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With the rapid growth of social economy, the requirements for resource utilization are becoming higher and higher. How to effectively solve the problem of energy shortage has become a hot topic. However, there are still many deficiencies in the current resource scheduling algorithm. For example, for different types of satellites with different needs and requirements, their performance indicators are different, and in the satellite communication system, the interaction between multiple nodes needs to be considered. Therefore, this paper studies the artificial intelligence algorithm, and proposes an improved particle complementary scheduling strategy for a single user in view of the shortcomings of the existing particle swarm optimization (PSO) algorithm. On this basis, this paper applies this method to evaluate the system performance and verify its effectiveness. The verification results show that the utilization efficiency of the satellite communication system resource scheduling algorithm is above 93%. [ABSTRACT FROM AUTHOR]

The university course scheduling problem (UCSP) aims at optimally arranging courses to corresponding rooms, faculties, students, and timeslots with constraints. Previously, the university staff solved this thorny problem by hand, which is very time-consuming and makes it easy to fall into chaos. Even some meta-heuristic algorithms are proposed to solve UCSP automatically, while most only utilize one single algorithm, so the scheduling results still need improvement. Besides, they lack an in-depth analysis of the inner algorithms. Therefore, this paper presents a novel and practical approach based on Tabu search and simulated annealing algorithms for solving USCP. Firstly, the initial solution of the UCSP instance is generated by one construction heuristic algorithm, the first fit algorithm. Secondly, we defined one union move selector to control the moves and provide diverse solutions from initial solutions, consisting of two changing move selectors. Thirdly, Tabu search and simulated annealing (SA) are combined to filter out unacceptable moves in a parallel mode. Then, the acceptable moves are selected by one adaptive decision algorithm, which is used as the next step to construct the final solving path. Benefits from the excellent design of the union move selector, parallel tabu search and SA, and adaptive decision algorithm, the proposed method could effectively solve UCSP since it fully uses Tabu and SA. We designed and tested the proposed algorithm in one real-world (PKNU-UCSP) and ten random UCSP instances. The experimental results confirmed its effectiveness. Besides, the in-depth analysis confirmed each component's effectiveness for solving UCSP. [ABSTRACT FROM AUTHOR]

The paper addresses the challenge of transmitting a big number of files stored in a data center (DC), encrypting them by compilers, and sending them through a network at an acceptable time. Face to the big number of files, only one compiler may not be sufficient to encrypt data in an acceptable time. In this paper, we consider the problem of several compilers and the objective is to find an algorithm that can give an efficient schedule for the given files to be compiled by the compilers. The main objective of the work is to minimize the gap in the total size of assigned files between compilers. This minimization ensures the fair distribution of files to different compilers. This problem is considered to be a very hard problem. This paper presents two research axes. The first axis is related to architecture. We propose a novel pre-compiler architecture in this context. The second axis is algorithmic development. We develop six algorithms to solve the problem, in this context. These algorithms are based on the dispatching rules method, decomposition method, and an iterative approach. These algorithms give approximate solutions for the studied problem. An experimental result is implemented to show the performance of algorithms. Several indicators are used to measure the performance of the proposed algorithms. In addition, five classes are proposed to test the algorithms with a total of 2350 instances. A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm. The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic (ISL) with a percentage equal to 97.7% and an average running time equal to 0.148 s. All other algorithms did not exceed 22% as a percentage. The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic (ILS) with a percentage equal to 21,4% and an average running time equal to 0.150 s. [ABSTRACT FROM AUTHOR]

The surge of lightweight containers in cloud storage, propelled by technological advancements, offers dependable services. However, the inherent security concerns arising from shared access to the host Operating System (OS) necessitate an effective solution. This paper introduces an energy-efficient and secure scheduling approach to tackle this issue. Users register task requests with a resource manager, which collects and preprocesses data to eliminate redundancies. The Levenberg-Marquardt Multi-Layer Perceptron Neural Network (LM-MLPNN) optimizes container resource utilization by analyzing user requests. The Homography Transform-based K-Mode Algorithm (HT-KMA) facilitates efficient clustering through attribute extraction. To address imbalances, the Weighted Round Robin (WRR) technique is employed. An optimal container, selected by the Variational Mode Decomposition-based Archimedes Optimization Algorithm (VMD-AOA), undergoes Elliptic Curve-based Key Derivation Function (EC-KDF) for enhanced security before being transmitted to the resource manager. Experimental results demonstrate the superiority of our methodology over existing algorithms. [ABSTRACT FROM AUTHOR]

Elevator systems in buildings face challenges due to unpredictable passenger flow, which can make scheduling elevators complicated to optimize their operation. Most of the existing algorithms are developed based on pattern recognition and may not be effective in scenarios where patterns are difficult to classify, especially when elevator operations involve uncertain human behaviors. To address this issue, this paper proposes a time-dependent optimization model that considers long-term and multi-floor peaks, with a focus on peak floors. The proposed model accounts for dynamic scheduling patterns of elevators and represents passenger flow direction as a relation matrix. The proposed direction optimization method contains several functions to guide iteration direction and improve the efficiency of an iteration process based on classical algorithms. This method also ensures the stability of Markov chains by adjusting an iteration process. The feasibility of the proposed method is supported by the relevant theory, and experimental results show that the direction-optimized algorithms outperform classical algorithms, resulting in the superb operating efficiency of elevators at a lower cost. This paper contributes to the development of efficient algorithms for scheduling elevators in complex traffic patterns, which can improve performance of elevator group systems in buildings. The proposed method is not only limited to elevators but can also be extended to other transportation systems with flow requirements. • Estimating the relation between peak floors without pattern recognition. • Four relations are defined to classify the passenger flow. • Dynamically modifying three metaheuristic algorithms' iteration law. • The application for transportation systems with unpredictable flow. [ABSTRACT FROM AUTHOR]

5G mobile communications introduce novel solutions to overcome the frequency spectrum's shortage. It broadens the spectrum band to millimeter-waves, employs multiple numerologies to calculate subcarrier spacing, and supports various division duplex modes. Furthermore, the fifth generation of mobile networks intends to employ both frequency division duplex and time division duplex. This study focuses on Time Division Duplex (TDD) mode. Compared to the Frequency Division Duplex (FDD), the time duplex mode enhances flexibility and allows efficient frequency spectrum usage. However, the recent papers addressing resource scheduling issues for TDD duplex employ only the current classical schedulers, which were primarily designed for FDD mode, to accomplish radio resource allocation. In this paper, we compared the achievable throughput and data accumulated in the buffer of these schedulers to assess their suitability and compatibility with TDD specifications. The resulting performances show that an appropriate scheduler in line with TDD requirements should be implemented to exploit the available spectrum efficiently and reach the required throughput. [ABSTRACT FROM AUTHOR]

Moving is the key to modern life. Most things are in moving such as vehicles and user mobiles, so the need for high-speed wireless networks to serve the high demand of the wireless application becomes essential for any wireless network design. The use of web browsing, online gaming, and on-time data exchange like video calls as an example means that users need a high data rate and fewer error communication links. To satisfy this, increasing the bandwidth available for each network will enhance the throughput of the communication, but the bandwidth available is a limited resource which means that thinking about techniques to be used to increase the throughput of the network is very important. One of the techniques used is the spectrum sharing between the available networks, but the problem here is when there is no available channel to connect with. This encourages researchers to think about using scheduling as a technique to serve the high capacity on the network. Studying scheduling techniques depends on the Quality-of-Service (QoS) of the network, so the throughput performance is the metric of this paper. In this paper, an improved Best-CQI scheduling algorithm is proposed to enhance the throughput of the network. The proposed algorithm was compared with three user scheduling algorithms to evaluate the throughput performance which are Round Robin (RR), Proportional Fair (PF), and Best-CQI algorithms. The study is performed under Line-of-Sight (LoS) link at carrier frequency 2.6 GHz to satisfy the Vehicular Long Term Evolution (LTE-V) with the high-speed scenario. The simulation results show that the proposed algorithm outperforms the throughput performance of the other algorithms. [ABSTRACT FROM AUTHOR]

Resource-constrained project scheduling problem (RCPSP) is a kind of representative practical engineering problem, the purpose is to schedule activities in the project through rational use of limited resources in the minimum time. This paper proposes an improved hybrid cuckoo algorithm (CS&SA) to solve the RCPSP problem. Firstly, the individual elements are randomly coded into priority vectors, and the population is decoded using serial scheduling to convert the individual into a set of task scheduling sequences. Secondly, the Levy flight is redesigned to change the algorithm from random walk to adaptive as the population fitness changes. Then, this article adds three neighborhood update techniques to meet the update requirements of the algorithm at different stages. Finally, in order to prevent the algorithm from falling into a local optimum, this paper introduces a simulated annealing strategy to allow the algorithm to accept some individuals with poor quality with a certain probability in each iteration. In the testing part, this paper firstly tests the effectiveness and optimization of three different-scale examples in the classic example library PSPLIB for RCPSP problems. Among them, the average error of the CS&SA algorithm in the small-scale J30 is 0.25%, and the average error in the medium-scale J60 is 11.21%, and the average error of the large-scale J120 is 19.83%. Then, by comparing other intelligent optimization algorithms, it is proved that NCS&SA is superior to other algorithms in optimization and accuracy. [ABSTRACT FROM AUTHOR]

