An effective adaptive adjustment model of task scheduling and resource allocation based on multi-stakeholder interests in cloud manufacturing

Cloud manufacturing (CMfg) is a new manufacturing mode emerging in the global manufacturing industry. One of the key issues in CMfg is task scheduling and resource allocation (TSRA), which is to allocate suitable resources for multi-tasks while satisfying the interests of multi-stakeholders. Among them, dynamic TSRA is a challenging but crucial problem to ensure the smooth operation of CMfg system since it involves several exceptions. Under these contexts, this study first analyzes the optimized objectives and conflict situations that may damage the interests of multi-stakeholders in dynamic TSRA process. And then, four adaptive adjustment strategies are designed to deal with these conflict situations and ensure the smooth operation. After that, an adaptive adjustment TSRA model based on multi-stakeholder interests (TSRA-MSI) is proposed. To solve the problem, a multi-objective algorithm called HHO-NSGA2 is proposed by combining the advantages of standard Harris Hawks Optimizer and Non-dominated Sorting Genetic Algorithm-II, which contains several problem-specific optimization strategies. In the numerical experiments, the superiority of HHO-NSGA2 is demonstrated by comparing with other five algorithms in terms of convergence, diversity, and comprehensive performance. Finally, a case study is conducted under the actual auto parts production environment, and the results also demonstrate the effectiveness of the proposed TSRA-MSI model and HHO-NSGA2 algorithm.     

Towards multi-task transfer optimization of cloud service collaboration in industrial internet platform

Cloud Manufacturing (CMfg) has gained significant attention owing to its capability in reshaping the cooperation paradigm among multiple geographically dispersed enterprises, which is conducive to handle a complex production task flexibly through the industrial internet platform. Cloud Service Assembly (CSA) is concerned with integrating a series of services together for serving a complex manufacturing task, which, as one of bottlenecks for CMfg, plays a critical role in efficient utilization of resources. Evolutionary Algorithms (EAs) have been widely used in resolving CSA in the past. However, they are always executed from scratch for tackling a single task in each run, whereas handling a batch of tasks collectively via leveraging inter-task knowledge transfer has been scarcely studied. Notably, CMfg is often faced with situation of multiple tasks arriving dynamically. In light of this, we propose a Multi-task Transfer EA (MTEA), where several service collaboration tasks are optimized jointly to speed up the search efficiency by exploiting knowledge extraction among tasks. Specifically, data models derived from evolving populations are learned to capture valuable knowledge for transfer so as to boost problem-solving efficacy, a parameter online learning strategy is utilized to tune the intensity of knowledge transfer across tasks. Extensive experiments are conducted on a series of CSA instances, results prove the feasibility and competence of MTEA against state-of-the-art peers.     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications

The use of smartphones and mobile devices has increased significantly, as have Mobile Cloud Applications based on cloud computing. These applications are used in various fields, including Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. While existing cloud-based frameworks provide such services on Virtual Machines, they incur problems such as overhead, lengthy boot time, and high costs. To address these issues, the paper proposes a Dynamic Decision-Based Task Scheduling Approach for Microservice-based Mobile Cloud Computing Applications (MSCMCC) that can run delay-sensitive applications and mobility with less cost than existing approaches. The study focuses on Task Offloading problems on heterogeneous Mobile Cloud servers. It proposes a Task Offloading and Microservices based Computational Offloading (TSMCO) framework to solve Task Scheduling in steps such as Resource Matching, Task Sequencing, and Task Offloading. Experimental results show that the proposed MSCMCC and TSMCO enhance Mobile Server Utilization while minimizing costs and improving boot time, resource utilization, and task arrival time for various applications. Specifically, the proposed system effectively reduces the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.     

基于区块链的云制造系统内可信资源调度方案

针对目前云制造系统中存在的各参与主体间信任问题以及资源调度效率问题,研究了将区块链技术应用于云制造系统中.首先,阐述了区块链技术应用于云制造系统的意义,提出了一种基于区块链技术的云制造系统;其次,设计了基于智能合约的制造资源调度方式,构建制造成本最小、时间最短、合格率最高的资源调度模型并用差分进化算法进行求解;最后,进行实验仿真.结果表明,基于区块链技术的智能合约内进行资源调度方法在保证了系统内各参与主体间相互信任的同时,有效地提高了云制造系统的资源调度效率和资源调度方案的优越性.     

云制造环境下面向多目标优化的虚拟资源调度研究

为解决云制造环境下虚拟资源调度存在的算法求解效率不高、模型建立缺乏考虑任务间关系约束和任务间及子任务间的物流时间及成本因素等不足,构建了兼顾交货期时间最小化、服务成本最低化、服务质量最优化为目标的多目标虚拟资源调度模型;采用一种基于项目阶段的双链编码方式进行编码,并提出自适应交叉与变异概率公式,以避免交叉、变异概率始终不变导致算法效率下降与过早收敛的问题;在此基础上利用基于项目阶段的多种交叉变异策略相结合的改进遗传算法进行求解,保证了算法的全局与局部搜索性能。实例结果表明,相比于传统的模型与算法,该模型适用性更强,改进的遗传算法在求解效率、准确度与稳定性方面均有较大提高。     

Hybrid multi-task learning-based response surface modeling in manufacturing

Response surface modeling is an essential technique for identifying the optimal input parameters in a process, especially when the physical knowledge about the process is limited. It explores the relationships between the process input variables and the response variables through a sequence of designed experiments. Conventional response surface models typically rely on a large number of experiments to achieve reliable modeling performance, which can be cost prohibitive and time-consuming. Furthermore, nonlinear input-output relationships in some processes may not be sufficiently accounted for by existing modeling methods. To address these challenges, this paper develops a new response surface modeling approach based on hybrid multi-task learning (H-MTL). This approach decomposes the variability in process responses into two components–a global trend and a residual term, which are estimated through self-learning and MTL of Gaussian process (GP), respectively. MTL leverages the similarities between multiple similar-but-not-identical GPs, thus achieving superior modeling performance without increasing experimental cost. The effectiveness of the proposed method is demonstrated by a case study using experimental data collected from real-world ultrasonic metal welding processes with different material combinations. In addition, the hyperparameter selection, the effects of the number of tasks, and the determination of the stopping criterion are discussed in detail.     

Multi-objective optimal scheduling of reconfigurable assembly line for cloud manufacturing

We propose a multi-objective optimization scheduling model to improve the production efficiency of a reconfigurable assembly line. We aim to minimize the costs of assembly line reconstruction, achieve the production load equalization, and minimize the delayed workload using this model. However, the proposed multi-objective optimization model is significantly complex for conventional mathematical optimization methods. Thus, we present an efficient solution approach based on a distance sorting particle swarm optimization. Finally, a case study is conducted to illustrate the feasibility and efficiency of the proposed method. Experimental results indicate that our proposed approach can significantly improve the production efficiency (i.e. increased production load balance, minimized reconstruction cost, and minimized delayed workload).     

An energy-efficient task scheduling for mobile devices based on cloud assistant

Mobile cloud computing is an emerging service model to extend the capability and the battery life of mobile devices. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. However, the task offloading results in some additional cost during the communication between cloud and mobile devices. Therefore, this paper proposes an energy-efficient scheduling of tasks, in which the mobile device offloads appropriate tasks to the cloud via a Wi-Fi access point. The scheduling aims to minimize the energy consumption of mobile device for one application under the constraint of total completion time. This task scheduling problem is reconstructed into a constrained shortest path problem and the LARAC method is applied to get the approximate optimal solution. The proposed energy-efficient strategy decreases 81.93% of energy consumption and 25.70% of time at most, compared with the local strategy. Moreover, the applicability and performance of the proposed strategy are verified in different patterns of applications, where the time constraint, the workload ratio between communication and computation are various.     

A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly

Some manufacturers outsource their disassembly tasks to professional factories, each factory of them has specialized in its disassembly ability. Different disassembly facilities are usually combined to execute disassembly tasks. This study proposes the cloud-based disassembly that abstracts ability of the disassembly factory as the disassembly resource, the disassembly resource is then able to be allocated to execute disassembly tasks. Based on this concept, the cloud-based disassembly system is proposed, which provides the disassembly service according to the user requirement. The disassembly service is the execution plan for disassembly tasks, which is the result of scheduling disassembly tasks and allocating disassembly resources. To formally describe the disassembly service, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are also discussed. The mathematical model is NP-complete, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions. The user can choose a preferred disassembly service among Pareto optimal solutions.     

