Effective Scheduling Research Articles

Improvement of the Performance of An Industrial Wringer

Objective: To maximize the machine's availability through periodic monitoring and propose design improvements to prevent future issues. Context: Overview of the recycling process and the role of the wringer in filtering recycled plastic. Problem Statement: Description of mechanical defects affecting the wringer, including imbalance due to bearing and rotor shaft deformation. Theoretical Analysis: Root Cause Identification, Impact of Imbalance, System Modelling and Simulation Experimental Studies and Strategy: - Practical experiments validate the theoretical and numerical findings and enhance the vibration analysis approach.- Effective scheduling of interventions and repairs is based on the machine's condition, ensuring continuous monitoring through vibration analysis devices. Results and Discussion: Explanation of the proposed modification, including the specific angle adjustment, Discussion on how the adjustment would reduce rotational speed and maintain high wringing performance.

EcoTrack: The Smart Waste Collection Navigator

This article presents EcoTrack, an innovative answer to today's trash issues related to management. Using state-of-the-art technology, EcoTrack offers predictive analytics for effective scheduling, optimised collection routes, and real-time monitoring of bin fill levels. EcoTrack builds a bridge between conventional methods and technological innovations, encouraging environmental stewardship and global sustainability by improving operational effectiveness and lowering environmental impact.

A joint work package sizing and scheduling problem considering resource constraints with a look-ahead heterogeneous reinforcement learning method

Effective work package sizing and project scheduling are crucial for construction project management. However, existing studies often address them as separate optimisation problems, neglecting the interactive nature of these processes. In practical scenarios with limited resources, there is an increasing demand for integrating work package sizing and project scheduling to enhance project management efficiency. This research aims to bridge this gap by developing an integer non-linear programming model that incorporates work package sizing and project scheduling while considering their interaction within a resource-constrained environment. Moreover, we introduce a novel look-ahead heterogeneous reinforcement learning approach that dynamically adjusts work package sizes and project schedules based on observed information at each decision step. A look-ahead sequential decision mechanism is proposed to effectively address the interdependencies and constraints inherent in the joint model. We extend the heterogeneous agent mirror learning technique to our problem to improve sample efficiency and ensure progressive enhancement of the joint policy. To evaluate our approach's effectiveness, we conduct experiments using datasets of 15, 30, 60, 90, and 120 tasks from Rangen and PSPLIB and validate our method in a real-world modular integrated construction project. The experimental results reveal that a joint model integrating work package sizing and project scheduling offers a more comprehensive understanding of their interplay, leading to better resource utilisation and improved project schedules. A comparative analysis against existing state-of-the-art reinforcement learning and priority rule-based methods further substantiates the superior effectiveness of our proposed approach, yielding an approximate 5% reduction in total project cost.

Allocating New Slots in a Multi-Airport System Based on Capacity Expansion

Over time, the rapid expansion of civil aviation infrastructure has led to the establishment of multi-airport systems (MASs) or Metroplex, where airports situated in close proximity form interconnected networks. Given that individual airport capacities often fall short of meeting flight scheduling demands, devising effective multi-airport flight scheduling methods becomes imperative. This article introduces a novel MAS slot expansion configuration framework centered on coupling terminal areas. In contrast to conventional airport capacity slot expansion approaches, this framework demonstrates superior configurational efficacy within respective airport terminal environments. The model outlined in this research identifies the terminal control sector as the pivotal resource node within the interconnected terminal area, aiming to maximize the total expanded slots while minimizing the overall unfairness among airports within the terminal airspace. Employing the ε-constraint method facilitates the transformation of the minimization objective into solvable constraint conditions. Subsequently, leveraging Beijing Metroplex as a case study, the research devises benchmark, single-airport, multi-airport minimum, and multi-airport maximum scenarios to compare and analyze configuration outcomes in terms of key resource allocation impacts and coupled resource utilization efficiencies. Ultimately, employing the AirTOp fast-time simulation model validates each scenario, demonstrating that the proposed configuration method yields reduced delay levels and fewer conflicts in simulation environments.