Cyber-physical system (CPS) is the core technology of Industry 4.0. The deterministic behaviors of the CPS require real-time deterministic guarantee. Therefore, this paper improves the ant colony optimization (ACO) into a scheduling algorithm for time-triggered flows in time-sensitive network (TSN), a standard developed by the IEEE 802.1 Working Group that fully satisfies the strict end-to-end delay requirements of industrial applications. Simulation results show that the improved ACO (IACO) can schedule the time-triggered flows in the TSN excellently, and outperform the traditional ACO in convergence speed, optimization ability and the proneness to local optimum trap. To sum up, this paper provides an effective real-time guarantee for the TSNs. [ABSTRACT FROM AUTHOR]

The computer technology searching for a preferable point in any factor space delivers to the extremum with regard to quantitative scalar response within scope of intelligent DSS-sphere, which was considered in this paper. The problem of universalization and planning (design) of extreme experiments in relation to complex objects in the conditions of significant limitation including the allowable number of experiments and sub-catastrophic duration of them was solved. A technology that involves the separation of three linked stages of optimization was proposed.The computer technology searching for a preferable point in any factor space delivers to the extremum with regard to quantitative scalar response within scope of intelligent DSS-sphere, which was considered in this paper. The problem of universalization and planning (design) of extreme experiments in relation to complex objects in the conditions of significant limitation including the allowable number of experiments and sub-catastrophic duration of them was solved. A technology that involves the separation of three linked stages of optimization was proposed. [ABSTRACT FROM AUTHOR]

Satellite range scheduling always plays a crucial role in tracking, telemetry, and control of the spacecraft. With the significant increase in the number and type of satellites in orbit, the demands of users for satellite range scheduling are becoming more and more diverse. However, existing studies for the satellite range scheduling problem (SRSP) rarely consider multiple optimization objectives, which is quite significant in practice. Hence, this paper investigates a multi-objective satellite range scheduling problem (MO-SRSP) that optimizes three objectives simultaneously: the overall profits of tasks, load-balance of antennas, and completion timeliness of tasks. To address MO-SRSP, this paper establishes a mathematical model on the basis of analysis of MO-SRSP and proposes a multi-objective evolutionary algorithm, which is called multi-objective differential evolution algorithm based on space division and adaptive selection strategy (MODE-SDAS). The space division strategy will uniformly divide the objective space into a set of subspaces and preserve a set of non-dominated solutions in each subspace during the environmental selection even if some of these solutions are dominated by other solutions in other subspaces, so as to maintain the diversity of the algorithm. The adaptive selection strategy will adaptively allocate computational resources to different subspaces to improve the convergence of the population. Besides, problem-specific designs such as coding and encoding methods, the discrete differential evolution operator, as well as objective-specific individual variation operators are incorporated into the algorithm to enhance the search capability. Finally, extensive experiments based on simulation test cases are carried out to verify the effectiveness and efficiency of MODE-SDAS. The comparison results show that MODE-SDAS significantly outperforms its competitors in terms of three metrics. Meanwhile, knee point analysis, sensitivity analysis, and application insights are presented. • A tri-objective satellite range scheduling problem. • A multi-objective differential evolution algorithm is proposed. • Space division and adaptive selection strategies are combined. • Problem-specific and objective-specific designs are applied. [ABSTRACT FROM AUTHOR]

With the advent of "Industry 4.0", more matrix manufacturing workshops have adopted automated guided vehicle (AGV) for material handling. AGV transportation has become a key link in manufacturing production. Traditional AGVs scheduling problem (AGVSP) is studied in depth. However, most research overlooks an important problem, in production with limited resources, the number of AGVs is insufficient. Therefore, the wait time of workstations is longer than expected. The service time of the task is delayed and the cost is increased. To solve above problem, this paper proposes the binary discrete enhanced Jaya (BDE-Jaya) algorithm. The main goal is to minimize transportation cost, including AGV traveling cost, service early penalty, and total tardiness (TTD). A key-task shift method is proposed to reduce TTD and task service early penalty. Two heuristics based on the problem features are designed to generate the initial solution. In the evolutionary stage, three offspring generation methods are used to improve the algorithm exploitation capability and exploration capability. Then, an insertion-based repair method is designed to prevent the exploitation process falling into local optimum. Furthermore, three parameters are proposed to improve the performance of the algorithm. Finally, simulation experiment shows that the proposed BDE-Jaya algorithm has significant advantages compared with other algorithms. [ABSTRACT FROM AUTHOR]

We address in this paper the two-machine job shop scheduling problem with a single server that sets up the jobs before they get processed on the machines. The server is only needed during the set-up and becomes free at the end of this phase. Moreover, the set-ups are non-anticipatory and the set-up times are sequence-independent. We seek a schedule that minimizes the overall completion time, also called the makespan. We propose several lower bounds to the problem and prove the N P -hardness in the strong sense of two restricted cases. In addition, we present a linear time algorithm for a special case. In order to solve the general problem, we develop a genetic and simulated annealing algorithms that use feasibility guaranteed procedures. An experimental study is carried out to analyze the performance of these meta-heuristic methods. [ABSTRACT FROM AUTHOR]

Schedule-based fieldbus is applied widely in industrial control. Building schedule table (ST) can affect the time-critical performance for communication greatly. Communication jitters can cause some unexpected results in bus traffic scheduling and longer jitters can even cause severe delay in time-critical application. Communication jitter and evenness in arrangement of variables to be scheduled in micro-cycles in ST were two important issues dealing with the performance. An algorithm to build schedule table based on numerical co-prime method was proposed in this paper. Communication jitter in ST was investigated. Concept dealing with rank of ST was introduced. Numerical co-prime method to evaluate rank of ST was proposed. The algorithm and procedure to build schedule table based on the proposed numerical co-prime method were presented and exemplified in detail. Performance analysis and comparison of the proposed algorithm to HCF/LCM method was presented. It was proved that the proposed algorithm can reduce communication jitter to its minimum value and the longest time consumed to schedule variable in one micro-cycle, and greatly reduce the possibility the time consumed to schedule variables exceeded micro-cycle. • Communication jitter in schedule table (ST) with its impact was investigated. • Concept dealing with rank of ST was introduced. • Numerical co-prime method to evaluate rank of ST was proposed. • The algorithm and procedure to build schedule table based on the proposed method was presented • Performance analysis and comparison of the proposed method was presented [ABSTRACT FROM AUTHOR]

This paper addresses an uncertain unrelated parallel machine scheduling problem (UPMSP) with setup times, which is referred to the scenario UPMSP since uncertain processing times are described by a set of discrete scenarios. The bi-objective robust optimization formulation is established. Two objectives are to minimize the mean makespan and the worst-case makespan across all scenarios, which reflect solution optimality and solution robustness respectively. The contribution of this paper is three-fold. First, we propose the bi-objective robust optimization formulation under discrete scenarios for uncertain UPMSP. Second, two versions of swarm intelligent algorithms are developed by combining fruit fly optimization algorithm (FOA) framework and scenario-guided local search, which are performed based on two problem-specific neighborhood structures. The learning-scenario neighborhood structure is constructed by selecting single scenario using reinforcement learning. The united-scenario neighborhood structure is constructed by collecting all discrete scenarios. Third, an experiment was conducted to compare two developed algorithms with the state-of-the-art alternative algorithms. The computational results show that the developed algorithms are identically better than possible alternatives in terms of multi-objective metrics. Moreover, it is shown that the FOA algorithm with learning-scenario-neighborhood smell search is advantageous for the discussed problem. [ABSTRACT FROM AUTHOR]

Task scheduling is one of the major issues in cloud computing environment. Efficient task scheduling is substantial to attain cost-effective execution and improve resource utilization. The task scheduling problem is classified to be a nondeterministic polynomial time (NP)-hard problem. This feature attracts researchers to utilize nature inspired metaheuristic algorithms. Initializing searching solutions randomly is one of the key features in such optimization algorithms. However, assisting metaheuristic algorithms with effective initialized solutions can significantly improve its performance. In this paper, an improved initialization of particle swarm optimization (PSO) using heuristic algorithms is proposed. Longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms are used to initialize the PSO. The performance of the proposed LJFP-PSO and MCT-PSO algorithms are evaluated in minimizing the makespan, total execution time, degree of imbalance, and total energy consumption metrices. Moreover, the performance of the proposed algorithms is compared with recent task scheduling methods. Simulation results revealed the effectiveness and superiority of the proposed LJFP-PSO and MCT-PSO compared to the conventional PSO and comparative algorithms. [ABSTRACT FROM AUTHOR]

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. [ABSTRACT FROM AUTHOR]

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms. • Multi-objective CSO. • Fault tolerance. • Load balancing. • Task scheduling. • High resource utilization. [ABSTRACT FROM AUTHOR]

