Scheduling In Networks Research Articles

DOFMS: DRL-Based Out-of-Order Friendly Multipath Scheduling in Mobile Heterogeneous Networks

DOFMS: DRL-Based Out-of-Order Friendly Multipath Scheduling in Mobile Heterogeneous Networks

Optimal Transmission Scheduling for Two-Hop Networks

Optimal Transmission Scheduling for Two-Hop Networks

Joint Beam Allocation and Scheduling for mmWave Cellular Networks

Joint Beam Allocation and Scheduling for mmWave Cellular Networks

Stochastic scheduling of distribution power networks counting advanced adiabatic compressed air energy storage

Stochastic scheduling of distribution power networks counting advanced adiabatic compressed air energy storage

A novel risk-averse optimal scheduling strategy for active distribution networks equipped with power-to-X technologies

This paper proposes a novel risk-averse approach for scheduling of an active electrical distribution network (AEDN). The network is connected to an electric vehicle parking lot (PL) and gas and hydrogen fueling stations (GHFSs). In addition, the AEDN operator is able to participate in both day-ahead and real-time markets to supply energy demands. In order to address the uncertainties of the studied network, a two-stage stochastic programming incorporating conditional value-at-risk (CVaR) is employed to model uncertainties of wind turbine and photovoltaic output, electric vehicles behavior in PL, and GHFSs energy consumption, while a robust optimization method is used to handle the uncertainty of real-time market price. According to the numerical results, the AEDN operator can decrease the risk level by up to 1.2% while the operational cost rises by 7.5%. Furthermore, the optimal use of multiple energy storage technologies and price-sensitive shiftable electric loads (PSELs) reduces the operational cost by 7.4%.

Two-stage optimization model for scheduling multiproduct pipeline network with multi-source and multi-terminal

The multiproduct pipeline network with multi-source and multi-terminal encompasses multi-point injection and multi-point distribution. Upon reorganization of batches at transfer depots, the intricacies of precise batch tracking become increasingly complex, thereby rendering the optimization of multiproduct pipeline network scheduling particularly arduous. In this paper, we introduce a dynamic index batch numbering method. By delineating the boundaries of intermediate injection points, the issue is bifurcated into two sub-problems: the optimization of batch planning for multiproduct pipeline with single-source and multi-terminal and the optimization of resource allocation at intermediate oil depots. The proposed algorithm is validated on two genuine multiproduct pipeline networks in China, demonstrating its capability to generate feasible solutions within a computational constraint of 18,000 CPU seconds. The findings reveal that this methodology holistically considers the entire pipeline network system, encompassing upstream oil intake, downstream oil supply, and the tank capacities of transfer depots, thereby establishing a coherent link between pipeline scheduling and depot planning. This paper may serve as a reference for the automated formulation of multiproduct pipeline network scheduling strategies.

Two-Stage Optimal Scheduling for Urban Snow-Shaped Distribution Network Based on Coordination of Source-Network-Load-Storage

With the widespread integration of distributed resources, optimizing the operation of urban distribution networks faces challenges including uneven source-load-storage distribution, fluctuating feeder power flows, load imbalances, and network congestion. The urban snow-shaped distribution network (SDN), characterized by numerous intra-station and inter-station tie switches, serves as a robust framework to intelligently address these issues. This study focuses on enhancing the safe and efficient operation of SDNs through a two-phase optimal scheduling model that coordinates source-network-load-storage. In the day-ahead scheduling phase, an optimization model is formulated to minimize operational costs and mitigate load imbalances. This model integrates network reconfiguration, energy storage systems (ESSs), and flexible load (FL). During intra-day scheduling, a rolling optimization model based on model predictive control adjusts operations using the day-ahead plan to minimize the costs and penalties associated with power adjustments. It provides precise control over ESS and FL outputs, promptly correcting deviations caused by prediction errors. Finally, the proposed model is verified by an actual example of a snow-shaped distribution network in Tianjin. The results indicate significant improvements in leveraging coordinated interactions among source-network-load-storage, effectively reducing spatial-temporal load imbalances within feeder clusters and minimizing the impact of prediction inaccuracies.

Considering the Optimization Design of Urban Bus Network Scheduling

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%.