Fairness in dataflow scheduling in the cloud

Expensive dataflow queries which may involve large-scale computations operating on significant volumes of data are typically executed on distributed platforms to improve application performance. Among these, cloud computing has emerged as an attractive option for users to execute dataflows allowing them to select proper configurations (e.g., number of machines) to achieve desired trade-offs between execution time and monetary cost. Discovering dataflow schedules that exhibit the best trade-offs within a plethora of potential solutions can be challenging, especially in a heterogeneous environment where resource characteristics like performance and price can be varied. To increase resource utilization, users may also submit multiple dataflows for execution concurrently. Traditionally, building fair schedules (schedules where the slowdown of all dataflows due to resource sharing is similar) while achieving good performance is a major concern. However, considering fairness in the cloud computing setting where monetary cost is part of the optimization objectives significantly increases the difficulty of the scheduling problem. This paper proposes an algorithm for the scheduling of multiple dataflows on heterogeneous clouds that identifies Pareto-optimal solutions (schedules) in the three-dimensional space formed from the different trade-offs between overall execution time, monetary cost and fairness. The results show that in most cases the proposed approach can provide solutions with fairer schedules without significantly impacting the quality of the execution time to monetary cost skyline compared to the state of the art where the fairness of a solution is not taken into account.     

Solving multi-task manufacturing cloud service allocation problems via bee colony optimizer with transfer learning

Industrial internet platform is regarded as an emerging infrastructure to increase the manufacturing efficiency via sharing resources located in multiple sites. Manufacturing cloud service allocation (MCSA) aims to assign available services to the interconnected subtasks of a complicated task such that some performance indices are optimized. Current studies on MCSA are single-task-oriented and fail to exploit the shared task-solving experiences to jointly optimize a group of tasks with enhanced solution quality and search speed. This work considers the joint optimization of multiple MCSA problems in a parallel fashion via cross-task transfer learning mechanism, and two novel transfer learning strategies are embedded into the framework of bee colony algorithm to make the best use of cross-task helpful knowledge when resolving multi-task MCSA. The first one is to design an individual-dependent transfer learning mechanism to govern the probability of whether a bee to perform intra-task self-evolution or cross-task knowledge transfer, which adaptively regulates the search behavior of each bee according to its state. The second one is to select the potential bees from foreign tasks for knowledge exchange with the aid of anomaly detection mechanism. The proposed optimizer is extensively examined on different scales of MCSA instances in multi-task scenario. Experimental results confirm the performance advantage of our proposal in comparison with other state-of-the-art peers.     

Online optimization for scheduling preemptable tasks on IaaS cloud systems

In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud system with preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce.     

Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm

Purpose

The objective of this study is to optimize task scheduling and resource allocation using an improved differential evolution algorithm (IDEA) based on the proposed cost and time models on cloud computing environment.

Methods

The proposed IDEA combines the Taguchi method and a differential evolution algorithm (DEA). The DEA has a powerful global exploration capability on macro-space and uses fewer control parameters. The systematic reasoning ability of the Taguchi method is used to exploit the better individuals on micro-space to be potential offspring. Therefore, the proposed IDEA is well enhanced and balanced on exploration and exploitation. The proposed cost model includes the processing and receiving cost. In addition, the time model incorporates receiving, processing, and waiting time. The multi-objective optimization approach, which is the non-dominated sorting technique, not with normalized single-objective method, is applied to find the Pareto front of total cost and makespan.

Results

In the five-task five-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.368 and C(IDEA, NSGA-II) of 0.3 are superior to the ratios C(DEA, IDEA) of 0.249 and C(NSGA-II, IDEA) of 0.288, respectively. In the ten-task ten-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.506 and C(IDEA, NSGA-II) of 0.701 are superior to the ratios C(DEA, IDEA) of 0.286 and C(NSGA-II, IDEA) of 0.052, respectively. Wilcoxon matched-pairs signed-rank test confirms there is a significant difference between IDEA and the other methods. In summary, the above experimental results confirm that the IDEA outperforms both the DEA and NSGA-II in finding the better Pareto-optimal solutions.

Conclusions

In the study, the IDEA shows its effectiveness to optimize task scheduling and resource allocation compared with both the DEA and the NSGA-II. Moreover, for decision makers, the Gantt charts of task scheduling in terms of having smaller makespan, cost, and both can be selected to make their decision when conflicting objectives are present.     

