIES). The upper level represents the integrated energy operator, and the lower level is the electricity-heat-gas load aggregator. Owing to the benefit conflict between the upper and lower levels of the IIES, a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed, combined with factors such as the carbon emissions of the IIES, as well as the lower load… More > Graphic Abstract

    Bilevel Optimal Scheduling of Island Integrated Energy System Considering Multifactor Pricing

  • Open Access

    ARTICLE

    Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

    Suliang Ma1, Zeqing Meng1, Mingxuan Chen2,*, Yuan Jiang3

    Energy Engineering, Vol.122, No.1, pp. 63-84, 2025, DOI:10.32604/ee.2024.057216 - 27 December 2024

    Abstract In the independent electro-hydrogen system (IEHS) with hybrid energy storage (HESS), achieving optimal scheduling is crucial. Still, it presents a challenge due to the significant deviations in values of multiple optimization objective functions caused by their physical dimensions. These deviations seriously affect the scheduling process. A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values. The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods. The proposed method More > Graphic Abstract

    Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

  • Open Access

    ARTICLE

    A Multi-Objective Clustered Input Oriented Salp Swarm Algorithm in Cloud Computing

    Juliet A. Murali1,*, Brindha T.2

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4659-4690, 2024, DOI:10.32604/cmc.2024.058115 - 19 December 2024

    Abstract Infrastructure as a Service (IaaS) in cloud computing enables flexible resource distribution over the Internet, but achieving optimal scheduling remains a challenge. Effective resource allocation in cloud-based environments, particularly within the IaaS model, poses persistent challenges. Existing methods often struggle with slow optimization, imbalanced workload distribution, and inefficient use of available assets. These limitations result in longer processing times, increased operational expenses, and inadequate resource deployment, particularly under fluctuating demands. To overcome these issues, a novel Clustered Input-Oriented Salp Swarm Algorithm (CIOSSA) is introduced. This approach combines two distinct strategies: Task Splitting Agglomerative Clustering (TSAC)… More >

  • Open Access

    ARTICLE

    Performance-Oriented Layout Synthesis for Quantum Computing

    Chi-Chou Kao1,*, Hung-Yi Lin2

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1581-1594, 2024, DOI:10.32604/csse.2024.055073 - 22 November 2024

    Abstract Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits (qubits) can only interact effectively with their nearest neighbors. This constraint severely impacts the design and efficiency of quantum algorithms, as arranging qubits optimally can significantly reduce circuit depth and improve computational performance. To tackle the layout synthesis challenge, we propose an algorithm based on integer linear programming (ILP). ILP is well-suited for this problem as it can formulate the optimization objective of minimizing circuit depth while adhering to the nearest neighbor interaction constraint. The algorithm aims… More >

  • Open Access

    ARTICLE

    Three-Level Optimal Scheduling and Power Allocation Strategy for Power System Containing Wind-Storage Combined Unit

    Jingjing Bai1, Yunpeng Cheng1, Shenyun Yao2,*, Fan Wu1, Cheng Chen1

    Energy Engineering, Vol.121, No.11, pp. 3381-3400, 2024, DOI:10.32604/ee.2024.053683 - 21 October 2024

    Abstract To mitigate the impact of wind power volatility on power system scheduling, this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy. And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit. The strategy takes smoothing power output as the main objectives. The first level is the wind-storage joint scheduling, and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster (WPC), respectively, according to the scheduling power of WPC and… More >

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