A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

Research on Optimization of Dual-Resource Batch Scheduling in Flexible Job Shop

With the rapid development of intelligent manufacturing and the changes in market demand,the current manufacturing industry presents the characteristics of multi-varieties,small batches,customization,and a short production cycle,with the whole production process having certain flexibility.In this paper,a mathematical model is established with the minimum production cycle as the optimization objective for the dual-resource batch scheduling of the flexible job shop,and an improved nested optimization algorithm is designed to solve the problem.The outer layer batch optimization problem is solved by the improved simulated annealing algorithm.The inner double resource scheduling problem is solved by the improved adaptive genetic algorithm,the double coding scheme,and the decoding scheme of Automated Guided Vehicle(AGV)scheduling based on the scheduling rules.The time consumption of collision-free paths is solved with the path planning algorithm which uses the Dijkstra algorithm based on a time window.Finally,the effectiveness of the algorithm is verified by actual cases,and the influence of AGV with different configurations on workshop production efficiency is analyzed.

Battle Royale Optimization-Based Resource Scheduling Scheme for Cloud Computing Environment

Cloud computing(CC)is developing as a powerful and flexible computational structure for providing ubiquitous service to users.It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment.The variation of software and hardware resources were combined and composed as a resource pool.The software no more resided in the single hardware environment,it can be executed on the schedule of resource pools to optimize resource consumption.Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation.This study introduces a Battle Royale Optimization-based Resource Scheduling Scheme for Cloud Computing Environment(BRORSS-CCE)technique.The presented BRORSS-CCE technique majorly schedules the available resources for maximum utilization and effectual makespan.In the BRORSS-CCE technique,the BRO is a population-based algorithm where all the individuals are denoted by a soldier/player who likes to go towards the optimal place and ultimate survival.The BRORSS-CCE technique can be employed to balance the load,distribute resources based on demand and assure services to all requests.The experimental validation of the BRORSS-CCE technique is tested under distinct aspects.The experimental outcomes indicated the enhancements of the BRORSS-CCE technique over other models.

Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often overlooked.It is frequently assumed that vehicles can be accurately modeled during actual motion processes.However,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios.Taking into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading process.The optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming problem.Due to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission.Finally,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization problem.Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading.Meanwhile,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

Research on Flexible Job Shop Scheduling Based on Improved Two-Layer Optimization Algorithm

To improve the productivity,the resource utilization and reduce the production cost of flexible job shops,this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching.Firstly,a mathematical model is established to minimize the maximum completion time.Secondly,an improved two-layer optimization algorithm is designed:the outer layer algorithm uses an improved PSO(Particle Swarm Optimization)to solve the workpiece batching problem,and the inner layer algorithm uses an improved GA(Genetic Algorithm)to solve the dual-resource scheduling problem.Then,a rescheduling method is designed to solve the task disturbance problem,represented by machine failures,occurring in the workshop production process.Finally,the superiority and effectiveness of the improved two-layer optimization algorithm are verified by two typical cases.The case results show that the improved two-layer optimization algorithm increases the average productivity by 7.44% compared to the ordinary two-layer optimization algorithm.By setting the different numbers of AGVs(Automated Guided Vehicles)and analyzing the impact on the production cycle of the whole order,this paper uses two indicators,the maximum completion time decreasing rate and the average AGV load time,to obtain the optimal number of AGVs,which saves the cost of production while ensuring the production efficiency.This research combines the solved problem with the real production process,which improves the productivity and reduces the production cost of the flexible job shop,and provides new ideas for the subsequent research.

Two-Timescale Online Learning of Joint User Association and Resource Scheduling in Dynamic Mobile Edge Computing

For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge server.With the limitations imposed on transmission capacity,computing resource,and connection capacity,the per-slot online learning algorithm is first proposed to minimize the time-averaged network cost.In particular,by leveraging the theories of stochastic gradient descent and minimum cost maximum flow,the user association is jointly optimized with resource scheduling in each time slot.The theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network environment.Moreover,to alleviate the high network overhead incurred during user handover and task migration,a two-timescale optimization approach is proposed to avoid frequent changes in user association.With user association executed on a large timescale and the resource scheduling decided on the single time slot,the asymptotic optimality is preserved.Simulation results verify the effectiveness of the proposed online learning algorithms.

Modeling for UAV resource scheduling under mission synchronization

Unmanned aerial vehicle（UAV） resource scheduling means to allocate and aggregate the available UAV resources depending on the mission requirements and the battlefield situation assessment.In previous studies,the models cannot reflect the mission synchronization;the targets are treated respectively,which results in the large scale of the problem and high computational complexity.To overcome these disadvantages,a model for UAV resource scheduling under mission synchronization is proposed,which is based on single-objective non-linear integer programming.And several cooperative teams are aggregated for the target clusters from the available resources.The evaluation indices of weapon allocation are referenced in establishing the objective function and the constraints for the issue.The scales of the target clusters are considered as the constraints for the scales of the cooperative teams to make them match in scale.The functions of the intersection between the ＂mission time-window＂ and the UAV ＂arrival time-window＂ are introduced into the objective function and the constraints in order to describe the mission synchronization effectively.The results demonstrate that the proposed expanded model can meet the requirement of mission synchronization,guide the aggregation of cooperative teams for the target clusters and control the scale of the problem effectively.

No-cooperative games for multiple emergency locations in resource scheduling

When an emergency happens, the scheduling of relief resources to multiple emergency locations is a realistic and intricate problem, especially when the available resources are limited. A non-cooperative games model and an algorithm for scheduling of relief resources are presented. In the model, the players correspond to the multiple emergency locations, strategies correspond to all resources scheduling and the payoff of each emergency location corresponds to the reciprocal of its scheduling cost. Thus, the optimal results are determined by the Nash equilibrium point of this game. Then the iterative algorithm is introduced to seek the Nash equilibrium point. Simulation and analysis are given to demonstrate the feasibility and availability of the model.

Improved Delay Priority Resource Scheduling with Low Packet Loss Rate for MBMS in LTE Systems

An improved delay priority resource scheduling algorithm with low packet loss rate for multimedia broadcast multicast service(MBMS)in long term evolution(LTE)systems is proposed in this paper.Real-time services in LTE systems require lower delay and packet loss rate.However,it is difficult to meet the QoS requirements of real-time services using the current MBMS resource scheduling algorithm.The proposed algorithm in this paper jointly considers user delay information and real-time channel conditions.By introducing the user delay information,the lower delay and fairness of users are guaranteed.Meanwhile,by considering the channel conditions of users,the packet loss rate can be effectively reduced,improving the system throughput.Simulation results show that under the premise of ensuring the delay requirements of real-time services,the proposed algorithm achieves a lower packet loss rate compared to other existing algorithms.Furthermore,it can achieve a good balance between system throughput and user fairness.

The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory

In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for resource scheduling,however,resource providers have resource utilization requirements for cloud manufacturing platforms.In the process of resource optimization scheduling,the interests of all parties have conflicts of interest,which makes it impossible to obtain better optimization results for resource scheduling.Therefore,amultithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling.The cloud manufacturing platform first calculates the expected value reduction plan for each round of global optimization,using the negotiation algorithm based on the Stackelberg game,the cloud manufacturing platformnegotiates andmediateswith the participants’agents,to maximize self-interest by constantly changing one’s own plan,iteratively find multiple sets of locally optimized negotiation plans and return to the cloud manufacturing platform.Through multiple rounds of negotiation and calculation,we finally get a target expected value reduction plan that takes into account the benefits of the resource provider and the overall benefits of the completion of the manufacturing task.Finally,through experimental simulation and comparative analysis,the validity and rationality of the model are verified.

Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm

In order to solve the problem that the resource scheduling time of cloud data center is too long,this paper analyzes the two-stage resource scheduling mechanism of cloud data center.Aiming at the minimum task completion time,a mathematical model of resource scheduling in cloud data center is established.The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm.On the technology of the conventional genetic algorithm,an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm.The experimental results show that the improved genetic algorithm can significantly reduce the total completion time of the task,and has good convergence and global optimization ability.

Fuzzy Control Based Resource Scheduling in IoT Edge Computing

Edge Computing is a new technology in Internet of Things(IoT)paradigm that allows sensitive data to be sent to disperse devices quickly and without delay.Edge is identical to Fog,except its positioning in the end devices is much nearer to end-users,making it process and respond to clients in less time.Further,it aids sensor networks,real-time streaming apps,and the IoT,all of which require high-speed and dependable internet access.For such an IoT system,Resource Scheduling Process(RSP)seems to be one of the most important tasks.This paper presents a RSP for Edge Computing(EC).The resource characteristics are first standardized and normalized.Next,for task scheduling,a Fuzzy Control based Edge Resource Scheduling(FCERS)is suggested.The results demonstrate that this technique enhances resource scheduling efficiency in EC and Quality of Service(QoS).The experimental study revealed that the suggested FCERS method in this work converges quicker than the other methods.Our method reduces the total computing cost,execution time,and energy consumption on average compared to the baseline.The ES allocates higher processing resources to each user in case of limited availability of MDs;this results in improved task execution time and a reduced total task computation cost.Additionally,the proposed FCERS m 1m may more efficiently fetch user requests to suitable resource categories,increasing user requirements.