Material supply scheduling in a ubiquitous manufacturing system

Recently, ubiquitous manufacturing has attracted wide attention in both academia and industry. To create a successful ubiquitous manufacturing system, an efficient material handling system is essential. In accordance with this reason, mobile robots have been used for transporting materials. This paper aims at developing a methodology for scheduling the material supply for a single mobile robot in a ubiquitous manufacturing environment. In this type of environment, the processing rate of the materials along with supply quantity corresponds to the cycle of material supply. The carrying capacity of the robots are limited and thus the problem of determining the material supply quantity and material supply schedule without lack of materials for production or service processes becomes complicated. In this work, a nonlinear program is formulated to schedule the supply of material and determine the required material quantity. A heuristic algorithm based on genetic algorithm is developed to solve the problem. From the numerical experiments conducted in this study, it is observed that the proposed algorithm shows good performance and can also be implemented to solve large scale problems.     

Robotic material handler scheduling in flexible manufacturing systems for mass customization

The scheduling problem of robotic material handlers in flexible manufacturing systems (FMSs) is NP-hard. This paper proposes a state-dependent algorithm for the FMS robot scheduling problem in make-to-order (MTO) environments for mass customization (MC). A mathematical model of the problem is formulated. A computational study of the proposed algorithm is performed. The algorithm is compared to an effective FMS robot scheduling rule, the shortest remaining processing time first (SRPF) rule. The results reveal the effectiveness of the algorithm in increasing the productivity-based measures of the FMS. Practical application insights are discussed. Further research is also provided.     

Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

This paper discusses the implementation of RFID technologies, which enable the shop floor visibility and reduce uncertainties in the real-time scheduling for hybrid flowshop (HFS) production. In the real-time HFS environment, the arriving of new jobs is dynamic, while the processes in work stages are not continuous. The decision makers in shop floor level and stage level have different objectives. Therefore, classical off-line HFS scheduling approaches cannot be used under these situations. In this research, two major measures are taken to deal with these specific real-time features. Firstly, a ubiquitous manufacturing (UM) environment is created by deploying advanced wireless devices into value-adding points for the collection and synchronization of real-time shop floor data. Secondly, a multi-period hierarchical scheduling (MPHS) mechanism is developed to divide the planning time horizon into multiple shorter periods. The shop floor manager and stage managers can hierarchically make decisions for their own objectives. Finally, the proposed MPHS mechanism is illustrated by a numerical case study.     

Two-machine flow shop scheduling problem with blocking,multi-task flexibility of the first machine,and preemption

Blocking flow shop scheduling problem has been extensively studied in recent years; however, some applications mentioned for this problem have some additional characteristics that have not been well considered. Multi-task flexibility of machines and preemption are two of such characteristics. Multi-task flexible machines are capable of processing the operations of at least one other machine in the system. In addition, if preemption is allowed, the solution space grows, and solutions that are more efficient may be obtained. In this study, the two-machine flow shop scheduling problem with blocking, multi-task flexibility of the first machine, and preemption is investigated by considering the minimization of makespan as criterion. It is proved that the complexity of the problem is strongly NP-hard. Because of preemption and multi-task flexibility, there are infinite schedules for each sequence; however, it is shown that a dominant schedule can be defined for each sequence. Two mathematical models are proposed for optimally solving the small-sized instances. Furthermore, a variable neighborhood search algorithm (VNS) and a new variant of it, namely, dynamic VNS (DVNS), are presented to find high quality solutions for large-sized instances. Unlike the VNS algorithm, the DVNS algorithm does not need tuning for the shaking phase. Nevertheless, computational results show that DVNS has even a slightly better performance. The VNS and DVNS algorithms are also compared with some of the best-performing metaheuristics already developed for the flow shop scheduling problem with blocking and minimization of makespan as criterion. Computational results reveal that both algorithms are superior to the others for large-sized instances.     

Physical simulation of work scheduling rules in a flexible manufacturing system

This paper describes a physical simulator of an actual flexible manufacturing system. The simulator was used to evaluate work scheduling rules for both part selection and machine selection. Twenty-eight decision rule sets were simulated and evaluated under six major performance criteria.