Synergizing AI and Multiprocessor Systems for Task Scheduling in Industry 4.0

An overview of knowledge-based systems for industrial scheduling is provided, with emphasis on systems which have undergone concrete development. Three perspectives are employed: knowledge representation, temporal reasoning and control. Where appropriate, developments in AI which could prove useful from the viewpoint of industrial scheduling are referred to. Some general conclusions and pointers for future development are offered. >

This chapter describes briefly the concept of hybrid (multi-model) systems and presents two implementations of such systems. The first one is a hybrid system for job-shop scheduling. It consists of a job-shop simulation model used to build a schedule and calculate its quality and an expert system optimising the schedule. A hypothetical job-shop (inspired from a real industrial shop) containing seven machine tools processing parts of ten types with complex processing routes is used to illustrate the possibilities of the system. The second implementation is a multimodel system for scheduling in the chemical industry. It also combines simulation, rules-based decision making, object-oriented representation of the system state as well as a set of well-known algorithms for solving particular problems used together for scheduling discrete-continuous processes.

PurposeIn recent years, it is imperative to establish the structure of manufacturing industry in the context of smart factory. Due to rising demand for exchange of information with various devices, and huge number of sensor nodes, the industrial wireless networks (IWNs) face network congestion and inefficient task scheduling. For this purpose, software-defined network (SDN) is the emerging technology for IWNs, which is integrated into cognitive industrial Internet of things for dynamic task scheduling in the context of industry 4.0.Design/methodology/approachIn this paper, the authors present SDN based dynamic resource management and scheduling (DRMS) for effective devising of the resource utilization, scheduling, and hence successful transmission in a congested medium. Moreover, the earliest deadline first (EDF) algorithm is introduced in authors’ proposed work for the following criteria’s to reduce the congestion in the network and to optimize the packet loss.FindingsThe result shows that the proposed work improves the success ratio versus resource usage probability and number of nodes versus successful joint ratio. At last, the proposed method outperforms the existing myopic algorithms in terms of query response time, energy consumption and success ratio (packet delivery) versus number of increasing nodes, respectively.Originality/valueThe authors proposed a priority based scheduling between the devices and it is done by the EDF approach. Therefore, the proposed work reduces the network delay time and minimizes the overall energy efficiency.

Rest Allowance Models for Optimizing Task Scheduling and Worker Well-Being in Industry

This paper studies a multi-stage and parallel-machine scheduling problem with job splitting which is similar to the traditional hybrid flow shop scheduling (HFS) in the solar cell industry. The HFS has one common hypothesis, one job on one machine, among the research. Under the hypothesis, one order cannot be executed by numerous machines simultaneously. Therefore, multiprocessor task scheduling has been advocated by scholars. The machine allocation of each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage is determined. However, machine allocation and production sequence decisions are highly interactive. As a result, this study, motivated from the solar cell industry, is going to explore these issues. The multi-stage and parallel-machine scheduling problem with job splitting simultaneously determines the optimal production sequence, multiprocessor task scheduling and machine configurations through dynamically splitting a job into several sublots to be processed on multiple machines. We formulate this problem as a mixed integer linear programming model considering practical characteristics and constraints. A hybrid-coded genetic algorithm is developed to find a near-optimal solution. A preliminary computational study indicates that the developed algorithm not only provides good quality solutions but outperforms the classic branch and bound method and the current heuristic in practice.

The planning and deployment of mega constellations has posed increasing pressure on the existing telemetry, track and command (TTC) resource scheduling strategy. The current plan-based scheduling strategy distributes resources according to needs, however, it cannot address sudden resource requests with prompt responses. By analyzing the status quo of resource scheduling systems, we have proposed a distributed collaborative scheduling technology for random access in real-time, based on the theory of low costs and rapid transfers of scheduling task resources. First, we use knowledge graph technology to map the link between scheduling tasks and idle resources and develop a graph network. Furthermore, by analyzing and evaluating the resource transfer capability of a multi-target TTC system in hemispherical coverage (MT-TTC), we propose the concept of a virtual resource pool and develop a two-layer scheduling framework in which the central scheduling system coordinates with multiple regional systems. This scheduling strategy works well when a sudden TTC request occurs. The central system manages the satellite requests and make plans, while regional systems are responsible for handling the sudden requests and report to the central system on its resource change. With regional systems sharing some degree of autonomy, the central system making a final decision and systems across the framework coordinating in real-time, we design a model on resource transfers for MT-TTC equipment and a collaboration mechanism that coordinates the resource transfers across MT-TTC equipment and other single-beam equipment within and between regions. We do experiments in simulation scheduling environments. The results show that the regional scheduling system based on MT-TTC equipment is able to manage continuous random-access requests in real-time, and avoid resource conflicts across regions by collaborating with the central scheduling system. It is of value to be applied in real scenarios.