Optimizing Traffic Scheduling in Autonomous Vehicle Networks Using Machine Learning Techniques and Time-Sensitive Networking

Optimizing Traffic Scheduling in Autonomous Vehicle Networks Using Machine Learning Techniques and Time-Sensitive Networking

An incremental learning approach to dynamic parallel machine scheduling with sequence-dependent setups and machine eligibility restrictions

Minimizing the tardiness of a parallel machine scheduling problem has been actively studied in modern manufacturing systems. In particular, dynamic parallel machine scheduling problems (DPMSPs) have gained much attention since rescheduling is required to address unpredictable events such as unexpected job arrivals and machine breakdowns, which usually occur in real-world manufacturing systems. To deal with such unpredictable events, many researchers have employed deep supervised learning (SL). However, it is still challenging to solve a DPMSP since SL requires a large number of high-quality schedules for the training neural networks. In this paper, we propose an incremental learning-based scheduling method (ILS) in which neural networks (NNs) are periodically trained by utilizing schedules built from the updated NNs in previous training intervals. Furthermore, to perform training without reducing solution space within a dynamic environment, the proposed method is designed to consider the dependency between consecutive allocations on a machine for the sum of setup time and tardiness. To verify the effectiveness of the proposed method, extensive experiments are carried out in static and dynamic scheduling problems. The experiment results demonstrate that ILS outperforms the existing methods such as dispatching rules, metaheuristics, and DRL-based methods. Additionally, we show that the proposed input features are effective in appropriately selecting job-machine pairs for assigning jobs on eligible machines through deep Shapley additive explanations analysis.

Two-Stage Carbon-Oriented Scheduling of an Active Distribution Network With Thermostatically Controlled Load Aggregators

Two-Stage Carbon-Oriented Scheduling of an Active Distribution Network With Thermostatically Controlled Load Aggregators

Development of Technology for Cleaning Vehicle Radiators

The most common and serious malfunction is engine overheating due to radiator clogging. This can be caused by improper vehicle operation or insufficient radiator cleaning during technical maintenance. The process of cleaning the tubes and internal cavities of the radiator is the most labor-intensive and involves the risk of damage, rendering it unusable. The article presents a technology developed by the authors for restoring the functionality of internal combustion engine cooling system radiators, based on a new method of cleaning the tubes and internal cavities of the radiator using ultrasound. The aim of the research is to develop a maintenance algorithm for engine cooling system radiators, determining the complexity and interdependence of tasks. Based on the analysis of the technological process of radiator maintenance, the authors have developed a list and sequence of tasks, as well as a network schedule for ultrasound cleaning of the vehicle's cooling system radiator.

Characterization and application of flexible operation region of virtual power plant

The construction of a virtual power plant (VPP) represents a powerful approach for harnessing the adjustable capacity of flexible resources. VPP actively participates in transmission network dispatching by aggregating these flexible resources. However, existing practical implementations and research cannot describe the influence of renewable energy's inherent unpredictability on the dispatching boundaries of VPPs. This shortfall leads to a lack of organic coordination between flexible resources and the power transmission network. Therefore, this paper suggests a way to characterize the dispatching boundaries of VPPs based on the concept of flexible operation region (FOR), aiming to better incorporate the flexible resources of decentralized access and lessen the issue of insufficient computing power caused by unified optimization. Initially, an analytical model for the VPP's FOR is presented, followed by dimensionality reduction of the FOR to the VPP's gateway. Subsequently, participation in transmission network scheduling is facilitated using an equivalent dimensionality reduction model. Based on standard example testing, the proposed method's effectiveness and accuracy have been verified in this paper.

Morpheus: An efficient timing-based attestation framework for safeguarding hypervisor integrity with dynamic trust

Hypervisor, the core software in cloud computing, is susceptible to malicious intrusions, potentially jeopardizing overall platform security. Physical hosts within the cloud computing environment constantly face persistent attacks, and not all hosts are equipped with essential security hardware. The prolonged latency of previous software-based detection methods could not comprehensively address these cloud threats. Furthermore, the widespread deployment of security hardware incurs substantial costs for hardware-based detection. To address these challenges, we introduce Morpheus, an efficient framework ensuring hypervisor integrity in the current cloud. This framework rapidly employs software-based methods to detect malicious hosts, utilizing a subset of hosts equipped with security hardware as the Root of Trust. Efficiency is augmented through a Neural Network scheduling module, and an embedded exponential aging mechanism fortifies time-aging trust against consistent cloud threats. Evaluation results demonstrate that it can promptly identify threatened hosts with acceptable system overhead loss, solidifying its position as a robust cloud security solution.

Graph reinforcement learning for flexible job shop scheduling under industrial demand response: A production and energy nexus perspective

Amidst the global energy crisis, the industrial sector is facing unparalleled energy conservation challenges as a primary energy consumer. Industrial demand response (IDR) is a promising strategy to enhance the energy efficiency of manufacturing enterprises. This paper introduces a graph reinforcement learning (GRL)-based method for flexible job shop scheduling under IDR from a production and energy nexus perspective. The scheduling problem is initially formulated as a Markov Decision Process (MDP). On this basis, the scheduling state is represented by a unique heterogeneous disjunctive graph incorporating IDR features, and the reward is constructed through a tailored generalized electricity consumption index (GECI). Moreover, a mixed graph neural network scheduler is designed to tackle this MDP, which integrates the heterogeneous attention mechanism and adaptive greedy sampling strategy. Furthermore, a proximal policy optimization algorithm is employed for training to obtain optimal scheduling schemes. Empirical case studies indicate that the proposed method can reduce GECI by up to 14.44% and 2.22%, respectively, outperforming existing well-known dispatching rules and another scheduling method based on GRL.

Stochastic two-stage resilient scheduling of hydrogen system in power distribution grids with high penetration of solar PV systems: A convex worst case analysis

The renewable distribution network holds tremendous potential for economic advantages and sustainability improvements within the power industry. However, resilient operations in post-disaster scenarios present significant challenges for the system. Operational risks arise from uncertain resources, making day-ahead scheduling complex for the distribution grid's operator. Therefore, real-time modeling is considered more suitable to effectively manage these uncertainties. The hydrogen system (HSYS) is also an emerging technology that improves the technical and operational characteristics of distribution networks, particularly in challenging and unpredictable situations. The study presents a comprehensive framework for evaluating HSYSs within a photovoltaic-focused power distribution network, focusing on day-ahead resiliency-oriented operations (RESOP) while considering risk factors associated with uncertain incidents. The effectiveness and feasibility of implementing the proposed HSYS in day-ahead risk-based resilient scheduling of the distribution network are analyzed using a specific distribution system. The model is formulated as a MILP problem, and the impacting uncertainties are modeled using stochastic programming. The primary risk associated with uncertainties is captured using the conditional value-at-risk (CVaR) approach, which involves the possible worst realizations of uncertainties in the model. The study's findings demonstrate that the integration of distributed HSYSs leads to significant cost savings of up to 16 % under normal conditions and 1.25 % when considering risk modeling.

Bi-Level Optimal Scheduling of Distribution Networks with the Photovoltaic Prosumer Groups Based on Contract Theory

Bi-Level Optimal Scheduling of Distribution Networks with the Photovoltaic Prosumer Groups Based on Contract Theory

Recovery Scheduling of Road Networks Considering Day-to-Day Flow Evolution

Natural disasters can lead to substantial disruptions in road networks, making many critical links unusable. It is important to timely repair the damaged links as they allow transportation of emergency services, relief materials, and so forth, after disasters. Many existing studies that focus on optimal recovery of damaged links after disasters assume that only a single agency is available for repair. Moreover, most of the existing studies do not consider travel time on links to be a function of traffic flow passing through the links and assume that the traffic flow gets distributed based on user equilibrium each time a link is repaired. However, such a traffic distribution is unrealistic as it assumes that the traffic flow remains the same across all the days for which a link is repaired and the traffic distribution gets suddenly modified whenever a link is fully repaired. The goal of this paper is to address these gaps in the literature of disaster recovery. We study the problem of determining the optimal repair scheduling of damaged links to minimize the sum of the total system travel time over the repair duration given that multiple repair agencies are available for recovery. Also, we consider a day-to-day traffic flow evolution where the route choices of travelers depend on the travel conditions of the previous day. We formulate this problem as a mixed-integer non-linear program. We proposed two solution methodologies to solve the problem: a genetic algorithm and a greedy algorithm. We tested these methodologies under different settings.

A Carbon-Tax-Based Pricing Scheme for Vehicle Scheduling in Coupled Power-Traffic Networks

A Carbon-Tax-Based Pricing Scheme for Vehicle Scheduling in Coupled Power-Traffic Networks

Benders Decomposition-based Coordinated Optimization Method for Medium and Low Voltage Distribution Network with Photovoltaics Access

aims: From the perspective of improving the economic benefit of the distribution network and ensuring the safe operation of the distribution network, the medium and low voltage optimization model with the goal of reducing the operation cost is established. background: Double carbon background and new energy power system policy, a large number of distributed photovoltaic access distribution network caused a great influence, not only on the low voltage distribution network user side power quality impact, photovoltaic full hair also can happen when tide back phenomenon, for the stable operation of the medium voltage distribution network and scheduling, therefore, this paper puts forward a scheduling of the voltage distribution network and low voltage distribution network optimization method, to solve the problems of large-scale distributed photovoltaic access. objective: Reducing operating costs and ensuring the stable and safe operation of the distributed network. method: The optimization problem of medium and low voltage distribution network is decomposed into the main optimization problems of medium voltage distribution network and the sub-problems of optimizing the low-voltage distribution network. An optimal solution is obtained by coordinating the boundary coupling variables between the two problems and solving them iteratively. Finally, the effectiveness of the coordination algorithm is verified by simulating the medium and low voltage distribution network. result: A coordinated optimization method for medium and low voltage distribution network with photovoltaics access. conclusion: It reduces the operation cost, ensures the stable and safe operation of the power grid, thus improving the economic benefits. other: None