Resource Scheduling Model Research Articles

Coordinated residential energy resource scheduling with human thermal comfort modelling and renewable uncertainties

With development of two-way communication technology, residential users are able to reshape their energy consumption patterns based on demand response signals. This study proposes an optimal residential energy resource scheduling model to minimise the home electricity cost while fully considering the user's life convenience, the user's thermal comfort, and renewable uncertainties. The proposed model accounts for the characteristics of shiftable appliance, air-conditioning system, electric vehicle's charging pattern, and renewable generation of both wind and solar power. Wasserstein distance metric and K-medoids-based scenario generation and reduction techniques are used to address the renewable uncertainty. An adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. A waiting cost model is applied to measure the user's preference on the household appliance's operation. In addition, a recently proposed metaheuristic optimisation algorithm (the natural aggregation algorithm) is used to solve the proposed model. The simulation results show the proposed model is effective in minimising the household's daily electricity bill while preserving the user's comfort level.

Software-defined railway wireless communication network

Railway wireless data communications have problems with insufficient bandwidth and poor reliability. This paper describes a railway software-defined wireless communication network (RailSDN) which separates the control plane from the data plane, enables heterogeneous network interoperability among operators, provides user logical isolation using virtualization, and provides transparent data transmission for users. A network resource scheduling model is developed based on a Markov decision process which takes into account various factors such as the infrastructure network status and the user requirements. The system searches for the optimal link allocation with the optimization goal being to satisfy user demand and dynamically adapts to the user requirements for the network resources. Tests show that this railway wireless communication network is scalable, enables flexible customization of user demand, and improves the network resources utilization efficiency. 摘要 针对铁路无线数据通信带宽不足、可靠性不高等问题, 提出一种基于软件定义的铁路无线通信网络 (RailSDN), 将控制平面和数据平面分离, 实现跨运营商异构网络协同工作, 采用虚拟化技术实现用户的逻辑隔离, 为用户提供透明的数据传输服务。基于Markov决策过程建立网络资源调度模型, 该模型考虑基础设施网络状态及用户需求等因素, 以用户需求的满足程度为优化目标, 搜索最优的链路分配决策, 将用户需求与网络资源动态适配。测试结果表明：所提出的铁路无线通信网络具有很强的可扩展性, 实现了用户需求的弹性定制, 提高了网络资源的利用效率。

求解云计算资源调度的免疫算法

求解云计算资源调度的免疫算法

An Intelligent Decision Algorithm for the Generation of Maritime Search and Rescue Emergency Response Plans

Maritime search and rescue (SAR) decisions are the most important part of maritime SAR operations. In the process of making maritime SAR decisions, a key factor affecting efficiency and success rate is how to quickly respond to accidents and develop an emergency response plan. At present, maritime SAR emergency response plans are still mostly obtained through a combination of drift prediction models and SAR experience. There is a lack of SAR resource scheduling and SAR task assignment. The primary purpose of this paper is to explore the possibility of using an intelligent decision-making algorithm to formulate maritime SAR emergency response plans so as to produce results more scientifically. First, the relevant research areas and research data are briefly introduced, and the main mathematical models involved in the optimal search theory are expounded. Next, key technologies involved in the process of maritime SAR emergency response plan generation, including the determination of search area, the scheduling of SAR resources, the allocation of search tasks, and the planning of search routes, are analyzed in detail. Two optimization model algorithms, namely the SAR resource scheduling model based on genetic simulated annealing algorithm (GSAA) and the regional task allocation algorithm based on space-time characteristics, are proposed as approaches to solving the problem of resource scheduling and task allocation. Finally, the effectiveness and optimization of the proposed algorithms are verified by analyzing the emergency response of a real case which occurred in the Bohai Sea and comparing the different schemes. Through the algorithm proposed in this paper, the efficiency of maritime SAR operations can be effectively improved and the loss of life and property can be reduced.

Distributed tasks-platforms scheduling method to holonic-C2 organization

To solve the problem of distributed tasks-platforms scheduling in holonic command and control (C2) organization, the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality, a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally, a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.

Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.

