Scheduling (production processes) ≈ Scheduling (production processes)
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MapReduce: Simplified Data Processing on Large Cluster Open
- MapReduce is a data processing approach, where a single machine acts as a master, assigning map/reduce tasks to all the other machines attached in the cluster. Technically, it could be considered as a programming model, which is applied …
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Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † Open
Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing…
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Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications Open
This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualiz…
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Delay-optimal computation task scheduling for mobile-edge computing systems Open
Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a chall…
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A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems Open
This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routin…
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Learning scheduling algorithms for data processing clusters Open
Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems use simple, generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling polic…
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On-Line Building Energy Optimization Using Deep Reinforcement Learning Open
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a…
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Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks Open
In this paper, we consider a wireless broadcast network with a base station sending time-sensitive information to a number of clients through unreliable channels. The Age of Information (AoI), namely the amount of time that elapsed since t…
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Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions Open
Emerging technologies such as the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big…
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Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda Open
Autonomous mobile robots (AMR) are currently being introduced in many intralogistics operations, like manufacturing, warehousing, cross-docks, terminals, and hospitals. Their advanced hardware and control software allow autonomous operatio…
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Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems With an Electric Vehicle Charging Station: A Bi-Level Approach Open
A community integrated energy system (CIES) with an electric vehicle charging\nstation (EVCS) provides a new way for tackling growing concerns of energy\nefficiency and environmental pollution, it is a critical task to coordinate\nflexible…
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Economic-based distributed resource management and scheduling for grid computing Open
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Efficient Energy Management for the Internet of Things in Smart Cities Open
The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and…
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Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning Open
Driven by the recent advances in electric vehicle (EV) technologies, EVs have become important for smart grid economy. When EVs participate in demand response program which has real-time pricing signals, the charging cost can be greatly re…
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Machine learning in manufacturing and industry 4.0 applications Open
The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories t…
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Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching Open
This two-part paper develops novel methodologies for using fractional\nprogramming (FP) techniques to design and optimize communication systems. Part\nI of this paper proposes a new quadratic transform for FP and treats its\napplication fo…
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Sub-Channel Assignment, Power Allocation, and User Scheduling for Non-Orthogonal Multiple Access Networks Open
In this paper, we study the resource allocation and user scheduling problem for a downlink nonorthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize th…
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Energy storage technologies: An integrated survey of developments, global economical/environmental effects, optimal scheduling model, and sustainable adaption policies Open
Energy Storage Technology is one of the major components of renewable energy integration and decarbonization of world energy systems. It significantly benefits addressing ancillary power services, power quality stability, and power supply …
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Robust Co-Optimization Scheduling of Electricity and Natural Gas Systems via ADMM Open
En el planeamiento de los sistemas energéticos existen parámetros externos sujetos a variaciones que afectan las decisiones que se deben tomar para su operación, como son los afluentes que alimentan a cada embalse de las centrales hidroelé…
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Deep Reinforcement Learning for Smart Home Energy Management Open
In this paper, we investigate an energy cost minimization problem for a smart\nhome in the absence of a building thermal dynamics model with the consideration\nof a comfortable temperature range. Due to the existence of model uncertainty,\…
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Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers Open
The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts …
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A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management Open
This paper proposes a novel framework for home energy management (HEM) based on reinforcement learning in achieving efficient home-based demand response (DR). The concerned hour-ahead energy consumption scheduling problem is duly formulate…
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Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning Open
Electric vehicles (EVs) have been popularly adopted and deployed over the past few years because they are environment-friendly. When integrated into smart grids, EVs can operate as flexible loads or energy storage devices to participate in…
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A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network Open
Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurat…
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Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing Open
Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constra…
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From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry Open
The widespread adoption of Building Information Modeling (BIM) and the recent emergence of Internet of Things (IoT) applications offer several new insights and decision-making capabilities throughout the life cycle of the built environment…
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Transportable Energy Storage for More Resilient Distribution Systems With Multiple Microgrids Open
Transportable energy storage systems (TESSs) have great potential to enhance resilience of distribution systems (DSs) against large area blackouts. A joint post-disaster restoration scheme for TESS and generation scheduling in microgrids (…
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LNSC: A Security Model for Electric Vehicle and Charging Pile Management Based on Blockchain Ecosystem Open
The Internet of Energy (IoE) provides an effective networking technology for distributed green energy, which allows the connection of energy anywhere at any time. As an important part of the IoE, electric vehicles (EVs), and charging pile …
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Day-Ahead Self-Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets Under Uncertainty Open
This paper proposes a novel model for the day-ahead self-scheduling problem of a virtual power plant trading in both energy and reserve electricity markets. The virtual power plant comprises a conventional power plant, an energy storage fa…
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A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders Open
Time-series forecasting is applied to many areas of smart factories, including machine health monitoring, predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust pro…