In this paper, we study the stochastic resource-constrained project scheduling problem with transfer and idle costs (SRCPSP-TIC) under uncertain environments, where the resource transfer and idle take time and costs. Priority rule (PR) based heuristics are the most commonly used approaches for project scheduling under uncertain environments due to their simplicity and efficiency. For PR-based heuristics of the SRCPSP-TIC, activity priority rules (APRs) and transfer priority rules (TPRs) are necessary to decide the activity sequence and resource transfer. Traditionally, APRs and TPRs need to be manually designed, which is time-consuming and difficult to adapt to different scheduling scenarios. Therefore, based on two individual representation methods, we propose two co-evolution genetic programming (CGP) based hyper-heuristics to evolve APRs and TPRs automatically. Furthermore, a fitness function surrogate-assisted method and a transfer learning mechanism are designed to improve the efficiency and solution quality of the CGP. Based on the instances with different stochastic activity duration distributions, we test the performance of different CGP-based hyper-heuristics and compare the evolved PRs with the classical PRs to demonstrate the effectiveness of evolved PRs. Experimental results show that the proposed algorithms can automatically evolve efficient PRs for the SRCPSP-TIC. [ABSTRACT FROM AUTHOR]

In the cloud computing environment, scheduling algorithms show the vital role of finding a possible schedule of the tasks. Extant literatures have shown that the task scheduling problem is NP-Complete as the objective is to obtain the minimum overall execution time. In this paper, we address the problem of scheduling a set of l tasks with a set of | G | groups to a set of m clouds, such that the overall layover time is minimized. Note that overall layover time is the sum of the timing gaps between paired tasks. Here, we present a pair-based task scheduling algorithm for cloud computing environment, which is based on the well-known optimization algorithm, called Hungarian algorithm. The proposed algorithm considers an unequal number of tasks and clouds, and pairs the tasks to make the scheduling decision. We simulate the proposed algorithm and compare it with three existing algorithms, first-come-first-served, Hungarian algorithm with lease time and Hungarian algorithm with converse lease time in twenty-two different datasets. The performance evaluation shows that the proposed algorithm produces better layover time in comparison to existing algorithms. The proposed algorithm is analyzed theoretically and shown to require O (kpl 2) time for k iterations, p repetitions and l tasks. [ABSTRACT FROM AUTHOR]

Big data has ushered in the era of the tera, during which massive amounts of data are being gathered at accelerating rates. The size of the world's data is increasing in zeta-bytes as a result of improvements in processing speed, storage capacity, and data availability. One of the big data technologies is Hadoop, which uses the Map-Reduce and Hadoop Distributed File System to analyse data. An essential task for effective cluster resource management is job scheduling. Schedulers for Hadoop are pluggable parts that allocate resources to jobs. The default FIFO, Fair, and Capacity schedulers are prevalent in a variety of schedulers. A thorough analysis of the various work scheduling algorithms has been carried out in this paper. Additionally, their comparative parametric analysis was conducted while highlighting the essential features that these schedulers have in common. [ABSTRACT FROM AUTHOR]

This paper presents a heterogenous cloud computing environment for provisioning real-time (dynamic workload) services in a cloud computing environment. Moreover, this work also presents SLA Aware Energy Optimized (SAEO) Scheduling Algorithm to execute dynamic workload applications like the data intensive and scientific applications. The main aim of the SAEO is to bring good tradeoffs in minimizing computation time and energy consumption by employing Dynamic Voltage Frequency Scaling (DVFS) effectively utilizing system resource of cloud. SAEO achieves much better performance than existing DVFS-based scheduling in terms of computation time and energy efficiency. [ABSTRACT FROM AUTHOR]

The paper presents an effective algorithm for solving Vehicle Routing Problem with Dynamic Requests based on memetic algorithms. The proposed method is applied to a widely-used set of 21 benchmark problems yielding 14 new best-know results when using the same numbers of fitness function evaluations as the comparative methods. Apart from encouraging numerical outcomes, the main contribution of the paper is investigation into the importance of the so-called starting delay parameter, whose appropriate selection has a crucial impact on the quality of results. Another key factor in accomplishing high quality results is attributed to the proposed effective mechanism of knowledge transfer between partial solutions developed in consecutive time slices. While particular problem encoding and memetic local optimization scheme were already presented in the literature, the novelty of this work lies in their innovative combination into one synergetic system as well as their application to a different problem than in the original works. [ABSTRACT FROM AUTHOR]

During the transportation of dangerous, the risk is an important factor that should be considered due to the potentially serious consequences of the accident. Regardless of risks, time is the primary issue that should be considered in the transportation of hazardous materials. This paper introduces a bi-objective model of vehicle routing and scheduling for hazardous material allocation problems under fuzzy conditions to minimize the total allocation time and risk. In the proposed model, the fuzzy inference system and fuzzy failure mode and effects analysis are used for the first time to identify and calculate high-level risks, instead of the previous simple methods. Moreover, the Jimenez method and fuzzy goal programming are respectively utilized to convert the fuzzy biobjective model into the same crisp and single-objective one. In addition, in order to deal with the NP-hardness of large-scale problems, two meta-heuristic algorithms, invasive weed optimization, and genetic algorithm, are used, and multiple sensitivity analyses are performed to prove the effectiveness of the proposed method. The performance of the proposed algorithms is also assessed through a comparative study. Finally, the proposed model is applied to a real case study to prove the validity of the model. [ABSTRACT FROM AUTHOR]

In this study, Artificial Neural Networks (ANN) were used to model parameters of scheduling of reconfigurable embedded systems containing resource constraints for applications running in real-time. The main goal is to implement a neural network based approach for real-time scheduling in order to handle real-time constraints in execution scenarios. Many techniques have been proposed for both the planning of tasks and reducing energy consumption. This paper presents a new hybrid contribution that handles the real-time scheduling of embedded systems by keeping energy consumption at a low power depending on the combination of Dynamic Voltage Scaling (DVS) and the energy Priority Earlier Deadline First (PEDF) algorithm. Indeed, in our original proposed approach, an other combination of DVS and time feedback can be used to scale the frequency by dynamically adjusting the operating voltage. The originality of our algorithm appears by allowing medium priority tasks to be executed more quickly before their deadline while decreasing their ability to be send again to the waiting list in order to ensure the execution of a task with the lowest voltage possible. [ABSTRACT FROM AUTHOR]

In this paper we consider the combinatorial optimization problem known as workflow scheduling. We compare three encoding schemes of varying density: one-hot, binary, and domain wall, and test their performance against two wellknown hybrid quantum-classical algorithms: Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). In an attempt to obtain the best results possible, we investigate various parameters of the algorithms and test out other state-of-the-art improvements, such as dedicated QAOA mixers. Ultimately, we prove that, despite its popularity, one-hot encoding is not always the best, and using a denser encoding scheme, such as binary or domain wall, can allow for solving larger instances of workflow scheduling. Additionally, combining the above-mentioned encodings with dedicated QAOA mixers reduces the number of infeasible solutions, leading to better results. [ABSTRACT FROM AUTHOR]

Most studies on distributed assembly permutation flowshop scheduling do not consider product priorities and factory heterogeneity. This causes delays in critical products and cannot reflect the real-world production situation. This paper focuses on the energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities (EHDAPFS-P) to minimize total tardiness and total energy consumption simultaneously. Unlike traditional models, factory heterogeneity and product priorities are considered to better reflect the production environment and customer satisfaction in real-world situations. Then, a Q-learning memetic algorithm (QLMA) is proposed to solve this problem: (i) a high-quality initial population is obtained using a hybrid initialization strategy that combines four problem-specific heuristics; (ii) six efficient neighborhood structures are tailored to guide the population to converge to the promising areas; (iii) the most useful neighborhood structure is selected among the six structures using the Q-learning algorithm to accelerate the convergence, thus maximizing the cumulative and future improvements according to the population state; and (iv) an energy-saving strategy is developed to optimize the total energy consumption without deteriorating the total tardiness. The proposed QLMA is compared with seven state-of-the-art algorithms on 261 benchmark instances to demonstrate its superiority or at least competitiveness. • Factory heterogeneity and product priorities are considered in DAPFSP. • Four problem-specific heuristics are devised to generate the initial population. • Six efficient neighborhood structures are designed. • The best neighborhood structure is determined using the Q-learning algorithm. • An effective energy-saving strategy is developed to reduce TEC. [ABSTRACT FROM AUTHOR]

The traditional monitoring technology of energy storage power stations has problems of poor control effect and low dispatching accuracy. Based on this, this paper studies the monitoring technology of energy storage power stations based on the discharge control scheduling algorithm. From the aspects of discharge control, daily maintenance, discharge process monitoring and power utilization rate of the energy storage station, the precise control of the discharge process is realized. The discharge control scheduling algorithm of the energy storage station is used for comprehensive analysis and evaluation. The algorithm can realize the dynamic record of multiple data entries in the discharge process of the energy storage station and realize diversified analysis and intelligent matching. Through the discharge control scheduling algorithm of the energy storage power station, the whole process monitoring of the energy storage power station is realized, so as to improve the working efficiency and reduce the cost of the discharge control link. The experimental results show that the monitoring technology based on the discharge control scheduling algorithm has the advantages of high control accuracy and fast response, which can effectively improve the monitoring efficiency of the energy storage power station. [ABSTRACT FROM AUTHOR]

From the past decade, the utilization of the cloud environment has been increased drastically. Many web applications are relying on the computing environment of the cloud. Therefore, resource scheduling for the client tasks is treated as the major issues challenged by the cloud. This paper concentrated on client QoS requirements and developed the optimized scheduling mechanism. The Optimized scheduling mechanism using ACO algorithm (OSACO) is proposed to reduce the energy, cost and time. The proposed OSACO algorithm performance is compared with the existing algorithms. The simulation results proved the effective optimization compared to the other existing algorithms. [ABSTRACT FROM AUTHOR]

From the past decade, the utilization of the cloud environment has been increased drastically. Many web applications are relying on the computing environment of the cloud. Therefore, resource scheduling for the client tasks is treated as the major issues challenged by the cloud. This paper concentrated on client QoS requirements and developed the optimized scheduling mechanism. The Optimized scheduling mechanism using ACO algorithm (OSACO) is proposed to reduce the energy, cost and time. The proposed OSACO algorithm performance is compared with the existing algorithms. The simulation results proved the effective optimization compared to the other existing algorithms. [ABSTRACT FROM AUTHOR]

To deliver the required QoS, the cloud provider is asked to efficiently execute the tenants' tasks and manages a huge amount of distributed and shared data. Hence, task scheduling and data replication are interdependent techniques that can improve the overall system performance and guarantee efficient data accessing. These operations must also preserve the economic profit of the cloud provider, which is very challenging. In this paper, we present a novel combination between a scheduling algorithm called Bottleneck Value Scheduling (BVS) algorithm with a dynamic data replication strategy called Correlation and Economic Model-based Replication (CEMR). Our aim is to improve data access effectiveness in order to meet service level objectives in terms of response time S LO RT and minimum availability S LO MA , while preserving the provider profit. Simulation results demonstrate that the proposed scheduling and replication strategies offer better performance compared to existing strategies. [ABSTRACT FROM AUTHOR]

In many enterprises, the work efficiency is suppressed by the irrational allocation of enterprise resources. To solve the problem, this paper puts forward the optimal scheduling path algorithm for enterprise resource workflow (ERWOSA). To measure the task efficiency of each person after project issuance, the ERWOSA models enterprise resource workflow, and quantifies each node in the model. Then, novel attributes like mean execution accuracy, mean execution time, and node weight were calculated. Based on the calculation result, the optimal scheduling path was solved, which is highly accurate and efficient. In this way, the ERWOSA manages to bring more benefits to the enterprise. [ABSTRACT FROM AUTHOR]

On the basis of analyzing the current status and the key technology of grid workflow scheduling, in-depth research on the grid workflow scheduling algorithm under the restraint of time QOS and trust QOS is conducted in this paper. A grid workflow task scheduling algorithm (GWTS) based on critical tasks under the constraints of trust is designed. Firstly, backward depth of tasks is calculated in GWTS, and critical tasks are ascertained according to the execution time on candidate resources. Secondly, the trust of grid resources is computed based on direct experience and recommendation experience synthetically. Finally, tasks are scheduled by decreasing backward depth, and resources are closed to meet the integrated function of execution time and trust and are allocated for critical tasks as a priority. Experiments show that the workflow completion time is reduced, the success rate of task execution is increased by 6-15%, and the GWTS algorithm can effectively guarantee grid scheduling resource optimization and improve the scheduling efficiency. [ABSTRACT FROM AUTHOR]

Workflow scheduling in cloud platforms is a highly challenging issue because it faces multiple conflicting optimization objectives and large-scale decision variables. Most of the existing multi-objective workflow scheduling algorithms regard the focused problems as black boxes, and optimize large-scale decision variables as a whole. This leads to inefficiency in searching solution spaces that grow exponentially with the increase of decision variables. To compensate the above deficiency, this paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm, KAMSA for short, to optimize makespan and cost of workflow execution in cloud platforms. Specifically, we excavate the knowledge that adjustment of a task's execution only affects its successor tasks to divide large-scale decision variables into a series of groups, so as to give play to the strengths of divide-and-conquer technology to improve the evolutionary search efficiency. Moreover, we develop an adaptive resource allocation scheme to reward more evolution opportunities for groups with high contributions to further improve the evolutionary search efficiency. We compare the proposed KAMSA with five state-of-the-art competitors in the context of 20 real-world workflows and the Amazon elastic compute cloud (EC2). The comparison results verify the KAMSA's advantages by prevailing over the five competitors on 18 out of the 20 test cases with respect to the metric hypervolume. • We design a knowledge-driven multi-objective workflow scheduling algorithm. • We excavate workflow structures to group large-scale decision variables. • We design an adaptive scheme to improve the evolutionary search efficiency. [ABSTRACT FROM AUTHOR]

