Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

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.

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.

ACS-based resource assignment and task scheduling in grid

To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.

Trusted Data Acquisition Mechanism for Cloud Resource Scheduling Based on Distributed Agents

Goud computing is a new paradigm in which dynamic and virtualized computing resources are provided as services over the Internet. However, because cloud resource is open and dynamically configured, resource allocation and scheduling are extremely important challenges in cloud infrastructure. Based on distributed agents, this paper presents trusted data acquisition mechanism for efficient scheduling cloud resources to satisfy various user requests. Our mechanism defines, collects and analyzes multiple key trust targets of cloud service resources based on historical information of servers in a cloud data center. As a result, using our trust computing mechanism, cloud providers can utilize their resources efficiently and also provide highly trusted resources and services to many users.

Improved differential evolution algorithm for resource-constrained project scheduling problem

An improved differential evolution（IDE）algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem（RCPSP）with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated.To investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm（HGA）,particle swarm optimization（PSO） and several well selected heuristics.The results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.

Solving resource availability cost problem in project scheduling by pseudo particle swarm optimization

This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem （RACP） by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion （PPSO） combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.

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.

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.

Joint Scheduling and Resource Allocation for Federated Learning in SWIPT-Enabled Micro UAV Swarm Networks

Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.

Dual-resource integrated scheduling method of AGV and machine in intelligent manufacturing job shop

In view of the fact that traditional job shop scheduling only considers a single factor, which affects the effect of resource allocation, the dual-resource integrated scheduling problem between AGV and machine in intelligent manufacturing job shop environment was studied. The dual-resource integrated scheduling model of AGV and machine was established by comprehensively considering constraints of machines, workpieces and AGVs. The bidirectional single path fixed guidance system based on topological map was determined, and the AGV transportation task model was defined. The improved A* path optimization algorithm was used to determine the optimal path, and the path conflict elimination mechanism was described. The improved NSGA-Ⅱ algorithm was used to determine the machining workpiece sequence, and the competition mechanism was introduced to allocate AGV transportation tasks. The proposed model and method were verified by a workshop production example, the results showed that the dual resource integrated scheduling strategy of AGV and machine is effective.

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.

Application-adaptive resource scheduling in a computational grid

Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.

Parallel Test Tasks Scheduling and Resources Configuration Based on GA-ACA

A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.

Efficient Task Scheduling for Many Task Computing with Resource Attribute Selection

Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.

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.

A New Algorithm for Resource Constraint Project Scheduling Problem Based on Multi-Agent Systems

The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.

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.

A Resource Scheduling Scheme for Spectrum Aggregation in Cognitive Radio Based Heterogeneous Networks

In spectrum aggregation(SA), two or more component carriers(CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. The current resource scheduling schemes for spectrum aggregation are not optimal or suitable for CR based heterogeneous networks(Het Nets). Consequently, the authors propose a novel resource scheduling scheme for spectrum aggregation in CR based Het Nets, termed as cognitive radio based resource scheduling(CR-RS) scheme. CR-RS has a three-level structure. Under a dynamic traffic model, an equivalent throughput of the CCs based on the knowledge of primary users(PUs) is given. On this basis, the CR users data transmission time of each CC is equal in CR-RS. The simulation results show that CR-RS has the better performance than the current resource scheduling schemes in the CR based Het Nets. Meanwhile, CR-RS is also effective in other spectrum aggregation systems which are not CR based HetNets.