Data center holistic demand response algorithm to smooth microgrid tie-line power fluctuation

With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal operation of precision equipment, such as CPU/GPU chips and hard disk, in data center, but it would also impact the stability of the utility power grids operation. To solve this problem, this paper presents a novel tie-line power fluctuation smoothing algorithm with consideration of data center’s holistic demand response. The contributions of this paper are: (1) overcoming the limitations of treating IT load as uncontrollable workload in the traditional demand response research, we design a data center resource scheduling model to realize IT load demand response controllability; (2) two novel mechanisms are proposed: (i) the server cluster workload scheduling method with time shift mechanism, and (ii) the data center UPS (Uninterruptible Power Supply) energy storage dynamic response mechanism. (3) Combining these two mechanisms as holistic demand response of data center, we present a tie-line power fluctuation smoothing algorithm to improve power supply reliability, which is beneficial to both the high density and precision IT equipment in the data center and the utility power grid. In the experiments, the results show that the new algorithm can effectively regulate the tie-line power fluctuations under different server cluster utilization ranges and scenarios of large-scale penetration of distributed renewable energy scenarios. The new algorithm is hence able to contribute beneficially to the reliability and stability of intelligent industrial park micro-grid and utility power grids.

Optimization of warship formation air defense combat model based on dynamic scheduling of combat resources

Aiming at the problem of limited resources and operational time combat air defense combat, naval combat formation analysis dynamic resources scheduling needs, on the basis of the establishment of ship dynamic scheduling model of warship formation combat resources dynamic combat formation resource scheduling, the operational resources and operational objectives, relationship between reasonable allocation of resources to play the warship formation combat operations the maximum efficiency. The design of three kinds of air defense command models: centralized platform free attack mode, command mode, request attack mode of air defense strategy, put forward the dynamic scheduling hybrid algorithm for solving the model, according to the naval fleet air defense combat scenario example for simulation. The simulation results show that the air defense strategy with the request attack mode can have effects on multi-target attacks under the shared combat situation, and the hybrid algorithm based on combat resource dynamic scheduling can meet the real-time operational requirements.

RETRACTED: Research on agricultural supply chain system with double chain architecture based on blockchain technology

Abstract As an underlying support technology, blockchain is a shared ledger system and a computational paradigm, which is decentralized, and it is highly compatible with the distributed economic system. The distributed scheduling model of agricultural business resources based on the public service platform is a comprehensive solution to the current situation of agricultural industry which is ”scattered, small, disorderly and weak”, and plays an important role in integrating decentralized resource and making on-demand scheduling. Aiming at some key problems in the current Chinese public service platform, this paper proposes a public blockchain of agricultural supply chain system based on double chain architecture, mainly studying the dual chain structure and its storage mode, resource rent-seeking and matching mechanism and consensus algorithm. The results show that the chain of agricultural supply chain based on double chain structure can take into account the openness and security of transaction information and the privacy of enterprise information, can self-adaptively complete rent-seeking and matching of resources, and greatly enhance the credibility of the public service platform and the overall efficiency of the system.

The Model Design of Mobile Resource Scheduling in Large Scale Activities

Large scale exhibition is a complex crowd system, providing quality service for many tourists is an important issue that managers must pay attention to. Intelligent information management system should adopt scientific and reasonable resource scheduling sche-me to improve the efficiency of the crowd management. Through the domestic and foreign literature research, this paper points out the deficiencies in the previous research, and analyzes the process of crowd service in large-scale exhibition activities. In order to carry out the management of tourist service resources in the large scale exhibition, the concept model of mobile resource scheduling is put forward, and the detailed introduction of each dimension is presented. Multi-resource scheduling model based on Dijkstra and multi-ant colony optimization algorithms are established respectively. The advantages and disadvantages of the two algorithms are compared through the numerical examples. Dijkstra algorithm is superior to multi-ant colony optimization algorithm in the cost control, but in the running time of the algorithm, multi-ant colony optimization algorithm is better than Dijkstra algorithm. The research has practical significance for the development of scientific and effective population service operation plan and service management plan for large scale exhibition activities.

An innovative GA for optimisation of integrated manufacturing–transportation scheduling in VCIM systems

Virtual computer-integrated manufacturing (VCIM) is a new manufacturing concept aimed at exploiting distributed manufacturing resources, both locally as well as globally. Recently, an innovative model for resource scheduling in VCIM systems, in which manufacturing scheduling and collaborative transportation scheduling are integrated together, has been proposed. In this paper, an innovative global optimisation method based on genetic algorithm (GA) is developed to optimise the integrated manufacturing–transportation scheduling problem recently raised in VCIM literature. The proposed GA with unique chromosome representation, modified genetic operators, and novel algorithm structure is capable of searching for the global optimal solution with very high success rate. The results achieved from 10 instances of a comprehensive case study have confirmed that the proposed GA outperforms three popular commercial optimisation solvers.

Research on Multi-objective Model of Resource Scheduling Based on General Aviation Emergency Rescue

In order to solve the scheduling problem of aviation emergency rescue resources scientifically and reasonably, improve the rescue efficiency. The characteristics of general aviation emergency rescue process and resource scheduling under emergencies are analyzed, and a multi-objective model with multiple resource points and multiple demand points is constructed. The timeliness, irreplaceability and hysteresis of emergency rescue resource scheduling under the incident, the objective function that satisfies the fairness, the highest utility, and the shortest scheduling time are set in a single cycle. Finally, the model and algorithm are validated and analyzed by combining emergency rescue examples in Southwest China. The results proved the validity of the model and algorithm.

Subway dynamic resource scheduling model and simulation based on distance game

Subway dynamic resource scheduling model and simulation based on distance game

Classified scheduling algorithm of big data under cloud computing

ABSTRACTIn order to realize the optimization of resource scheduling in the cloud computing environment and improve the utilization ratio of network data resource, we need to carry out the data resource scheduling in the cloud computing environment. But when using the current scheduling method for data resource scheduling in cloud computing environment, we schedule the current task through determining the resource scheduling cost function, however, we do not take into account the comprehensive evaluation cost of performance of user data, resulting in data network resource scheduling is poor. Therefore, this paper proposed a method of scheduling network data resources in cloud computing environment based on particle swarm optimization. Firstly, this method dynamically clusters cloud computing network data resources. By considering the network bandwidth, the bandwidth utilization, the current network running state, the method introduces the particle swarm apportion model in the process of building the network data resource scheduling model, and predicts the execution speed of task based on network nodes, and schedules and arranges task sets of different data resources. We obtain the total weight of data network resource scheduling tasks and take it as parameters to calculate the fitness value, on the basis of particle swarm optimization algorithm, we optimize different network data resource scheduling tasks. Experimental results show that the proposed method is suitable for the scheduling of network data resources in cloud computing environment, and has advantages in scheduling efficiency and resource utilization.

Optimized traffic emergency resource scheduling using time varying rescue route travel time

Abstract Traffic emergency resource scheduling not only needs to satisfy the type and quantity of resources required for traffic incidents, but also to consider the change of traffic state before making the rescue decision. For this purpose, a traffic emergency resource scheduling method based on rescue route travel time estimation was proposed. Considering the vehicle queuing phenomenon in the rescue route caused by traffic incidents, the rescue route travel time was defined as the sum of free flow travel time and queuing delay time. The queuing section was shifted to the downstream of the section and making the free flow section and queue section regarded as two continuous sections. Then according to the relative relationship between the total length of rescue route and the queuing delay time of rescue vehicles, the rescue route travel time was estimated and the improved resource scheduling model was established. As shown by the emergency resource scheduling application in the expressway network of Henan province, the simulation results of rescue route travel time estimation and the scheduling model revealed that the queuing delay time directly affects the computation of the rescue travel time. In addition, using branch and bound algorithm to obtain the optimized emergency resource scheduling scheme, the proposed scheduling model could enhance the effectiveness and efficiency of emergency resource allocation and improve the road traffic safety.

Expressway Emergency Resource Scheduling Model Based on Phased Collaboration

This study considers the goal of expressway traffic incident emergency rescue and proposes an emergency resource configuration optimization method with the objective of minimum total area rescue resources in a network. First, a regional expressway road network was processed via network abstraction under the assumption that rescue resource configuration points were fixed. Then, an emergency resource scheduling model based on phased collaboration was built to minimize emergency response time and fulfill the largest resource requirements of accident points. Finally, LINGO and MATLAB software were applied to analyze and verify the models. Results show that the resource allocation amount of each configuration point and network is significantly lower after optimization. Total resources are reduced by 36%. The proposed resource scheduling model contributes to reasonable emergency resource scheduling.

A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems

The resource distribution in post-disaster is an important part of emergency resource scheduling. In this paper, we first design a multi-objective optimization model for multi-period dynamic emergency resource scheduling (ERS) problems. Then, using the framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an MOEA is proposed to solve this model. In the proposed algorithm, new evolutionary operators are designed with the intrinsic properties of multi-period dynamic ERS problems in mind. The experimental results show that the proposed algorithm can get a set of better candidate solutions than the non-dominated sorting genetic algorithm II (NSGA-II).

A Novel Resource Scheduling Model for Cloud Data Center based on Game Analysis and Optimization Theory

A Novel Resource Scheduling Model for Cloud Data Center based on Game Analysis and Optimization Theory

Dynamic electricity pricing for electric vehicles using stochastic programming

Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty.The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business.

Resource scheduling in a private cloud environment: an efficiency priority perspective

Purpose Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view. Design/methodology/approach To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model. Findings The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency. Research limitations/implications The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds. Practical implications The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice. Originality/value This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments.