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

In the context of Industry 5.0, worker well-being is a central tenet within the production process. This research work aims to contribute to this goal by integrating ergonomic factors into task allocation and scheduling while maintaining high productivity. An ergonomics-aware operator-task allocation and scheduling algorithmic framework is developed for a flexible job shop production system to minimise the makespan while ensuring that the maximum physical burden on each operator is not exceeded. The proposed algorithmic framework can integrate any method that assesses the composite ergonomic load on an operator who has been assigned multiple tasks. It can handle several ergonomic measurements of different risk factors with their predefined threshold to ensure safety and efficiency. This flexibility enables decision-makers to select the best measures for their needs. This framework is tested on small to moderate-sized instances, and the numerical experiments show that the proposed algorithmic framework converges to a solution satisfying the ergonomic risk requirement, at the expense of only a slight increase in the makespan. The expected reduction in ergonomics-related injuries in companies adopting this framework would result in an actual productivity gain, which may turn out to be significantly higher than the minor loss due to the increased makespan.

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.

Task Scheduling is the establishment of specific guidelines for the consecutive execution of activities. Within the domain of parallel computing, the process of scheduling becomes complex, requiring the use of several processors and the allocation of resources to each individual processor. The goal is to effectively distribute particular tasks across processors and computers to accomplish predetermined objectives. It is essential to focus on work scheduling and resource management, as these are the main problems in numerical comparison. This study employs a metaheuristic hybrid strategy to address the inherent challenges of task scheduling. The primary objective is to enhance the efficiency of resource allocation and work scheduling, which is achieved by utilizing a Hybrid Genetic Algorithm (HGA). The results obtained from implementing the HGA algorithm on 40 operational tasks demonstrate its higher performance in comparison to other algorithms. The approach is very effective in tackling task scheduling problems in multiprocessor computing systems. This approach is notable for its faster convergence time and more efficient utilization of resources compared to other algorithms. The HGA technique is a reliable solution for improving work scheduling and resource management in parallel computing environments.

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.

Cloud computing provides computer resources such as hardware and software as a service to consumers over a network. The primary idea of cloud computing is to distribute massive amounts of storage, processing, and information to scientific applications. In cloud computing, user tasks are organized and executed with appropriate resources to supply services successfully. In order to complete task scheduling, a variety of task allocation approaches are employed. This work proposes an efficient task scheduling method to improve the task scheduling approach. Optimization techniques are often used to solve NP-hard situations. This work presents an effective scheduling task method for cloud computing systems based on the RAO-3 algorithm. We evaluate the performance of our approach by applying it to three instances. According to the data, the proposed technique provided the optimal solution in terms of schedule length, speedup, efficiency, and throughput.KeywordsHeterogeneous resourcesRAO-3 algorithmTask schedulingCloud computing

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

Cloud computing is a new development in the world of Information Technology (IT) infrastructure and it has brought could of challenges. Task scheduling is one of the main features that allows to be efficient in cloud-computing to guarantee the effectiveness of work with the resources and make the completion time as short as possible. It should, however, be pointed out that the task scheduling in cloud computing belongs to the NP-complete optimization problems. In order to eliminate the difficulties related to task scheduling in cloud computing, a number of algorithms have been presented. One of them is also an original version of the list scheduling scheduling technique, but its implementation is specifically aimed at efficient schedules of tasks execution and load balancing in a cloud environment. This method is based on the Heterogeneous Earliest Finish Time (HEFT) strategy but with some modifications that enhance its efficiency as compared to keeping a similar level of algorithm complexity. I carried out experiments on randomly created Directed Acyclic Graphs (DAGs) with a view to testing the usefulness of the algorithm. The test done on the WorkFlowSim simulator involves testing the real and synthetic workflows. The experiments point out the difference in the effectiveness of the offered mechanism compared to the existing algorithms. As demonstrated by the experiments, the suggested would outperform the current scheduling algorithms by efficiency and use of resources. This algorithm has potential of providing improved results than any prior solutions to the task scheduling problem in computer computing although the task problem is a complex one.

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.

Task scheduling is a NP-hard problem and also an integral part of parallel and distributed computing.It becomes more complicated especially in the grid computing environment.An optimal task scheduling model and an algorithm were brought forward,which combined the advantages of immune clonal algorithm and simulated annealing.The simulation results show that this algorithm achieves resource load balancing,and it overcomes the shortcomings of genetic algorithm,and can be applied to the optimization of task scheduling successfully.

Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach

In a mobile crowd sensing system, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this paper, we consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to report their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as an effective polynomial-time heuristic algorithm, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve over 79% energy savings on average compared to a widely-used baseline approach, and moreover, the proposed heuristic algorithm produces close-to-optimal solutions.

Nowadays, big data analytics has been widely applied in addressing the growing cybercrime threats. However, energy consumption is explosive increasing with the fast growth of big data processing in anti-cybercrime. In this paper, an energy-efficient framework for big data applications is proposed to reduce energy consumption while satisfying deadline constrains. First, the problem of energy-efficient tasks scheduling of a single Spark job is modeled as an integer program. We design an energy-efficient tasks scheduling algorithm to minimize the energy consumption for big data application in Spark. To avoid service-level agreement violations for execution time, we propose an optimal task scheduling algorithm with deadline constrains by tradingoff execution time and energy consumption. Experiments on a Spark cluster are performed to determine the energy consumption and execution time for several workloads from the HiBench benchmark suite. Our algorithms consume less energy on average than FIFO and FAIR under deadlines. The optimal algorithm is able to find near optimal tasks schedules to trade off energy consumed and response time benefit in small shuffle partitions.

Optimizing task scheduling in human-robot collaboration with deep multi-agent reinforcement learning

User-controlled optimization of task scheduling for imprecise computer systems

Cloud Manufacturing (CMfg) utilizes the cloud computing paradigm to provide manufacturing services over the Internet flexibly and cost-effectively, where users only pay for what they use and may access services as needed. The scheduling method directly impacts the overall efficiency of CMfg systems. Manufacturing industries supply services aligned with customer-specific needs recorded in CMfg systems. CMfg managers develop manufacturing strategies based on real-time demand to establish service delivery timing. Many elements influence customer satisfaction, including dependability, timeliness, quality, and pricing. Therefore, CMfg depends on the use of multi-objective and real-time task scheduling. Multi-objective evolutionary algorithms have effectively examined many solutions, such as non-dominant, Pareto-efficient, and Pareto-optimal solutions, using both actual and synthetic workflows. This study introduces a new Multi-level Scheduling Model (MSM) and evaluates its effectiveness by comparing it with other multi-objective algorithms, including the weighted genetic algorithm, the non-dominated genetic sorting Algorithm II, and the starch Pareto evolution algorithm. The primary emphasis is on assessing the efficacy of algorithms and their suitability in commercial multi-cloud setups. The MSM's dynamic nature and adaptive features are emphasized, indicating its ability to effectively handle the complexity and demands of CMfg and resolve the scheduling issue within this environment. Experimental results suggest that MSM outperforms other algorithms by achieving a 20% improvement in makespan.