Effective task scheduling and deployment is hard to achieve in a grid environment, especially where computing resources are heterogamous and local tasks have a high priority in the non-dedicated grid. We introduce a scheduling system of the non-dedicated grid and propose a novel task scheduling algorithm. The simulations show that the algorithm overweighs traditional algorithms a lot in the makespan and the scalability of the scheduling algorithm.

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

Cyber-physical systems (CSPs) are demanding energy-efficient design not only of hardware (HW), but also of software (SW). Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Manage (DPM) are most popular techniques to improve the energy efficiency. However, contemporary complicated HW and SW designs requires more elaborate and sophisticated energy management and efficiency evaluation techniques. This paper is concerned about energy supply planning for real-time scheduling systems (units) of which tasks need to meet deadlines. This paper presents a model-based compositional energy planning technique that computes a minimal ratio of processor frequency that preserves schedulability of independent and preemptive tasks. The minimal ratio of processor frequency can be used to plan the energy supply of real-time components. Our model-based technique is extensible by refining our model with additional features so that energy management techniques and their energy efficiency can be evaluated by model checking techniques. We exploit the compositional framework for hierarchical scheduling systems and provide a new resource model for the frequency computation. As results, our use-case for avionics software components shows that our new method outperforms the classical real-time calculus (RTC) method, requiring 36.21% less frequency ratio on average for scheduling units under RM than the RTC method.

Task management is a dilemma many people are plagued with in their daily lives. Ironically, this itself is a massive task for many, especially if they suffer from depression or other mental illnesses. In developing this app, we used ChatGPT 3.5-Turbo to receive a list of tasks from a user and return a completed schedule for them to follow. It contains a scheduling system that receives a token from the user and returns a token of its own in the form of JSON data that the program then parses and displays, a cloud storage that stores every user’s data individually and can be drawn on easily, and a tasks page where users may create new tasks and complete old ones with three different display settings relating to the range of time the tasks are in: Today, Week, and All. This application will enable users to save time by crafting a schedule for them, help improve the time management skills of users, and help them finish goals before a set deadline.

The prime motive of this work is to develop an efficacious system for workflow scheduling in the cloud using optimized deep learning. Initially, the incoming tasks are fed into a multi-objective task scheduling module where the workflow is scheduled using proposed HMLO by considering the fitness, such as cost, predicted energy, reliability and make span. Here, the energy is predicted using DRN. The HMLO is obtained by the consolidation of Mutated Leader Optimization (MLO), and Hybrid Leader Optimization (HLBO). On the other hand, the deep learning-based task scheduling process is performed by getting the incoming tasks and it is given to DLSTM for workflow scheduling by assuming the live parameters of Virtual Machine (VM) and task. Finally, both outputs are fused to schedule the tasks in the VM. The proposed HMLO[Formula: see text]DLSTM has delivered superior results in terms of minimum make span of 0.499, maximum energy of 0.947 and minimum resource utilization of 0.079.

Dynamic intelligent scheduling and control of reconfigurable parallel architectures for computer vision/image processing

A distributed scheduling framework based on selfish autonomous agents for federated cloud environments

The system developed is a scheduling system for a material manufacturer. The company focused attention on improving productivity by operating production facilities more efficiently. To this end, the company mainly aimed at increasing the lot size of production plans and decreasing equipment change losses. Production plans were done manually with a huge amount of time and were controllable only on a macro basis to prevent the occurrence of opportunity losses from operational stoppage due to the absence of inventory. Actual work instructions were issued by control departments and were loosely linked with production plans. The work instructions were formed to maximize the lot size for increasing production efficiency, and lacked in long-term outlook and total cost control. The globalization of competition called for strengthening nonprice competitiveness by shortening the manufacturing lead time as well as reducing costs. To this end, it was necessary to do away with production control aimed at improvement in the productivity of specific process units and to accomplish production plans in a short time at a low inventory level without spoiling productivity. Our scheduling model covers not only the problem of allocating lots to a process but a higher level of production planning. The objective of production planning is to incorporate the philosophy of the kanban method for manufacturing what is required in as large a quantity as required when needed. What we should especially emphasize is the simplification of the model by the hierarchization of scheduling tasks and the distribution of roles between the man and machine. The scheduling system presented is run on a daily basis and is highly rated by the users.

Task scheduling in real-time systems is a concept by which we can schedule the tasks according to their priorities. In hard real-time systems, the task scheduling is more important due to the chance of system failure. We discuss EDF task scheduling which is an optimal technique for the uniprocessor systems. The existing result of the QPA algorithm was applied on the uniprocessor system to check the schedulability of the tasks in uniprocessor system. We have extended the QPA algorithm to check the schedulability of the task sets in multiprocessor systems. Our algorithm is based on QPA algorithm and the utilization balanced algorithm. The proposed algorithm checks the absolute deadlines much faster than the existing QPA algorithm for uniprocessor real-time systems.