Concept Of Scheduling Research Articles

Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing

AbstractThe most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model.

Multi-objective sustainable flexible job shop scheduling problem: Balancing economic, ecological, and social criteria

Industry 5.0 makes it imperative to reevaluate the manner of using resources in manufacturing systems to ensure sustainability. In this context, scheduling problems are encountering new environmental and human-related challenges, and the concept of sustainable scheduling has gained importance, aiming to balance economic, environmental, and human factors. In this paper, we propose two multi-objective mathematical models to simultaneously address these three factors as objective functions. In the first model, we consider the operator safety while using the Occupational Repetitive Action (OCRA) index to assess ergonomic risks related to task execution. The second model includes workers’ preferences in terms of machines, shifts and task variety. The objective is to improve the general well-being of workers by proposing a schedule that respects as much as possible their preferences. Both models integrate the travel time of workers and products between machines. To solve these NP-hard scheduling problems, we use the Non-dominated Sorting Genetic Algorithms II and III (NSGA-II and NSGA-III), enhanced with a Q-learning strategy for parameter selection and a variable neighborhood search based on reinforcement learning. The obtained results provide a comprehensive analysis of the interactions between these criteria, demonstrating the capability of such approach to achieve a favorable balance between multiple objectives while addressing the new challenges of production scheduling in Industry 5.0 context.

Decentralized Scheduling and Dispatch Control for the Traditional Labor-Intensive Assembly System

The fourth industrial revolution has broadly transformed the manufacturing system. However, this transformation is somewhat lacking in traditional or manual production systems due to the absence of IT infrastructure. Such traditional industries need to have the advantage of real-time control and monitoring. This study has developed economic assembly planning, scheduling, and control for a traditional assembly system. We used the concept of the configurable virtual workstation as the digitalization framework. Then, we employed the decentralized scheduling concept to reduce the computational effort in scheduling the complex product. The implementation result showed that scheduling and planning have transformed the traditional assembly process into intelligent scheduling and control with low digitalization effort

Telework as a dynamic capability of firms: A qualitative research and narratives

Unexpected and negative factors such as pandemics, crises, etc. have a profound effect on business environment and entail a paradigm shift in the classical concept of work schedule. The paper aims to explore the phenomenon of telework as a dynamic capability of firms to ensure business continuity in the face of all kinds of unpredictable events and delves into the factors behind telework applications that contribute to increasing the resilience of SMEs to chaotic business shifts. Dynamic capabilities theory constitutes the methodological basis of the study. For the purpose of the research, the qualitative method was used, which has a detailed and exploratory approach with inductive questions and enables a holistic picture of the phenomenon under investigation to be revealed. The multi-site case study design was preferred, and the data collected were analysed using content analysis. In July–August 2022, 13 semi-structured interviews were conducted with the owners, top and mid-level managers of SMEs operating in the service sector in Turkey. MAXQDA 2020 programme was used to analyse the data obtained. As a result of the research, the structure of the telework theme consisting of three dimensions (technological infrastructure, culture, and work attributes) and ten sub-dimensions (working order, trust, implementation history, business strategy, etc.) was discovered. For telework to be carried out healthily, firms should establish working order, work to be done, time spent by employees in front of the computer, etc., be more flexible and receptive to changes in work practices, create an appropriate culture and establish manager-employee trust.

A systematic energy-aware scheduling framework for manufacturing factories integrated with renewables

ABSTRACT With the escalating integration of renewable energy sources (RESs) within the manufacturing sector, the synergy between energy and production management has gained paramount importance. To harmonise energy efficiency with production effectiveness, the concept of energy-aware scheduling, comprising off-line, on-line, and hybrid scheduling, has been introduced. While each approach has merits and drawbacks in handling unexpected disturbances, recovering optimal performance, and managing computational costs, their designs often tend to be ad hoc. Currently, there is a lack of a systematic design that effectively balances computational costs, robustness to unexpected disturbances, and optimal performance. Moreover, existing results do not fully leverage the flexibility in factories integrated with Renewable Energy Sources (RESs). In response, this study introduces the paradigm of event-triggered hybrid scheduling (ETHS) to achieve a balance among optimality, robustness, and computational cost by fully exploring the system's flexibility. The efficacy of ETHS is empirically demonstrated through a comprehensive case study involving a real-world manufacturing facility. Diverse scenarios are scrutinised, accompanied by sensitivity analyses pertaining to parameter choices. The outcomes conspicuously underscore the potency of ETHS in facilitating the harmonious alignment of energy and production management within the ambit of manufacturing industries.

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.

Enhancing Public Transit System Through AI and IoT

The paper investigates the synergy between AI and IoT in revolutionizing public transit systems, emphasizing their role in addressing existing challenges and improving overall efficiency. It explores the application of IoT devices in creating a smart infrastructure that enables real-time data collection, monitoring, and management of transit operations. The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles. By analyzing sensor data, transit authorities can proactively address maintenance needs, minimizing downtime, reducing repair costs, and ensuring a more reliable and sustainable public transit service. Route optimization emerges as another crucial aspect of the study, highlighting how AI algorithms leverage historical and real-time data to recommend the most efficient transit routes. Factors such as traffic patterns, weather conditions, and passenger demand are considered to enhance overall system efficiency and reduce travel time for passengers. The paper introduces the concept of dynamic scheduling, illustrating how AI-driven algorithms adapt transit schedules in real-time based on changing passenger needs and external factors. This dynamic approach aims to provide more responsive services, ultimately reducing wait times and improving overall user satisfaction. Passenger information systems are explored as a pivotal component, illustrating how AI and IoT technologies enhance the passenger experience. Real-time communication through mobile apps, digital displays, and other channels ensures that passengers have accurate and timely information about arrival times, delays, and alternative routes, empowering them to make informed decisions. The researchers also delve into fare optimization, examining how AI algorithms analyze data on passenger demographics, travel patterns, and economic factors to create fair and affordable fare structures. This approach aims to encourage ridership, increase revenue, and improve the financial sustainability of public transit systems. The abstract presents a comprehensive overview of how the integration of AI and IoT technologies in public transit systems transforms urban mobility. The findings suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit. As cities continue to explore innovative solutions, the abstract serves as a roadmap for developing smarter, user-friendly, and efficient urban transportation networks, ultimately contributing to improved quality of life for residents.

Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption.

Research on real-time scheduling optimization technology of power system based on deep learning

Abstract In the context of the increasingly severe world climate form, how to rationally arrange and dispatch energy has become an urgent need. This paper proposes a deep learning-based power system scheduling model based on the concept of perfect scheduling, using GRU to learn scheduling data. A different training set is constructed to train the model according to the load characteristics at different moments, and the model is updated in real time based on the data at the current moment. The analysis of the algorithms reveals that the scheduling error rate of this model ranges from −-3% to 2%, and the average RMSE of the scheduling scheme is 2.72, placing it in close proximity to the optimal scheduling strategy. Due to a 6.5% reduction in scheduling cost compared to the average cost of the two analyzed algorithms, the average time reduction is 76.3%. The scheduling optimization model proposed in this paper exhibits excellent performance.

An attempt to resolve no-wait flow shop scheduling problems using hybrid ant colony and whale optimization algorithms

The incentive for many developments and scientific progresses within the field of scheduling has emerged from industrial environments, and naturally, it could be utilized in expressing the scheduling concepts regarding terms used in the industry. Generally speaking, scheduling problems are known as limited optimization issues through which decisions related to the machines’ assignment and works processing sequence are probed. Thus, following a review of the related literature, the major goal of this research is to design a mathematical model and to solve it through a meta-heuristic for no-wait flow shop scheduling problem using different machines for the purpose of minimizing the time required to complete the work using whale and ant colony optimization (ACO) algorithms in Sanat-Gostar-e-Hamgam Shoe Company. The ACO and whale algorithm methods are used to compare and predict scheduling activities in manufacturing line of shoe industry. The results showed an ACO algorithm with two stages in mean ideal distance (MID) end amounting to 76.65 and 77.38, respectively. Also, regarding the amounts of standard error mean squares, it could be claimed that the model designed using the improved whale algorithm has a better prediction, and the minimum time required to complete works using the whale algorithm is estimated to be equal to 86.1071. This could lead to an optimal state in achieving the predetermined goals.

Approximation algorithms for scheduling parallel machines with an energy constraint in green manufacturing

Motivated by current green manufacturing standards, in this paper we study a parallel-machine scheduling model in which the energy cost incurred on each machine is machine-dependent and proportional to the load of the machine. The objective is to determine a production schedule with the minimum makespan subject to the energy constraint that the total energy cost does not exceed a given bound. We provide a technical lemma that enables us to design a very efficient approximation algorithm with a worst-case bound that can arbitrarily approach 43, improving on the existing performance ratio of 33+14≈1.686 in the literature. By introducing the concept of a monotonic schedule, we are able to develop the first polynomial time approximation scheme for this scheduling problem. The scheduling problem studied in this paper is an important special case of the generalized assignment problem (GAP). Our techniques and results bring new insights into research on the GAP.

An Evolutionary Review on Resource Scheduling Algorithms Used for Cloud Computing with IoT Network

Abstract: The goal of the distributed computing paradigm known as "cloud computing," which necessitates a large number of resources and demands, is to share the resources as services delivered over the internet. Task scheduling is a very significant stage in today's cloud computing. While lowering the makespan and cost, the task scheduling method must schedule the tasks to the virtual machines. Various academics have proposed many scheduling methods for organizing work in cloud computing environments. Scheduling has been considered the most important for cloud computing since it might directly impact a system's performance, including the efficiency of resource utilization and running costs. This paper has compared all the already used algorithms that work on different parameters. We have tried to give better solutions for resource allocation and resource scheduling. In this study, various swarm optimization, evolutionary, physical, evolving, and fusion meta-heuristic scheduling methods are categorized according to the environment of the scheduling problem, the main scheduling goal, the task-resource mapping pattern, and the scheduling constraint. More specifically, the fundamental concepts of cloud task scheduling are addressed without difficulty.

Energy‐optimized cluster head selection based on enhanced remora optimization algorithm in underwater wireless sensor network

SummaryWith the technological advancements, wireless sensor network (WSN) has played an impeccable role in monitoring the underwater applications. Underwater WSN (UWSN) is supported by WSN but subjected to data dissemination in an acoustic medium. Due to challenging conditions in underwater scenario, the limited battery resources of these sensor nodes stem to a crucial research problem that needs to address the energy‐efficient routing in UWSN. In this research work, we intend to propose an energy‐optimized cluster head (CH) selection based on enhanced remora optimization algorithm (ECERO) in UWSN. Since CH devours the maximum energy among the nodes, we perform selection of CH based on EROA while considering energy, Euclidean distance from sink, node density, network's average energy, acoustic path loss model and lastly, the adaptive quantity of CHs in the network. Further, to reduce the load on CH node, we introduce the concept of sleep scheduling among the closely located cluster nodes. The proposed work improves the performance of recently proposed EOCSR algorithm by great magnitude which claims to mitigate hot‐spot problem, but EOCSR still suffers from the same due to relaying a large magnitude of data.

The Enlightenment of German Short-term Work Schedule to China under the Background of COVID-19 Epidemic

Starting from the concept of German short-term work schedule, this paper fully studies its operation system from various aspects and discusses the changes it made during the COVID-19 epidemic. Based on the actual situation, the ARMA model is used to analyze the data of the five major commodity industries in Germany during the epidemic. By comparing the average relevant data of Germany with those of the European Union, the euro area and OECD during the COVID-19 epidemic, the advantages of Germany's short-term work schedule are high-lighted and the disadvantages are analyzed. Finally, in view of the advanced experience of the schedule, combined with the national conditions of our country, the paper puts forward reasonable suggestions for the unemployment insurance system in our country.

Knowledge extraction for solving resource-constrained project scheduling problem through decision tree

PurposeProject scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.Design/methodology/approachThe resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.FindingsIn this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.Originality/valueThis paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

Use of Predictive Models for Labor-Productivity Loss in Settling Disputes

Given inherent difficulties in construction, optimizing labor efficiencies is paramount to project success. Research described in this article conducted demonstrates that an analysis of planned activities in a critical path methodology (CPM) schedule may be used to forecast future productivity inefficiencies. Specifically, this study relies on the concept of CPM schedule’s density, which is defined as the number of overlapping like-trade activities on any given workday. This metric is directly related to the required labor resources required to complete that work within the activities’ planned durations. Schedule density increases where more planned activities overlap with each other; for instance, occurrence of such increases is common when the schedule is accelerated. Regression models were derived using metrics drawn from CPM schedule updates’ activities and durations and compared to actual labor productivity experienced. Strong correlation findings support development of predictive models that quantify potential labor inefficiencies before they occur. However, the question remains as to the strength and applicability of predictive models in formal litigation. This paper presents findings of this research and discusses how such findings may be used to facilitate settlement in dispute resolution procedures.

Adaptive Bandwidth Management Model for Wireless Mobile Ad-hoc Network

The quality of service (QoS) component in a mobile ad-hoc network has an active role in the current network scenario. In a dynamic mobile ad hoc network, ensuring optimum QoS with a scarce network resource is a significant challenge. To achieve QoS, it is essential to adopt some effective and efficient mechanisms. We have proposed an adaptive bandwidth manager model (ABMM) which uses a bandwidthsharing concept along with the flexible bandwidth reservation algorithm (FBRA) for an effective, quick and authentic data transfer. During real-time data transfer, to make communication effective, we make use of bandwidth-sharing network design problems and the concept of reserving bandwidth in high-performance networks. In our proposed model we are concentrating on the maximum utilization of resources, and using the scheduling concept to provide the minimum required bandwidth guarantee to QoS flows. Our goal is to reduce the delay in data transfer and enhance the throughput while properly utilizing the system resources. Our simulation result also shows that our model improves the network performance.

A framework for production planning in additive manufacturing

Additive manufacturing (AM) introduces a set of technology-specific problems, such as the proper orientation of parts or the placement of several heterogeneous parts in the same build cycle, which are not addressed by traditional approaches to production planning and scheduling. Although these new production subproblems have been implicitly addressed by several works according to generic nesting and scheduling concepts, a literature review revealed that there is no uniformity in identifying and, thus, solving all these subproblems. For this reason, and as a result of an in-depth analysis of the existent literature on AM production planning and an analogy with classic cutting and packing typologies, the present paper offers a framework to formalise the production planning problem in AM at the operational level. This framework can be used as a reference to focus on and address these AM-related problems for efficient production planning. It is designed at the subproblem level and centres on production order processing in AM. A coding strategy is specifically developed for the framework, which is applied to a review of relevant works that propose models for the production planning of AM systems. Finally, the review results are discussed and possible extensions of the framework are proposed.

Multi objective short term hydro-thermal- CHP scheduling using social spider algorithm

Potential of cheap and emission free hydro electric generation and that of another non linear efficient distributed energy resource namely combined heat and power (CHP) are utilized in the present dissertation. Water discharge from cascaded multi-reservoir system, its net head and power generation from such a hydroelectric system bears a nonlinear relationship. Such a hydro system with water transport delay among reservoirs is considered here. Optimal utilization of these sources to serve demands over short term of 24 h of a day by scheduling their respective generations so as to achieve multi objectives of cost effective production, reduced emission and transmission loss have been studied using a bio-inspired social spider algorithm. Outcome of the first test system containing hydro-thermal units shows the efficacy of the utilized optimization method while compared to other existing optimization techniques. The second test system is designed for the first time to exhibit the usefulness of the used algorithm in hydro-chp scheduling concept. Provided Statistical analysis justifies the significance of the data of the test systems.

Optimal design of a nonlinear control system based on new deterministic neural network scheduling

In this paper, a new deterministic neural network scheduling is proposed to optimally design the controller of a nonlinear system. The new deterministic neural network scheduling can improve the robustness and stability of the controller design by merging the concept of scheduling based on Q-learning and the neural network algorithm. The controller design of the proposed method is online; therefore, an accurate model and plant parameters are not required. In the proposed method, a new rule for updating the Q-learning policy is used to estimate the tracking error according to the Bellman equation, and the stability of the designed controller is derived by applying graph theory and the Riccati inequality and is determined based on the Lyapunov stability. Furthermore, to illustrate the effectiveness and robustness of the proposed method, two simulated cases based on an inverted pendulum between the new deterministic neural network scheduling and a radial basis function neural network are compared. Additionally, convergences are also compared. The simulation results indicate that the controller designed by the new deterministic neural network scheduling is better than that designed by the radial basis function neural network. In addition, compared to the radial basis function neural network, the proposed method can better minimize the cost function.