Abstract: This paper attempts to study the scheduling scheme in emergency resource center. The scheduling engineering in resource center can be divided into two stages: the normal stage and emergency stage. In the normal stage, orders come brokenly to the resource center in small number, and the order seldom has emergency requirement. In contrast, a large number of orders, which always have different emergency requirements, reach the resource center in a very short time. This paper analyzes the differences between the normal stage and emergency stage, and proposes the corresponding scheduling methods of two stages. In order to feed the different emergency requirements of orders, various weights are given to different orders. A polynomial algorithm is proposed to minimize the weighted makespan of emergency resource scheduling. Furthermore, a dynamic algorithm is designed to solve the dynamic emergency scheduling. [Copyright &y& Elsevier]

Compute Cloud comprises a distributed set of High-Performance Computing (HPC) machines to stipulate on-demand computing services to remote users over the internet. Clouds are capable enough to provide an optimal solution to address the ever-increasing computation and storage demands of large scientic HPC applications. To attain good computing performances, mapping of Cloud jobs to the compute resources is a very crucial process. Currently we can say that several effcient Cloud scheduling heuristics are available, however, selecting an appropriate scheduler for the given environment (i.e., jobs and machines heterogeneity) and scheduling objectives (such as minimized makespan, higher throughput, increased resource utilization, load balanced mapping, etc.) is still a diffcult task. In this paper, we consider ten important scheduling heuristics (i.e., opportunistic load balancing algorithm, proactive simulation-based scheduling and load balancing, proactive simulation-based scheduling and enhanced load balancing, minimum completion time, Min-Min, load balance improved Min-Min, Max-Min, resource-aware scheduling algorithm, task-aware scheduling algorithm, and Sufferage) to perform an extensive empirical study to insight the scheduling mechanisms and the attainment of the major scheduling objectives. This study assumes that the Cloud job pool consists of a collection of independent and compute-intensive tasks that are statically scheduled to minimize the total execution time of a workload. The experiments are performed using two synthetic and one benchmark GoCJ workloads on a renowned Cloud simulator CloudSim. This empirical study presents a detailed analysis and insights into the circumstances requiring a load balanced scheduling mechanism to improve overall execution performance in terms of makespan, throughput, and resource utilization. The outcomes have revealed that the Suffer age and task-aware scheduling algorithm produce minimum makespan for the Cloud jobs. However, these two scheduling heuristics are not effcient enough to exploit the full computing capabilities of Cloud virtual machines. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a novel framework for improving the performance of multi-population algorithms in solving dynamic optimization problems (DOPs). The fundamental idea of the proposed framework is to incorporate the concept of scheduling into multi-population methods with the aim to allocate more function evaluations to the best performing sub-populations. Two methods are developed based on the proposed framework, each of which uses a different approach for scheduling the sub-populations. The first method combines the quality of sub-populations and the degree of diversity among them into a single feedback parameter for detecting the best performing sub-population. The second method uses the learning automata as the central unit for performing the scheduling operation. In order to validate the applicability of the proposed methods, they are incorporated into three well-known algorithms for DOPs. The experimental results show the efficiency of the scheduling approach for improving the multi-population methods on the moving peaks benchmark (MPB) and generalized dynamic benchmark generator. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present slot selection algorithms in economic models for independent job batch scheduling in distributed computing with non-dedicated resources. Existing approaches towards resource co-allocation and multiprocessor job scheduling in economic models of distributed computing are based on search of time-slots in resource occupancy schedules. The sought time-slots must match requirements of necessary span, computational resource properties, and cost. Usually such scheduling methods consider only one suited variant of time-slot set. This paper discloses a scheduling scheme that features multi-variant search. Two algorithms of linear complexity for search of alternative variants are compared. Having several optional resource configurations for each job makes an opportunity to perform an optimization of execution of the whole batch of jobs and to increase overall efficiency of scheduling. [Copyright &y& Elsevier]

Abstract: This paper investigates scheduling job shop problems with sequence-dependent setup times under minimization of makespan. We develop an effective metaheuristic, simulated annealing with novel operators, to potentially solve the problem. Simulated annealing is a well-recognized algorithm and historically classified as a local-search-based metaheuristic. The performance of simulated annealing critically depends on its operators and parameters, in particular, its neighborhood search structure. In this paper, we propose an effective neighborhood search structure based on insertion neighborhoods as well as analyzing the behavior of simulated annealing with different types of operators and parameters by the means of Taguchi method. An experiment based on Taillard benchmark is conducted to evaluate the proposed algorithm against some effective algorithms existing in the literature. The results show that the proposed algorithm outperforms the other algorithms. [Copyright &y& Elsevier]

This paper presents a graph-coloring-based algorithm for the exam scheduling application, with the objective of achieving fairness, accuracy, and optimal exam time period. Through the work, we consider few assumptions and constraints, closely related to the general exam scheduling problem, and mainly driven from accumulated experience at various universities. The performance of the algorithm is also a major concern of this paper. [ABSTRACT FROM AUTHOR]