Effective Scheduling Research Articles

A novel BiGRU multi-step wind power forecasting approach based on multi-label integration random forest feature selection and neural network clustering

Accurate wind power forecasting helps to carry out effective scheduling and scientific management of wind power, and improve the security and reliability of the power grid. However, the intermittency, volatility and instability of wind energy make wind power forecasting challenging. Therefore, in order to improve the accuracy and stability of wind power forecasting, this paper proposes a bidirectional gated recurrent unit (BiGRU) multi-step wind power forecasting approach based on multi-label integration random forest (MLRF) feature selection and neural network clustering (NNClustering). The proposed MLRF method extends the applicability of random forest through multiple criteria and enables feature selection for multi-step forecasting tasks of multi-factor time series to obtain optimal input features and time steps, which reduces the computational cost and improves the generalization ability of the model. The proposed NNClustering method establishes a novel convolution-based clustering structure and adjusts the parameters by gradient descent method to obtain the optimal clustering centers, and the robust data applicability of the method is validated in multiple seasonal experiments. The WOA-BiGRU forecasting model is constructed separately for each cluster, which reduces the modeling difficulty and better extracts the characteristics. The BiGRU model extracts more efficient characteristics by processing sequences in both directions and the important parameters of BiGRU are optimized by the whale optimization algorithm (WOA) to obtain the optimal forecasting model. Experimental results over multiple seasons show that the proposed hybrid approach has good forecasting performance and robustness, which provides a novel and efficient solution for wind power forecasting.

Reinforcement learning for electric vehicle charging scheduling: A systematic review

As climate change and environmental concerns have become increasingly pressing issues, electric vehicles (EVs) have emerged as a viable and environmentally-friendly alternative to their traditional gasoline-powered counterparts. With the growing popularity of EVs, it is widely recognized that scheduled EV charging is of vital importance in enhancing users’ satisfaction, improving the profitability of charging stations, and ensuring the secure operation of power grids. Therefore, effective charging scheduling is crucial to achieve EV integration into modern mobility and further promote mass EV adoption. However, EV charging scheduling problems (CSPs) present a significant challenge to conventional methods owing to the complex nature of the decision environment. Reinforcement learning (RL) has gained considerable attention for addressing various CSPs with a Markov decision process (MDP) formulation. This paper aims to conduct a systematic review of existing studies that utilize RL to tackle CSPs. Firstly, we present a summary of the state-of-the-art CSPs and RL algorithms, followed by a review of previous research studies and a discussion of the current challenges in this field. Finally, several unresolved issues and possible future research topics are identified based on our review findings.

Optimizing omnichannel retailer inventory replenishment using vehicle capacity-sharing with demand uncertainties and service level requirements

This study explores an inventory replenishment problem for an omnichannel retailer selling a product with demand uncertainties and service level requirements in different channels through a capacity-sharing strategy. The omnichannel retailer allows customers to order products online and then pick them up in retail stores. The capacity-sharing strategy is considered to reduce travel costs when the omnichannel retailer delivers products in different channels. With the capacity-sharing strategy, some products can be transferred from the online delivery vehicle to the retail store delivery vehicle. The expectation and the bounds on the online route travel cost of the capacity-sharing strategy are determined, based on which the capacity-sharing rules are established. Optimization models and a solution procedure are developed to solve the omnichannel retailer inventory replenishment problem. To ensure adequate performance, service level requirements are considered when making delivery decisions. Numerical experiments using randomly generated and artificial instances are performed to verify the effectiveness and practicality of the proposed optimization models and the solution procedure. The results show that the proposed models and solution procedure can effectively reduce travel costs, and can provide effective schedule and delivery quantity decision support for omnichannel retailers.

Mean makespan task scheduling approach for the edge computing environment

Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics.

Proses Penyusunan Proposal Corporate Event oleh PT Aldena Ganthari Kreatif

This study aims to analyze the process of preparing a proposal for the event "Re-Grand Launching BMW Performance Motors Indonesia" by PT Aldena Ganthari Kreatf. The purpose of this study is to understand the steps in the preparation of the proposal as well as the challenges faced and solutions implemented during the process. The research methods used are observation, secondary data collection through documentation, and interviews and analysis methods carried out are qualitative descriptive. The results of the study show that 1) In the process of preparing the BMW PMI Re-Grand Launching proposal, there are several discrepancies with the pitching process theory in Aryanti & Rudatin (2022). 2) The challenges faced in the proposal preparation process are the short time in processing and changes in the client's wishes that have the potential to change the concept. In facing these challenges, Event Organizers (EOs) must create vendor-related databases and create effective schedules to achieve short time targets and always maintain communication with clients.

Adaptive task migration strategy with delay risk control and reinforcement learning for emergency monitoring

The timely and reliable handling of post-disaster emergency monitoring tasks is crucial for effective rescue operations. UAV-assisted edge computing plays a pivotal role in the rapid deployment of such systems. However, challenges persist due to communication and computation resource bottlenecks when dealing with delay-sensitive monitoring tasks. In dynamic post-disaster environments, effective task scheduling and resource allocation decisions directly impact the system’s ability to process tasks. Therefore, this paper proposes an adaptive task migration decision-making system for emergency monitoring tasks in UAV-assisted edge computing. Firstly, We decomposed the optimization objectives based on the task processing workflow, then devised a stepwise delay risk control and resource recovery mechanism based on early discarding. Secondly, by integrating multi-agent reinforcement learning (MARL), optimal strategies for task offloading, UAV queue scheduling, and communication resource allocation are learned to enhance the decision system’s environmental awareness and maximize the successful completion of emergency monitoring tasks. Simulation experiments demonstrate that the algorithm significantly improves the success rate of migration tasks and data processing capacity, thereby validating its convergence and effectiveness.

A paradigm shift in appointment Scheduling: Introducing a decentralized integrated Online booking system

Long wait times, coordination, and integration are persistent issues in many health systems, hindering effective management of client waitlists and delaying treatment. The traditional approach of independent scheduling at each center does not allow for a coordinated response to demand fluctuations.An Integrated Online Booking system (IOB) for outpatient services is designed in this study, presenting the first central intake-based appointment system integrated with an optimization approach and a novel decentralized and distributed appointment system. This system considers the independence of centers while better responding to demand and improving overall system performance.The optimization component of the IOB system is expanded in this paper to include multiple sites, patient preferences, priorities, and wait time targets. It becomes a large-scale problem as all requests from a geographical region are combined into one stream. A decomposable algorithm based on the Alternating Direction Method of Multipliers (ADMM) is used for this system. The study employs real large-scale MRI data from all hospitals in Ontario, Canada, to demonstrate the effectiveness of the IOB system, providing insights into its potential impact on healthcare operations. The results indicate that the IOB system can outperform common appointment scheduling approaches in terms of reducing patient wait times, balancing utilization rates, improving referral patterns, and enhancing system efficiency.The proposed IOB system can bring added value to the market by speeding up diagnosis and treatment processes while improving the efficiency of healthcare systems. This platform can be considered as Software as a Service (SaaS), providing a scalable and accessible solution for managing appointments across various sectors, and leveraging blockchain technology to ensure secure, transparent, and tamper-proof appointment records. This study provides insights for decision-makers, highlighting the importance of effective scheduling in managing client waitlists during treatment in the healthcare sector, such as elective surgery, home healthcare, physical and mental therapies, and other service industries like education, public consultations, and government services. The IOB system can be integrated with Artificial Intelligence (AI)-based tools and machine learning methods and combined with other service software to optimize appointment scheduling and improve system performance.

Optimal Profitability Analysis for Contractors Regarding Variations in Payment Systems and Scheduling

Construction projects often face higher risks than other types of projects, primarily due to potential delays from inaccurate scheduling and cash flow issues. Therefore, effective scheduling and payment systems are crucial for contractors to reduce risks and achieve optimal profitability. This study aims to identify optimal cash flow and profitability for contractors through the analysis of various payment systems and the utilization of float time. The methods used include cash flow analysis and feasibility analysis using NPV, BCR, and ROI methods. This research explores eight different payment and scheduling approaches to evaluate their impact on project profitability. The results show that cash flow across all alternatives remains positive without experiencing a shortage of funds and indicate that the contractor's optimal profitability is achieved with application of alternative 5, featuring 0% float time and no advance payments. This alternative results in the highest profit, with an NPV of Rp 15,338,960,581, a BCR of 1.059, and an ROI of 5.90%. From these results, it can be concluded that not utilizing float time leads to the most optimal profitability for the contractor. Additionally, it can be said that the scheduling plan implemented by the contractor for the Twin Tower project is well-structured. However, for the payment system, it is recommended to adopt a no-advance payment approach. It is hoped that this study will help contractors optimize their profits in the competitive construction industry.

A resilience-oriented pre-positioning approach for electric vehicle routing and scheduling in coupled energy and transport sectors

High-impact low-probability (HILP) events have occurred more frequently than before and caused severe damage to conventional power systems. Energy hubs (EHs) consist of various distributed energy resources (DERs) for energy generation, conversion and storage across different sectors. The high energy integration of EH systems makes them more robust under extreme events than traditional power systems. In addition, the large penetration of electric vehicles (EVs) has been witnessed in modern energy systems due to their significant benefits in accelerating transport electrification and reducing carbon emissions. Due to the characteristics of mobility and flexibility, EVs can be utilised as mobile energy resources in coupled energy and transport sectors and shift energy between different regions via effective routing and scheduling behaviours. In this context, this paper proposes a resilient-oriented pre-positioning approach for the routing and scheduling of multiple EVs in a coupled energy-transport system, where the energy system includes multiple EHs across both electricity and heat sectors. To simulate real-world scenarios, uncertainties associated with renewable generation, load profiles, and EV commuting time as well as contingencies related to component failures are incorporated into the proposed pre-positioning approach via stochastic programming. Extensive case studies are carried out based on an EH system including three EHs and five EVs, which illustrate that EVs can coordinate with static DERs in the system and perform energy shifting between different EHs via appropriate routing and scheduling behaviours. Additionally, results demonstrate that the proposed pre-positioning approach can achieve higher resilience level and ensure the supply continuity of critical loads.

A randomized controlled pilot study of daily intravenous ketamine over three days for treatment-resistant depression

BackgroundStudies have confirmed the rapid antidepressant action of ketamine in depressive episodes. Nevertheless, a standardized procedure for the delivery of ketamine infusion in individuals suffering from treatment-resistant depression, particularly in terms of infusion frequency and total dosage, remains undetermined. In addition, an efficacious ketamine regimen for persistent pain management involved a continuous 10-day infusion period with no notable adverse effects. Consequently, the primary objective of this study was to evaluate the antidepressant capacity of consecutive ketamine infusions spanning over three successive days, the duration of therapeutic response, and the overall safety profile of the treatment.MethodsIn this randomized controlled trial, participants aged 18–64 with treatment-resistant depression were randomized to receive either intravenous ketamine or midazolam (used as an active placebo) for 40 min daily over three consecutive days. Statistical analysis using repeated measures ANOVA was employed to assess the changes in the total score of the Montgomery–Åsberg Depression Rating Scale (MADRS) and the clinical global impression-Severity from the initial assessment to 10 and 31 days post-infusion. Additionally, the duration of response and remission was evaluated using Kaplan–Meier survival analysis.ResultsOut of 33 randomized participants, 20 underwent the treatment as planned. By day 10th, the ketamine group had a mean reduction in MADRS score of 12.55 (95% CI = 6.70–18.09), whereas the midazolam group had a decrease of 17.22 (95% CI = 11.09–23.36). This pattern continued to day 31, with ketamine showing a mean score decrease of 13.73 (95% CI = 7.54–19.91) and midazolam a fall of 12.44 (95% CI = 5.61–19.28). Both treatments were well tolerated, with dissociative symptoms in the ketamine group being temporary and ceasing by the end of each infusion.ConclusionIntravenous ketamine given for three consecutive days did not show a notable antidepressant advantage when compared to the active placebo midazolam, highlighting the need for further research into effective treatments schedules for treatment-resistant depression.Trial registrationNCT05026203, ClinicalTrials.gov, registered on 24/08/2021.

Impact of effective schedule management on high-rise building projects

Effective schedule management is a critical factor in the successful execution of high-rise building projects. This paper explores the impact of robust schedule management practices on project performance, highlighting the benefits, challenges, and strategies for optimizing timelines. High-rise construction presents unique complexities, including coordination of multiple trades, strict safety regulations, and logistical challenges related to vertical construction. Utilizing advanced scheduling methodologies such as Critical Chain Project Management (CCPM) and Building Information Modeling (BIM), combined with traditional techniques like the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT), can significantly enhance project efficiency. The integration of these methodologies facilitates better resource allocation, reduces delays, and improves overall project outcomes. The study also addresses the role of technology, including project management software like Primavera P6, in enhancing schedule accuracy and reliability. Additionally, it examines the critical role of project managers in orchestrating these activities and emphasizes the importance of effective communication and risk management. This comprehensive review provides insights into the key factors that contribute to successful schedule management, offering recommendations for future research and practice to further advance the field. Keywords: Building Information Modeling (BIM), Critical Chain Project Management (CCPM), High-Rise Building Projects, Project Performance Optimization, Schedule Management.

Fractional Jaya election‐based optimization enabled routing and charge scheduling for electric vehicle in cloud‐assisted Vehicular Ad Hoc NETwork

SummaryElectric vehicles (EVs) are the emerging environmentally friendly approach that is used to minimize greenhouse gases and carbon dioxide (CO2) emissions in the atmosphere. A clear strategy is required for scheduling charging stations (CS) to EVs based on their applications. In this research work, a robust routing and effective charge scheduling approach are devised using a cloud‐assisted Vehicular Ad Hoc NETwork (VANET) for charging EVs. Here, the multi‐objectives, like predicted traffic density, battery power, and distance, are used to identify the optimal routing of EV to CS. The predicted traffic density is evaluated using Deep Long Short Term Memory (DLSTM) and is trained using a developed Jaya Election‐Based Optimization Algorithm (JEBOA), which is the incorporation of Jaya Optimization (Jaya) and Election‐Based Optimization Algorithm (EBOA). Next to optimal routing, the charge scheduling process is carried out using the Fractional Jaya Election‐Based Optimization Algorithm (Fractional JEBOA) by considering the priority, response time, and latency of the EV. The designed Fractional JEBOA is the integration of Fractional Calculus (FC) and the developed JEBOA. Moreover, the various evaluation metrics are considered to calculate the performance of the designed method, which attained a delay of 0.243 ms, distance of 35 km, power of 95 W, response time of 0.441 s and traffic density of 0.664.

Research on Multi-Objective Optimization Scheduling Strategy for Cascaded Small Hydropower Stations Based on Improved HBA

Abstract Effective optimization scheduling strategy is the premise and key to improving the power generation and capacity benefits of cascade small hydropower stations (CSHS). However, the power generation of CSHS is significantly affected by complex hydraulic and electrical constraints. To effectively solve this problem, an improved honey badger algorithm (HBA) is proposed by updating the mutation strategy and introducing non-dominated sorting to achieve the multi-objective optimization scheduling solution of CSHS. The following improvements have been made to the standard HBA: Firstly, the Tent chaotic mapping is applied to the population initialization stage, its strong ergodicity and randomness ensure the randomness of the initialization stage and improve the global search ability of CSHS scheduling. Secondly, the powerful optimization ability and fast convergence speed of the Golden-Sine strategy make updating and mutation more efficient, greatly enhancing the local search ability of CSCH scheduling. And then combining the non-dominated sorting of the non-dominated sorting genetic algorithm-II (NSGA-II), an improved multi-objective Honey Badger Algorithm (IMOHBA) is further proposed to achieve multi-objective solutions for CSCH scheduling. Finally, abundant field experiments were tested for validation. The results expressed that compared to other algorithms, the effect of IMOHBA in CSHS scheduling can further increase power generation, while also improving the peak shaving ability of CSHS.

A Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling

Effective work scheduling for clinical training is essential for medical education, yet it remains challenging. Creating a clinical training schedule is a difficult task, due to the complexity of curriculum requirements, hospital demands, and student well-being. This study proposes the Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling (CCP-AI-MWS) to optimize clinical training schedules. The CCP-AI-MWS integrates the Collaborative Requirement Planning principle with Artificial Intelligence (AI). Two experiments have been conducted comparing CCP-AI-MWS with current practice. Results show that the newly developed protocol outperforms the current method. CCP-AI-MWS achieves a more equitable distribution of assignments, better accommodates student preferences, and reduces unnecessary workload, thus mitigating student burnout and improving satisfaction. Moreover, the CCP-AI-MWS exhibits adaptability to unexpected situations and minimizes disruptions to the current schedule. The findings present the potential of CCP-AI-MWS to transform scheduling practices in medical education, offering an efficient solution that could benefit medical schools worldwide.

CA-Live360: Crowd-assisted transcoding and delivery for live 360-degree video streaming

In the burgeoning field of digital media, the popularity of live 360-degree video has witnessed an upward trajectory, underpinned by its immersive experience. In live 360-degree video streaming services, transcoding enormous video tiles and providing low-latency services for global viewers require significantly more computing and communication resources than traditional live services. Inspired by the huge crowdsourcing computational resources available at crowd, this paper designs CA-Live360, a Crowd-Assisted Live 360-degree video streaming solution, in which transcoding and delivery are supported by crowdsourcing devices. Due to the dynamic availability of resources, it is challenging to achieve low-latency live services, while guaranteeing fair transcoding. Therefore, firstly an innovative fairness-guaranteed transcoding task assignment scheme is proposed. This scheme employs a novel Fair Bandit algorithm based on the combinatorial multi-armed bandit approach to achieve low-latency transcoding while considering time-varying available resources and fairness constraints. Secondly, to reduce delivery latency, we design a transcoding-aware delivery scheme, in which a provider–requester matching algorithm is introduced to realize effective requester scheduling. Real-world trace-driven experiments demonstrate the improved performance of the proposed CA-Live360 solution in terms of system delay and fairness.

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

Mobile edge computing (MEC) enables computation intensive applications in the Internet of Vehicles (IoV) to no longer be limited by device resources. However, the lack of an effective task scheduling strategy will seriously affect users’ quality of experience (QoE). In this paper, a task type-based task offloading and resource allocation strategy is proposed to reduce delay and energy consumption during task execution. First, we establish communication, computing, and system cost models based on task offloading schemes, and model the joint optimization problem of task offloading and resource allocation as a Markov decision process. The utility function is obtained based on the task completion rate and the system cost. Second, an algorithm framework based on multi-agent deep deterministic policy gradient (MADDPG) is designed to solve the difficulty that traditional single-agent reinforcement learning algorithms are difficult to converge in a dynamic environment. In distributed scenarios, the proposed framework can also reduce system costs while handling more tasks. Finally, federated learning is introduced in the training process to reduce the impact of non-IID data while protecting privacy. Simulation results show that the proposed algorithm can effectively improve system processing efficiency and reduce device energy consumption compared to the popular reinforcement learning algorithms.

A joint optimization method for multi-UAV deployment and task scheduling in mobile edge computing with large-scale mobile users

In the realm of multi-UAV-assisted mobile edge computing (MEC), the joint optimization of UAV deployment and task scheduling has emerged as a potent approach for mitigating energy consumption while ensuring high-quality services for mobile users. Nevertheless, the intricate interdependence between UAV deployment and task scheduling presents formidable challenges when implementing this scheme. In response, we introduce a novel methodology called the Dual-Stage Hybrid Strategy with Genetic-Simulated Annealing and Knowledge Sharing (DSHSGSK). DSHSGSK employs an alternating optimization strategy known as HS-GSK, supplemented by an elimination operator, to simultaneously optimize UAV deployment and task scheduling. In pursuit of heightened efficiency in UAV deployment and a more effective task scheduling strategy, we propose an innovative location encoding scheme for UAVs. To harness the local search capabilities of the Harmony search (HS) algorithm and the global search capabilities of the Gaining Sharing Knowledge based Algorithm (GSK), we introduce a coordination parameter (CP) to modulate the frequency of alternation between these two optimization techniques. Experimental results validate the superiority of our proposed method over other state-of-the-art algorithms for jointly optimizing multi-UAV deployment and task scheduling. Notably, our approach excels in terms of energy consumption, underscoring its exceptional performance.

Adaptive Scheduling of High-Availability Drone Swarms for Congestion Alleviation in Connected Automated Vehicles

The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles (CAVs) through comprehensive information gathering, sensing, device control, and data processing. Presently, ITS predominantly relies on sensors embedded in fixed infrastructure, notably Roadside Units (RSUs). However, RSUs are confined by coverage limitations and may encounter challenges in prompt emergency responses. On-demand resources, such as drones, present a viable option to supplement these deficiencies effectively. This paper introduces an approach where Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are integrated to formulate a high-availability drone swarm control and communication infrastructure framework, comprising the cloud layer, edge layer, and device layer. Drones confront limitations in flight duration attributed to battery limitations, posing a challenge in sustaining continuous monitoring of road conditions over extended periods. Effective drone scheduling stands as a promising solution to overcome these constraints. To tackle this issue, we initially utilized Graph WaveNet, a specialized graph neural network structure tailored for spatial-temporal graph modeling, for training a congestion prediction model using real-world dataset inputs. Building upon this, we further propose an algorithm for drone scheduling based on congestion prediction. Our simulation experiments using real-world data demonstrate that, compared to the baseline method, the proposed scheduling algorithm not only yielded superior scheduling gains but also mitigated drone idle rates.

Optimized intuitionistic fuzzy enriched honey badger algorithm for cloud network-based work load scheduling

The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum Honey Badger Algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters.