Scheduling Of Applications Research Articles

Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding

In developing countries like South Africa, users experienced more than 1 030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national grid. Residential homes that cannot afford to take actions to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand involuntarily. This study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load shedding. To start with, we predict the load shedding stages that are used as input for the optimal strategies by using the K-Nearest Neighbour (KNN) algorithm. Based on an accurate forecast of the future load shedding patterns, we formulate the residents’ inconvenience and the loss of power supply probability during load shedding as the objective function. When solving the multi-objective optimisation problem, four different strategies to fight against load shedding are identified, namely (1) optimal home appliance scheduling (HAS) under load shedding; (2) optimal HAS supported by solar panels; (3) optimal HAS supported by batteries, and (4) optimal HAS supported by the solar home system with both solar panels and batteries. Among these strategies, appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels, eliminates the loss of power supply probability and reduces the inconvenience by 92% when tested under the South African load shedding cases in 2023.

Network Planning Application for Scheduling of Marine Diesel Engine General Overhaul Activities

There are many sub-tasks and tools in general overhaul activities, so that a planned and systematic arrangement are needed. In systematic general overhaul planning can use the Network Planning method. Scheduling by the Network Planning method describes a task into a network. The Network Planning has two commonly used techniques, namely CPM (Critical Method Path) and PERT (Program Evaluation and Review Technique). Therefore, in this study, scheduling will be carried out by the Network Planning method, CPM and PERT techniques. By using network planning in overhaul activities, it is hoped that it will be able to identify deviations and critical paths in the sequence of work processes so as to prevent delays in completing work.

Application timing and duration of LED and HPS supplements differentially influence yield, nutrient bioaccumulation, and light use efficiency of greenhouse basil across seasons.

Three primary factors that impact plant growth and development are light quantity, quality, and duration. Commercial growers can manipulate these parameters using light-emitting diodes (LEDs) to optimize biomass yield and plant quality. There is significant potential to synergize supplemental lighting (SL) parameters with seasonal variation of ambient sunlight to optimize crop light use efficiency (LUE), which could increase biomass while reducing SL electricity costs. To determine the best lighting characteristics and durations for different crops, particularly for enhancing the yield and nutritional quality of high-value specialty crops produced in greenhouses during the winter, a thorough efficacy comparison of progressive incremental daily light integrals (DLIs) using LED and high-pressure sodium (HPS) sources is required. The purpose of this study was to compare the effects of differential application timing and DLIs of supplemental blue (B)/red (R) narrowband wavelengths from LED lighting systems and HPS lamps on greenhouse hydroponic basil (Ocimum basilicum var. 'Genovese') production. We assessed edible biomass, nutrient bioaccumulation, and LUE. Nine light treatments included: one non-supplemented natural light (NL) control, two end-of-day (EOD) HPS treatments applied for 6h and 12h, five EOD 20B/80R LED treatments applied for 3h, 6h, 9h, 12h, 18h, and one continuous LED treatment (24h). Each SL treatment provided 100 µmol·m-2·s-1. The DLI of the NL control averaged 9.9mol·m-2·d-1 during the growth period (ranging from 4 to 20mol·m-2·d-1). SL treatments and growing seasons significantly impacted biomass and nutrient bioaccumulation; some SL treatments had lower yields than the non-supplemented NL control. January growing season produced the lowest fresh mass (FM) and dry mass (DM) values compared to November, which had the highest. Mineral analyses revealed that both growing seasons and lighting types impacted macro and micronutrient accumulation. Additionally, the efficiency of each treatment in converting electrical energy into biomass varied greatly. EOD supplements using LED and HPS lighting systems both have merits for efficiently optimizing yield and nutrient accumulation in basil; however, biomass and nutrient tissue concentrations highly depend on seasonal variation in ambient sunlight in conjunction with a supplement's spectral quality, DLI, and application schedule.

Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments

Edge/fog computing, as a distributed computing paradigm, satisfies the low-latency requirements of ever-increasing number of IoT applications and has become the mainstream computing paradigm behind IoT applications. However, because large number of IoT applications require execution on the edge/fog resources, the servers may be overloaded. Hence, it may disrupt the edge/fog servers and also negatively affect IoT applications’ response time. Moreover, many IoT applications are composed of dependent components incurring extra constraints for their execution. Besides, edge/fog computing environments and IoT applications are inherently dynamic and stochastic. Thus, efficient and adaptive scheduling of IoT applications in heterogeneous edge/fog computing environments is of paramount importance. However, limited computational resources on edge/fog servers imposes an extra burden for applying optimal but computationally demanding techniques. To overcome these challenges, we propose a Deep Reinforcement Learning-based IoT application Scheduling algorithm, called DRLIS to adaptively and efficiently optimize the response time of heterogeneous IoT applications and balance the load of the edge/fog servers. We implemented DRLIS as a practical scheduler in the FogBus2 function-as-a-service framework for creating an edge–fog–cloud integrated serverless computing environment. Results obtained from extensive experiments show that DRLIS significantly reduces the execution cost of IoT applications by up to 55%, 37%, and 50% in terms of load balancing, response time, and weighted cost, respectively, compared with metaheuristic algorithms and other reinforcement learning techniques.

Robust and efficient task scheduling for robotics applications with reinforcement learning

Effective task scheduling can significantly impact on performance, productivity, and profitability in many real-world settings, such as production lines, logistics, and transportation systems. Traditional approaches to task scheduling rely on heuristics or simple rule-based methods. However, with the emergence of machine learning and artificial intelligence, there is growing interest in using these methods to optimize task scheduling. In particular, reinforcement learning is a promising task scheduling approach, because it can learn from experience and adapt to changing conditions. One step often missed or neglected is choosing optimal algorithm parameters and different ways the environment could be implemented. The study analyzes the performance possibilities of task scheduling using reinforcement learning. The deep analysis allows to select highly efficient environment models and Q-learning parameters. Moreover, automatic selection based on optimization algorithms has been proposed. Regardless of the selected optimal parameters, the resilience to environmental changes seems poor. The deducted analysis motivated the Authors to develop a novel Hybrid Q-learning approach. It allows to provide superior efficiency regardless of the environmental parameters.

Evaluation of fluopyram for disease and root knot nematode control along with yield response on peanut

Efficacy of in-furrow application of fluopyram + imidacloprid alone or followed by fluopyram + prothioconazole at-pegging and aldicarb for managing peanut root-knot nematode populations, root and pod galling due to nematodes, and yield, along with non-target effects on leaf spot diseases and stem rot were compared on the root-knot susceptible cultivar Georgia-06G over seven years on an irrigated site with an established population of Meloidogyne arenaria race 1. In all years, fungicides were applied to all plots following a Peanut Rx application schedule. Root-knot nematode populations, nematode damage, and yield along with leaf spot severity and stem rot incidence significantly differed among years; except for nematode populations, these measured variables also differed by nematicide treatment. Averaged over years, galling and root damage was significantly reduced with aldicarb compared to the no-nematicide control but not with either of the fluopyram treatments. While similar late-season root-knot nematode populations were noted for all nematicide programs and the non-treated control, pooled over years, significant yield gains were obtained with aldicarb and fluopyram programs with the two-treatment program providing greater yields than the aldicarb treatment. Leaf spot severity, averaged over years, was similar in aldicarb and control treatments; however, both fluopyram treatments had significantly lower leaf spot severity than the aldicarb treatment. Stem rot incidence did not differ among nematicide treatments. In addition, pooled over years, there was a negative relationship between juvenile populations and yield, but this was relatively weak. Weather, particularly extreme rain conditions, impacted yield directly as well as indirectly by affecting leaf spot severity and nematode activity.

Feasibility/Flexibility-based optimization for process design and operations

This paper provides an overview of concepts and computational approaches for the evaluation of feasibility/flexibility and how they can be used for process design and process operations optimization. It emphasizes more recent topics in this area, in particular feasible region evaluation, feasibility-based optimization, and optimization with flexibility requirements. The description of process feasibility and the feasibility-based optimization problem are presented as a way to efficiently incorporate multiple constraints and avoid unnecessary exploration of the infeasible space in the black-box optimization context. The relationship between flexibility analysis and robust optimization is also highlighted, and opportunities in exploring synergies therein are outlined. Applications in pharmaceutical design and process scheduling are used to provide context in the utilization of the presented approaches.

Double-layer home energy management strategy for increasing PV self-consumption and cost reduction through appliances scheduling, EV, and storage

Maximizing self-consumption of the photovoltaic (PV) generation is an important factor to increase the penetration of PV in the residential grid. It can improve PV system profitability, save energy and reduce grid stress. This study proposes a double-layer home energy management strategy to increase PV self-consumption and reduce household electricity costs. The first layer involves rescheduling shiftable appliances to operate during surplus PV generation hours, while the second layer employs a multi-objective energy management strategy based on Jaya and particle swarm optimization (PSO) algorithms to optimize power exchange between the energy storage system (ESS) and electric vehicle (EV), with smart home. Six different scenarios are simulated to investigate the role of household appliances, ESS, and EV technology in increasing PV self-consumption. Results scheduling showed that the proposed strategy can increase self-consumption and reduce grid consumption by 17–41% and 27–78%, respectively, depending on the proposed scenarios. The double-layer strategy is found to be more effective than the single-layer approach, with significant economic benefits. The proposed method provides an efficient solution for improving PV system profitability, saving energy, and reducing grid stress. Thus, this study contributes to the development of sustainable energy management systems, paving the way for future research in this area.

An integrated residence smart grid system with enhanced energy management for batteries

Residential consumers can efficiently engage in demand response programs with the help of home energy management systems (HEMSs). The best scheduling of gas, oil, coal, and uranium appliances has yet to be considered by traditional HEMSs, which exclusively control domestic electric devices to lower the cost of electricity usage. The associated demand response (DR) for smart homes is then adjusted to lessen the difference between predicted and actual demand after comparing the actual price to this forecast. Data acquired by Nordpool and publicly available energy information have been used to test the proposed strategy about price demand forecast and DR management. The function is to reduce energy costs for the home and the dissatisfaction brought on by moving, cutting, and replacing loads by changing prices over time. Case studies with accurate data show that the suggested strategy can return on investment bills by up to 85% while maintaining their satisfaction.

P2P power trading of nanogrids for power management in consideration of battery lifetime of ESS

This study presents a peer-to-peer (P2P) trading strategy for power management of nanogrids with renewable energy sources (RESs), taking battery lifetime of energy storage system (ESS) into consideration. Nanogrid requires more analytical power management as individual control of each electric appliance should be carried out, considering its rated power. With the increasing penetration of distributed energy resources, P2P trading can be applied to nanogrids equipped with RESs and ESSs. For the power management of a nanogrid, multi-objective optimization is used to minimize grid power consumption, electricity cost, and delay of using electric appliances in appliance scheduling. In this paper, a battery aging model or degradation cost model is considered in the multi-objective optimization to prolong the ESS lifetime. The electricity from RESs and ESSs used for P2P trading is determined to increase the profitability of nanogrids. Depending on the amount of electricity allocated to P2P trading, a nanogrid can be a seller or buyer in P2P trading. P2P trading is carried out based on the cooperative game theory to maximize the welfare of both sellers and buyers. As compared to power management involving P2P trading without considering ESS lifetime, the proposed one in consideration of ESS lifetime decreases the electricity cost of nanogrid by 2.86% on average, and the degradation cost of ESS by 32.75% on average.

Meta-heuristic optimization based cost efficient demand-side management for sustainable smart communities

The monetary value of grid electricity is inflating significantly due to the staggeringly broadening gap between electricity demand and supply, which arise from the unceasing growth of consumption demands. Although heuristic optimization based demand side management has its merits, incorporating Ant Colony Optimization remains disputable due to its tendency to converge at a local optimum. Therefore, this work presents a hybridized algorithm of Ant Colony Optimization and Genetic Algorithm, which alleviates the drawbacks of Ant Colony Optimization through Genetic Algorithm. The proposed work promotes sustainable energy utilization simultaneously with demand-side optimization. The performance of the proposed algorithm is compared with no scheduling instance, Ant Colony Optimization based energy management controller, and mutated Ant Colony Optimization based appliance scheduling. The proposed algorithm successfully curtails 35.4% from community peak load demand and achieves 33.67% cumulative cost saving for the community. In other words, comparative analysis confirms the supremacy of the proposed algorithm in terms of minimizing peak load, total cost, peak-to-average ratio, and waiting time, while providing prevailing insights about proposed algorithm as a sustainable solution approach.

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC’s resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor–critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network’s capacity.

A Framework for QoS Parameters-Based Scheduling for IoT Applications on Fog Environments

A Framework for QoS Parameters-Based Scheduling for IoT Applications on Fog Environments

A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.

TIVC: An Efficient Local Search Algorithm for Minimum Vertex Cover in Large Graphs.

The minimum vertex cover (MVC) problem is a canonical NP-hard combinatorial optimization problem aiming to find the smallest set of vertices such that every edge has at least one endpoint in the set. This problem has extensive applications in cybersecurity, scheduling, and monitoring link failures in wireless sensor networks (WSNs). Numerous local search algorithms have been proposed to obtain "good" vertex coverage. However, due to the NP-hard nature, it is challenging to efficiently solve the MVC problem, especially on large graphs. In this paper, we propose an efficient local search algorithm for MVC called TIVC, which is based on two main ideas: a 3-improvements (TI) framework with a tiny perturbation and edge selection strategy. We conducted experiments on real-world large instances of a massive graph benchmark. Compared with three state-of-the-art MVC algorithms, TIVC shows superior performance in accuracy and possesses a remarkable ability to identify significantly smaller vertex covers on many graphs.

A sustainable approach for demand side management considering demand response and renewable energy in smart grids

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.

Variable Neighborhood Search for precedence-constrained tasks optimization on heterogeneous systems

Supercomputing power is one of the fundamental pillars of the digital society, which depends on the accurate scheduling of parallel applications in High-Performance Computing (HPC) centers to minimize computing times. However, precedence-constraint task scheduling is a well-known NP-Hard optimization problem, and no optimal polynomial-time algorithm exists to solve it. Therefore, as accurate as possible to the optimal values, heuristic algorithms are of relevant interest. Our new scheduling proposal name is EFT-GVNS, which stands for Earliest Finish Time - General Variable Neighborhood Search. EFT-GVNS uses a Composite Local Search (CLS), making our proposal more efficient than traditional GVNS. EFT-GVNS accuracy against four high-performance algorithms from the state-of-the-art (EDA, EFT-ILS, GRASP-CPA, MPQGA) and one reference algorithm in the literature (HEFT) is studied. Experimental results over four real-world applications (Fpppp, LIGO, Robot, Sparse) and 14 synthetic instances from the literature show that EFT-GVNS outperforms in terms of the median achieved results, with a global improvement of 37.6%, 27.4%, 17.8%, 6.1%, 2.2% to HEFT, EDA, EFT-ILS, GRASP-CPA, and MPQGA, respectively. EFT-GVNS achieves all the 14 optimal values of the synthetic benchmark.

Cost-Efficient Fault-Tolerant Workflow Scheduling for Deadline-Constrained Microservice-Based Applications in Clouds

Microservices are becoming increasingly popular in the construction of cloud applications. On the basis of containers, microservice instances can be implemented with high scalability and maintainability. Due to the need of ensuring various quality of service (QoS) requirements and the two-layer resource structure of containers and virtual machines (VMs), microservice workflow scheduling in clouds is a challenging problem to address. This paper proposes a heuristic algorithm GSMS to minimize execution cost of a microservice-based workflow application while satisfying deadline and reliability constraints. GSMS adopts a greedy fault-tolerant scheduling strategy for replicas of each task to select appropriate resources that meet the sub-deadline and minimize the cost until the sub-reliability is guaranteed. Furthermore, a resource adjustment strategy is incorporated into GSMS to further improve resource utilization. By conducting extensive experiments with several realistic workflow applications, in comparison with existing algorithms, the effectiveness and efficiency of GSMS in achieving lower execution cost and meeting deadline and reliability requirements are validated.

Design and Implementation of High QoS 3D-NoC using Modified Double Particle Swarm Optimization on FPGA

One technique to overcome the exponential growth bottleneck is to increase the number of cores on a processor, although having too many cores might cause issues including chip overheating and communication blockage. The problem of the communication bottleneck on the chip is presently effectively resolved by networks-on-chip (NoC). A 3D stack of chips is now possible, thanks to recent developments in IC manufacturing techniques, enabling to reduce of chip area while increasing chip throughput and reducing power consumption. The automated process associated with mapping applications to form three-dimensional NoC architectures is a significant new path in 3D NoC research. This work proposes a 3D NoC partitioning approach that can identify the 3D NoC region that has to be mapped. A double particle swarm optimization (DPSO) inspired algorithmic technique, which may combine the characteristics having neighbourhood search and genetic architectures, also addresses the challenge of a particle swarm algorithm descending into local optimal solutions. Experimental evidence supports the claim that this hybrid optimization algorithm based on Double Particle Swarm Optimisation outperforms the conventional heuristic technique in terms of output rate and loss in energy. The findings demonstrate that in a network of the same size, the newly introduced router delivers the lowest loss on the longest path. Three factors, namely energy, latency or delay, and throughput, are compared between the suggested 3D mesh ONoC and its 2D version. When comparing power consumption between 3D ONoC and its electronic and 2D equivalents, which both have 512 IP cores, it may save roughly 79.9% of the energy used by the electronic counterpart and 24.3% of the energy used by the latter. The network efficiency of the 3D mesh ONoC is simulated by DPSO in a variety of configurations. The outcomes also demonstrate an increase in performance over the 2D ONoC. As a flexible communication solution, Network-On-Chips (NoCs) have been frequently employed in the development of multiprocessor system-on-chips (MPSoCs). By outsourcing their communication activities, NoCs permit on-chip Intellectual Property (IP) cores to communicate with one another and function at a better level. The important components in assigning application duties, distributing the work to the IPs, and coordinating communication among them are mapping and scheduling methods. This study aims to present an entirely advanced form of research in the area of 3D NoC mapping and scheduling applications, grouping the results according to various parameters and offering several suggestions for further research.

Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling

Many evolutionary artificial intelligence (AI) technologies have been applied to assist with job scheduling in manufacturing. One of the main approaches is genetic algorithms (GAs). However, due to their complexity, users may need help understanding or communicating GA applications, preventing their widespread acceptance among factory workers. The concept of explainable AI (XAI) has been proposed to address this issue. This study reviews existing XAI techniques for explaining GA applications in job scheduling and summarizes the problems existing XAI techniques face. Several novel XAI techniques are proposed to solve these problems, including decision tree-based interpretation, dynamic transformation and contribution diagrams, and improved bar charts. To illustrate the effectiveness of the proposed methodology, it is applied to a case from the literature. According to the experimental results, the proposed methodology can compensate for the deficiencies of existing XAI methods in handling high-dimensional data and visualizing the contribution of feasible solutions, thereby satisfying all the requirements for an effective XAI technique for explaining GA applications in job scheduling. Additionally, the proposed methodology can be easily extended to explain other evolutionary AI applications in job scheduling, such as ant colony optimization (ACO), particle swarm optimization (PSO), and the artificial bee colony (ABC) algorithm.