Stochastic scheduling and routing decisions in online meal delivery platforms with mixed force

This paper investigates stochastic scheduling and routing problems in the online meal delivery (OMD) service. The huge increase in meal delivery demand requires the service providers to construct a highly efficient logistics network to deal with a large-volume of time-sensitive and fluctuating fulfillment, often using inhouse and crowdsourced drivers to secure the ambitious service quality. We aim to address the problem of developping an effective scheduling and routing policy that can handle real-life situations. To this end, we first model the dynamic problem as a Markov Decision Process (MDP) and analyze the structural properties of the optimal policy. Then we propose four integrated approaches to solve the operational level scheduling and routing problem. In addition, we provide a continuous approximation formula to estimate the bounds of required fleet size for the inhouse drivers. Numerical experiments based on a real dataset show the effectiveness of the proposed solution approaches. We also obtain several managerial insights that can help decision makers in solving similar resource allocation problems in real-time.

Optimizing Factory Workers Work Shifts Scheduling Using Local Antimagic Vertex Coloring

Optimization of industrial processes, reduction of worker costs, and protection of workers well-being are significantly dependent on effective work shifts scheduling. This article introduces a new approach to work shifts scheduling by combining Local Antimagic Vertex Coloring. In the provided model, workers are diagrammed as edges, while work shifts are shown as vertices. In order to ensure that adjacent vertices are allocated distinct weights that correspond to non-overlapping work shifts, the Local Antimagic Vertex Coloring approach is used to assign uniform weights to vertices. To optimize the effectiveness of scheduling, regular graphs are used, offering a methodical and fair framework for administrating shift allocations. Adopting this comprehensive approach ensures fair allocation of responsibilities among all workers, minimizes conflicts over shift schedules, and meets operational requirements such as skill levels, shift lengths, and staff availability. By virtue of its versatility, this model may be tailored to various industrial environments, therefore enhancing both scheduling efficiency and staff satisfaction. The actual applications of this method clearly demonstrate its durability, identifying significant improvements in shift planning, reduction of scheduling errors, and a more effective work shifts management process. This paper proposes a comprehensive solution to the complex problem of scheduling work shifts for industrial workers, including both theoretical and practical benefits.

Wavelet and VMD enhanced traffic forecasting and scheduling method for edge cloud networks

With the proliferation of Intelligent applications, more and more organizations migrate their applications from cloud to edge cloud network to reduce the latency of applications and alleviate the workload of cloud. When the network congestion occurs, network monitoring and detection system may disable, and edge cloud network will be vulnerable to be attacked. Traffic forecasting can identify the traffic patterns in advance, and dynamically allocate network resources to decrease latency of applications and avoid security risks caused by network congestion. Therefore, we propose a network scheduling framework WVNF (Wavelet VMD Based Network Flow Management) based on traffic prediction, which utilizes neural networks to forecast network traffic and deploys route strategies to optimize network scheduling. Specifically, in order to accurately forecast network traffic, we propose a neural network model, named TSWNet (Traffic Sequence Wavelet Network). TSWNet uses VMD (variational mode decomposition) to decompose time series and extract signal structure information on different time scales, and adopts wavelet transformation to extract the local and global features of the traffic sequence in the time and frequency domain. In addition, we model this traffic scheduling problem and propose a route strategy, which utilizes the result of TSWNet to find the best path. In extensive tests, TSWNet significantly outperformed existing models, reducing MSE and MAE by up to 48.8% and 27.8% respectively, demonstrating its effective traffic prediction and network scheduling capabilities.

Reliable ships: A fuzzy FMEA based risk analysis on four-ram type hydraulic steering system

Hydraulic steering systems are vital for safe and efficient navigation in maritime vessels. Failures in these systems can pose serious operational and safety risks. This study uses fuzzy Failure Modes and Effects Analysis to assess risks in a four-ram hydraulic steering system commonly used in ships. A total of 47 failure modes of the hydraulic system were identified and analyzed using domain expert input to improve the accuracy of the risk assessment. Fuzzy input and output variables are developed, followed by the construction of membership functions, a rule base, and an inference engine to compute the Fuzzy Risk Priority Numbers (FRPN). Based on the calculated FRPN values, all failure modes are ranked, with the top ten highlighted and detailed in the Discussion chapter, which concludes with recommendations for control and prevention. It was found that the most critical failure mode is “insufficient system pressure” due to defective hoses, pipes, and connectors (FRPN 6.59). The results of this study provide practical benefits to the maritime industry by enabling more precise risk identification and mitigation strategies. This approach facilitates effective maintenance scheduling, enhances system reliability, reduces operational downtime and maintenance costs, and ultimately improves the safety and efficiency of ship navigation systems.

Enhancing productivity and profitability of spring maize (Zea mays) in Uttarakhand's Tarai region through effective weed management practices and irrigation scheduling

A field experiment was conducted during the spring seasons of 2022 and 2023 at Norman E Borlaug Crop Research Centre, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand to enhance maize yield and profitability. The experiment was conducted a split-plot design (SPD) with two irrigation scheduling treatments, viz. irrigation applied at an IW/CPE ratio of 0.8 (I1); and 1.2 (I2) in main plot and 8 weed management treatments, viz. W1, atrazine 1000 g/ha; W2, tembotrione 120 g/ha; W3, topramezone 25.2 g/ha; W4, atrazine 1000 g/ ha followed by hand-weeding at 35 DAS (days after sowing); W5, atrazine 1000 g/ha followed by tembotrione 120 g/ha; W6, atrazine 1000 g/ha followed by topramezone 25.2 g/ha; W7, weed-free; and W8, weedy check in sub-plots. Irrigation applied at IW/CPE ratio of 1.2 significantly improved the maize productivity and profitability, yielding higher net returns (₹66,206.9/ha) and grain yield (6.1 t/ha). Among the herbicidal treatments, atrazine 1000 g/ha followed by topramezone 25.2 g/ha recorded the highest weed control efficiency (80%) and maize grain yield (6.3 t/ ha). The percentage net monetary return was in the following sequence: W6 (100%) > W7 (92.4%) > W5 (91.8%) > W4 (89.6%) > W3 (81.5%) > W2 (78.2%) > W1 (67.6%) > W8 (46.4%). The percentage gain in grain yield was recorded in the following sequence: W7 (100%) > W6 (91.4%) > W5 (87.4%) > W4 (86.4%) > W3 (84.1%) > W2 (81.1%) > W1 (71.5%) > W8 (52.5%). In conclusion, optimizing irrigation to IW/CPE ratio of 1.2 and employing weed management practice atrazine (1000 g/ha) followed by topramezone (25.2 g/ha) application, can significantly enhance spring maize yield, weed control efficiency and economic returns in Uttarakhand's Tarai region.

Interspecies interactions alter the antibiotic sensitivity of Pseudomonas aeruginosa.

Polymicrobial infections are infections that are caused by multiple pathogens and are common in patients with cystic fibrosis (CF). Although polymicrobial infections are associated with poor treatment responses in CF, the effects of the ecological interactions between co-infecting pathogens on antibiotic sensitivity and treatment outcome are poorly characterized. To this end, we systematically quantified the impact of these effects on the antibiotic sensitivity of Pseudomonas aeruginosa for nine antibiotics in medium conditioned by 13 secondary cystic fibrosis-associated bacterial and fungal pathogens through time-kill assays. We fitted pharmacodynamic models to these kill curves for each antibiotic-species combination and found that interspecies interactions changing the antibiotic sensitivity of P. aeruginosa are abundant. Interactions that lower antibiotic sensitivity are more common than those that increase it, with generally more substantial reductions than increases in sensitivity. For a selection of co-infecting species, we performed pharmacokinetic-pharmacodynamic modeling of P. aeruginosa treatment. We predicted that interspecies interactions can either improve or reduce treatment response to the extent that treatment is rendered ineffective from a previously effective antibiotic dosing schedule and vice versa. In summary, we show that quantifying the ecological interaction effects as pharmacodynamic parameters is necessary to determine the abundance and the extent to which these interactions affect antibiotic sensitivity in polymicrobial infections.IMPORTANCEIn cystic fibrosis (CF) patients, chronic respiratory tract infections are often polymicrobial, involving multiple pathogens simultaneously. Polymicrobial infections are difficult to treat as they often respond unexpectedly to antibiotic treatment, which might possibly be explained because co-infecting pathogens can influence each other's antibiotic sensitivity, but it is unknown to what extent such effects occur. To investigate this, we systematically quantified the impact of co-infecting species on antibiotic sensitivity, focusing on P. aeruginosa, a common CF pathogen. We studied for a large set co-infecting species and antibiotics whether changes in antibiotic response occur. Based on these experiments, we used mathematical modeling to simulate P. aeruginosa's response to colistin and tobramycin treatment in the presence of multiple pathogens. This study offers comprehensive data on altered antibiotic sensitivity of P. aeruginosa in polymicrobial infections, serves as a foundation for optimizing treatment of such infections, and consolidates the importance of considering co-infecting pathogens.

Raising user awareness through unsupervised clustering of energy consumption habits

Climate change mitigation requires the urgent reduction of Greenhouse Gas (GHG) emissions, with the building sector as a significant contributor. This study develops a system to identify appliance profiles from smart meter data, enhancing energy consumption awareness and management. These profiles provide valuable insights into users’ consumption patterns and habits, enabling more accurate load consumption prediction and effective appliance scheduling strategies.The proposed approach employs feature extraction techniques to characterise energy consumption profiles, followed by k-means clustering to identify distinct appliance profiles. Eleven representative features are identified, offering comprehensive insights into occupants’ energy usage habits.The evaluation with real-case data shows accurate consumption cycle approximations for each profile, with errors consistently below 10%. Performance assessment using classification metrics indicates well-characterised and representative profiles, outperforming state-of-the-art methods with average values exceeding 0.88 for all considered metrics. This system helps raise occupants’ awareness of appliance energy usage and facilitates optimised scheduling through an Energy Management System (EMS). By promoting more efficient energy consumption, the proposed approach contributes to overall energy reduction and, consequently, lower GHG emissions in the building sector.

Implementation and Evaluation of a Smart Machine Monitoring System under Industry 4.0 Concept

Production planning and control is essential in industrial manufacturing, ensuring efficient resource allocation and process management. Industry 4.0 introduces advanced technologies like cyber physical systems (CPS), artificial intelligence (AI), and internet of things (IoT) to effectively manage and monitor manufacturing operations. However, integrating these technologies into existing machinery, particularly for small and medium-sized enterprises (SMEs), poses challenges due to complexity and cost. The present study addresses this gap by designing and implementing a Smart Machine Monitoring System (SMMS) compatible with existing machinery such as computer numerical control and special purpose machines. The SMMS integrates IoT-based systems with AI algorithms to enhance machine tool utilization through effective planning, scheduling, and real-time monitoring. Through a nine-month case study in the shackle bolt manufacturing section, it was tested and compared to an Enterprise Resource Planning (ERP)-based system to assess its performance. Results showed significant improvements in production output, machine utilization rates, labor efficiency, and overall manufacturing costs. In conclusion, this study contributes to the body of knowledge on practical Industry 4.0 implementations for SMEs, offering insights into cost-effective solutions for enhancing operational efficiency and resource utilization in manufacturing environments.

Assessing forecast performance of daily reference evapotranspiration: A comparison of equations, machine and deep learning using weather forecasts

An accurate forecast of short-term reference evapotranspiration (ET0) is crucial for effective farm and irrigation scheduling management. The techniques of forecasting ET0 have progressed from empirical equations to artificial intelligence-based models. However, gaps have remained in comprehensively evaluating the ET0 forecast performance discrepancies between various methods based on weather forecasts in addition to historical meteorological data, as well as investigating the effect of the amount of input variables on ET0 forecast accuracy. Here, the study evaluated the forecast accuracy of five conventional equations calibrated and six machine learning (ML) and deep learning (DL) models in predicting daily ET0 for a lead time of 1–7 days in the North China Plain (NCP). Comparative evaluations indicate that the Analytical Penman-Monteith (APM) is the optimum empirical equation for forecasting ET0, but its accuracy decreases as lead times increase. Conversely, ML and DL models consistently outperform empirical equations, characterized by lower mean absolute error (MAE) and root mean square error (RMSE) as well as correlation coefficient and Nash-Sutcliffe efficiency coefficient approaching 1 across all lead times. Notably, the Bidirectional Long short-term memory (Bi-LSTM) model outperforms other models, maintaining robust and effective forecast performance even on day 7. Moreover, with the increasing input variables, combining weather forecasts and historical meteorological data, the forecast performance of all models is improved, with significant enhancement observed in the Bi-LSTM model. This work provides valuable insights for choosing appropriate models to forecast ET0, holding essential in regional managing farm and irrigation systems.

Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This article presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. First, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at runtime while significantly reducing computational complexity. Moreover, to overcome the “short-sightedness” of single-step performance prediction, we extend ADPS to ADPS-m ( m ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H ( H eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes and ADPS-H-m ( m ulti-step prediction for H eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of \(-\) 76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. The ten-loop case indicates the effectiveness and scalability of the proposed approaches.

A hierarchical adaptive federated reinforcement learning for efficient resource allocation and task scheduling in hierarchical IoT network

The increasing demand for processing numerous data from IoT devices in a hierarchical IoT network drives researchers to propose different resource allocation methods in the edge hosts efficiently. Traditional approaches often compromise on one of these aspects: either prioritizing local decision-making at the edge, which lacks global system insights or centralizing decisions in cloud systems, which raises privacy concerns. Additionally, most solutions do not consider scheduling tasks at the same time to effectively complete the prioritized task accordingly. This study introduces the hierarchical adaptive federated reinforcement learning (HAFedRL) framework for robust resource allocation and task scheduling in hierarchical IoT networks. At the local edge host level, a primal–dual update based deep deterministic policy gradient (DDPG) method is introduced for effective individual task resource allocation and scheduling. Concurrently, the central server utilizes an adaptive multi-objective policy gradient (AMOPG) which integrates a multi-objective policy adaptation (MOPA) with dynamic federated reward aggregation (DFRA) method to allocate resources across connected edge hosts. An adaptive learning rate modulation (ALRM) is proposed for faster convergence and to ensure high performance output from HAFedRL. Our proposed HAFedRL enables the effective integration of reward from edge hosts, ensuring the alignment of local and global optimization goals. The experimental results of HAFedRL showcase its efficacy in improving system-wide utility, average task completion rate, and optimizing resource utilization, establishing it as a robust solution for hierarchical IoT networks.

Optimizing Swarm UAV Scheduling for Efficiency in Complex Operations and Collision Avoidance

The use of drones or Unmanned Aerial Vehicle (UAVs) has rapidly evolved across various civil, public, and military applications over the past two decades. Military UAVs have long been used for border surveillance, while civilian UAVs can monitor traffic, provide disaster alerts, and deliver medical supplies. Swarm UAVs, inspired by natural animal colonies such as birds and bees, enable complex flight coordination in disaster situations. To optimize operations, the implementation of scheduling methods for swarm drones is crucial. This method involves scheduling flights based on mission priorities, estimated time of arrival, and drone battery conditions. Effective scheduling ensures operational efficiency and flight safety of drones.

Analysis of luminance reduction based on the operating durations of emergency exit lights

Emergency exit lights (EELs) are essential for providing directional guidance to evacuees during emergencies. Although significant advancements have been achieved in the methods of manufacturing and installing EELs, as guided by researchers and relevant regulations, the maintenance and management of these systems have been relatively neglected, with no specific regulations addressing these aspects. Consequently, several EELs have become outdated, which raises concerns regarding their reliability and effectiveness in emergencies because the evacuation times could potentially increase. In this study, a regression equation was developed to predict the luminance of EELs, which was then used to determine their service life. The findings from the regression equation indicate that the service life of EELs in South Korea should not exceed 10 years. By predicting luminance reduction, effective maintenance schedules can be established. Moreover, maintaining EELs through a data-driven system can ensure high reliability for users.

Energy-efficient and reliable coordinated scheduling for water distribution systems: enhancing hydraulic conditions and water quality

ABSTRACT Multi-objective operation optimization of water distribution systems (WDSs) in large metropolitan areas is essential; however, it is complex and time-consuming. Effective and reliable scheduling of WDSs can significantly enhance the efficiency and reliability of urban water resources management, which requires further research. This paper develops an optimization method combining genetic algorithm and multiple criteria analysis that coordinates energy conservation, hydraulic condition improvement, and water age optimization in WDSs. Results showed that the optimal scheduling method could achieve a 23.0, 16.7, and 2.5% decrease in energy consumption, and water supply volume with unsatisfied pressure, and average water age, respectively. To achieve optimal results, this study indicated that it is crucial to properly allocate water supply volume and pressure across multiple drinking water treatment plants. This finding provides important guidance for water utilities in scheduling. Furthermore, the emergency scenario analysis shows that the newly proposed method can significantly enhance the social and economic sustainability of WDSs through the coordinated optimization of energy consumption, hydraulic conditions, and water age.

Reflections on co-researching AI literacy

Students as Partners (SaP) approaches have gained more and more traction in higher education in recent years (Dai & Matthews, 2022). Rooted in values such as reciprocity and shared responsibility, SaP can offer opportunities for internationalizing the curriculum and departing from traditional teacher-student hierarchies (Green & Baxter, 2022). This case study focuses on a SaP project involving international students and their English for Academic Purposes (EAP) teacher, which investigated artificial intelligence (AI) literacy during a UK pre-sessional course in summer 2023. The project identified that learning about the limitations of AI, in addition to developing skills for effective prompt writing, was beneficial to students (Partridge et al, 2023). This case study reflects on the challenges and benefits of SaP for both students and the teacher using the Advance HE (2016) Framework for Student Engagement Through Partnership. Based on these reflections, the case study offers recommendations for future SaP projects including effective scheduling, defining roles, engaging in continual reflection, and formally recognising student input.

Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment

Microgrids (MG) are small-scale energy systems that use distributed energy storage and sources. Hybrid microgrids are transforming energy management by incorporating various energy resources like wind, solar, and battery storage. Effective scheduling of this resource is vital to minimize the costs and maximize energy autonomy. Advanced scheduling algorithm optimizes the operation of hybrid microgrids, which dynamically adjusts the energy consumption and generation to satisfy the demand while ensuring power balancing. This scheduling strategy has been instrumental in improving the sustainability and resilience of MGS, which paves the way for an environmentally friendly and more reliable energy future. They can operate on islanded or grid-connected modes. The optimization of hybrid MG scheduling is paramount in the field of post-island management to ensure effective energy sustainability and distribution. Using metaheuristic approaches like simulated annealing or genetic algorithms allows the finetuning of scheduling parameters to increase energy utilization while reducing environmental impact and costs. Therefore, the study presents a Real-Time Scheduling for Post Islanding Energy Management using African Vulture Optimization Algorithm (RTSPIEM-AVOA) in Hybrid microgrid environment. The RTSPIEM-AVOA approach is utilized to improve its functioning by determining the most efficient scheduling of the installed generation unit. The AVOA can handle complex optimization issues while avoiding local optima solutions because of the balance among the exploitation and exploration stages. A microturbine (MT) system, battery storage, a fuel cell (FC), photovoltaic (PV), and wind turbine (WT) make up the suggested MG system. This work looks at three scenarios: PV and WT operating at normal generation, PV and WT operating at their maximum power, and WT operating at its rated power. Consider the two objective functions of minimizing pollutant emissions and reducing operational costs. According to the experimental results, the RTSPIEM-AVOA technique outperforms other models in microgrid scheduling by efficiently optimizing it to satisfy the community's changing needs while transitioning to a greener and more sustainable energy future.