To alleviate the tense situation of limited passenger service resources in the terminal and to achieve the matching of resource scheduling with the flight support process, the process–resource interdependent network is constructed according to...
Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable eve...
In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on the...
Many adaptive gradient methods have been successfully applied to train deep neural networks, such as Adagrad, Adadelta, RMSprop and Adam. These methods perform local optimization with an element-wise scaling learning rate based on past gradients. Alt...
Distributed scheduling has become a common manufacturing mode, and the distributed job scheduling problem (DJSP) has attracted more manufacturers and researchers in the field of operation research. For the distributed scheduling problem, it emphasize...
Task scheduling algorithms based on reinforce learning (RL) have been important methods with which to improve the performance of cloud platforms; however, due to the dynamics and complexity of the cloud environment, the action space has a very high d...
This study introduces a novel tracking control strategy tailored to aeroengines, which are highly nonlinear and characterized by significant uncertainty. The proposed method entails a robust extended Kalman filter (REKF) enhanced by a forgetting fact...
To address the scheduling problem of dynamic flexible job shop, this study proposes a hybrid scheduling method that integrates an adaptive genetic algorithm, dynamic target smoothing, and a deep Q-network (DQN). The scheduling process is formulated a...
For workflow scheduling with complex dependencies in cloud computing environments, existing research predominantly focuses on multi-objective algorithm optimization while neglecting the critical factor of workflow topological structure. The proposed...
In a Kubernetes environment, the resource allocation for Pods has a direct impact on both performance and cost. When resource sizes are determined based on user experience, under-provisioning can lead to performance degradation and execution instabil...
The intricacies of air traffic departure scheduling, especially when numerous flights are delayed, frequently impede the implementation of automated decision-making for scheduling. To surmount this obstacle, a mathematical model is proposed, and a dy...
Task scheduling in data relay satellite networks (DRSNs) is subject to dynamic disruptions such as resource failures, sudden surges in task demands, and variations in service duration requirements. These disturbances may degrade the performance of pr...
With the rapid development of intelligent driving technology, in-vehicle bus networks face increasingly stringent requirements for real-time performance and data transmission. Traditional bus network technologies such as LIN, CAN, and FlexRay are sho...
Container technology has gained a widespread application in cloud computing environments due to its low resource overhead and high flexibility. However, as the number of containers grows, it becomes increasingly challenging to achieve the rapid and c...
The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a mul...
Integrated process planning and scheduling (IPPS) is an intricate and vital issue in smart manufacturing, requiring the coordinated optimization of both process plans and production schedules under multiple resource and precedence constraints. This p...
This paper presents a new metaheuristic approach based on a self-adaptive genetic algorithm (SAGA) for solving the short-term hydro-thermal-solar scheduling with combined-cycle (CCGT) units. First of all, the proposed approach is applied to a test sy...
The effective production scheduling of dry bulk ports is a challenging task that demands meticulous planning, task allocation based on customer requirements, as well as strategic route and timing scheduling. Dry bulk ports dedicated to handling commo...
The development of industrial-enabling technology, such as the industrial Internet of Things and physical network system, makes it possible to use real-time information in production-logistics scheduling. Real-time information in an intelligent facto...
Efficient scheduling of agricultural machinery is critical for optimizing resource utilization and reducing operational costs in modern farming operations. This study proposes an Adaptive Genetic Algorithm integrated with Ant Colony Optimization (AGA...
To address the conflict between real-time performance and optimal resource allocation in large-scale digital array radars, this paper proposes a novel resource scheduling framework that integrates graph-theoretic modeling with an adaptive heuristic s...
In industries such as aircraft or train manufacturing, large-scale manufacturing companies often manage several complex projects. Each of these projects includes multiple tasks that share a set of limited resources. Typically, these tasks are also su...
Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resour...
Maximum power point tracking (MPPT) controllers have already achieved remarkable efficiencies. For smaller photovoltaic (PV) systems, any improvement will not really be worth mentioning as an achievement. However, for large solar farms, even a fracti...
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This research aimed to present a solution to the problem of production scheduling and assignment in broiler farms, which thus enabled the farms to achieve maximum profit. In the operation of farms, there are many factors that affect profits, such as...
The interdependency of electric and natural gas systems is becoming stronger. The challenge of how to meet various energy demands in an integrated energy system (IES) with minimal cost has drawn considerable attention. The optimal scheduling of IESs...
Cognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive and ubiquitous services. These sensors frequently create massive amounts of data with many redundan...
In today’s customer-centric economy, the demand for personalized products has compelled corporations to develop manufacturing processes that are more flexible, efficient, and cost-effective. Flexible job shops offer organizations the agility an...
This study presents a novel LSTM–CNN-based adaptive scheduling framework (LSTM-CNN–AS) designed to improve real-time energy management and extend the lifespan of lithium-ion Battery Energy Storage Systems (BESS) in rural and resource-cons...
Environmental concerns and rising energy prices put great pressure on the manufacturing industry to reduce pollution and save energy. Electricity is one of the main machinery energy sources in a plant; thus, reducing energy consumption both saves ene...
Green data centers (GDCs) are increasingly deployed worldwide to power digital infrastructure sustainably. These centers integrate renewable energy sources, such as solar and wind, to reduce dependence on grid electricity and lower operational costs....
Owing to the different quantities and processing times of sub-lots, intermingling sub-lots with each other, instead of fixing the production sequence of sub-lots of a lot as in the existing studies, is a more practical approach to lot-streaming flow...
Environmental and economic considerations create a challenge for manufacturers. The main priorities for production planning in environmentally friendly manufacturing industries are reducing energy consumption and improving productivity by balancing m...
Unbalance vibration is one of the main vibration forms of a high speed machine tool spindle. The overlarge unbalance vibration will have some adverse effects on the working life of the spindle system and the surface quality of the work-piece. In orde...
We investigate the robustness of adaptive chemotherapy schedules over repeated cycles and a wide range of tumor sizes. Using a non-stationary stochastic three-component fitness-dependent Moran process model (to track frequencies), we quantify the var...
With the increasing demand for decentralized systems and the widespread usage of blockchain, low throughput and high latency have become the biggest stumbling blocks in the development of blockchain systems. This problem seriously hinders the expansi...
In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufa...
The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. To solve this problem, this paper proposes a digital twin-driven shop floor adaptive scheduling method. Firstly, a digi...
There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in...
According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the k...
Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, ba...
Using unmanned aerial vehicles (UAVs) is an effective way to gather data from Internet of Things (IoT) devices. To reduce data gathering time and redundancy, thereby enabling the timely response of state-of-the-art systems, one can partition a networ...
Computation Offloading (CO) can accelerate application running through parallel processing. This paper proposes a method called vehicular adaptive offloading (VAO) in which vehicles in vehicular ad-hoc networks (VANETs) offload computationally intens...
Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-he...
This paper presents a novel metascheduling algorithm to enhance communication efficiency in off-chip time-triggered multi-processor system-on-chip (MPSoC) platforms, particularly for safety-critical applications in aerospace and automotive domains. T...
Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to schedule for a maintenance intervention before a possible breakdown; however, much less focus has been devoted to the use of such...
Joint radar–communications (JRC) waveform could enable simultaneously radar sensing and communication functions in a multifunction integrated system (MFIS). Because of the special JRC waveform characteristics of MFIS, the traditional task sched...
Conventional project scheduling techniques often rely on manual trial-and-error methods, which can lead to inaccurate evaluations. This study presents a dynamic scheduling framework to dynamically adjust scheduling decisions based on real-time produc...
Hierarchical federated learning (HFL) has emerged as a promising paradigm for distributed machine learning over vehicular networks. Despite recent advances in vehicle selection and resource allocation, most still adopt a fixed Edge-and-Vehicle Schedu...
Domain-specific systems on chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, whereas hardware accelerators tailored to the target domain minimize task executi...
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