A user-oriented resource scheduling method for improving agile software pattern in cloud environment

The crowdsourcing, as a service pattern in cloud environment, usually aims at the cross-disciplinary cooperation and creating value together with customers and becomes increasingly prevalent. Software process, as a kind of software development and management strategy, is defined as a series of activities implemented by software life cycle and provides a set of rules for various phases of the software engineering to achieve the desired objectives. With the current software development cycle getting shorter, facing more frequent needs change and fierce competition, a new resource management pattern is proposed to respond to these issues agilely by introducing the crowdsourcing service to agile software development for pushing the agility of software process. Then, a user-oriented resource scheduling method is proposed for rational use of various resources in the process and maximizing the benefits of all parties. From the experimental results, the proposed pattern and resources scheduling method reduces greatly the resource of project resource manager and increases the team resource utilization rate, which greatly improves the agility of software process and delivers software products quickly in crowdsourcing pattern.

Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

A multi-resource scheduling scheme of Kubernetes for IIoT

With the rapid development of data applications in the scene of Industrial Internet of Things(IIoT),how to schedule resources in IIoT environment has become an urgent problem to be solved.Due to benefit of its strong scalability and compatibility,Kubernetes has been applied to resource scheduling in IIoT scenarios.However,the limited types of resources,the default scheduling scoring strategy,and the lack of delay control module limit its resource scheduling performance.To address these problems,this paper proposes a multi-resource scheduling(MRS)scheme of Kubernetes for IIoT.The MRS scheme dynamically balances resource utilization by taking both requirements of tasks and the current system state into consideration.Furthermore,the experiments demonstrate the effectiveness of the MRS scheme in terms of delay control and resource utilization.

Spectrum-Efficient and Fair Resource Scheduling for MBMS in LTE Systems

An improved spectrum-efficient and fair resource scheduling algorithm for multimedia broadcast multicast service(MBMS)in long term evolution(LTE)systems is proposed in this paper.By jointly considering the channel conditions of all the users,the average packet loss rate,and the fairness of users in the MBMS group,the transmission data rate of the MBMS group is first selected according to the link adaptation and the average packet loss rate of users.Then,the resource blocks are allocated to MBMS groups according to the scheduling priority.Such a resource scheduling algorithm further balances the system throughput and user fairness.Theoretical analysis and simulation results show that the proposed algorithm can achieve a good tradeoff between system throughput and user fairness in comparison with traditional scheduling algorithms.

Resource Schedule in Grid Based on Market Models

A new modern resource management method based on economic model is proposed. Giving mathematic description about economic model; analysis different resource scheduling methods based on deadline and budget constrained which present by Buyya, point out shortcoming of Buyya's schedule method. Considerate integrate factor of time and budget, by import a weight coefficient named a , puts forward a new resource schedule method named STPP based on economic models of Buyya. Contrast to old schedule strategy of Buyya through analysis and experiments, STPP policy is more flexible, and is easy to import other new QoS parameters.

Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment

Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition to some other limitations for achieving a subset of goals.Project scheduling problems are dependent on many limitations,including limitations of precedence relationships,resource constraints,and some other limitations for achieving a subset of goals.Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required,which are known and stable during the implementation process.The concept of deterministic project scheduling conflicts with real situations,in which in many cases,some data on the activity’s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations.Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us.We use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters,that is,the scheduling process must be flexible to dynamic environment features.In this paper,we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations,in which we apply the proposed heuristic rules(PHR)for scheduling multi-mode resource-constrained projects.Five projects are considered test problems for the PHR.The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems.The results in many cases of the proposed priority rules are very promising,where they achieve better scheduling dates in many test case problems and the same results for the others.The proposed model is based on the dynamic features for project topography.

Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently.The distributed robotic control system can be used in real time for resolving various challenges such as localization,motion controlling,mapping,route planning,etc.The distributed robotic control system can manage different kinds of heterogenous devices.Designing a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational requirements.For instance,scheduling of resources(such as communication channel,computation unit,robot chassis,or sensor input)to the various system components turns out to be an essential requirement for completing the tasks on time.Therefore,resource scheduling is necessary for ensuring effective execution.In this regard,this paper introduces a novel chaotic shell game optimization algorithm(CSGOA)for resource scheduling,known as the CSGOA-RS technique for the distributed robotic control system environment.The CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall performance.The CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is increased.The CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early estimations.For validating the enhanced performance of the CSGOA-RS technique,a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of measures.The resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques.