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Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects—from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing is widely used in various fields and is booming day by day with different services offered to users according to their needs and contracts. However, this has brought many challenges and constraints that organizations must to be aware of and address to fully harness its power. In practice, the most important issue that has gained significant influence in improving system performances is task scheduling. Unfortunately, it is commonly known that this problem is NP-hard and the use of both heuristics and metaheuristics is required to obtain near optimal solutions but in a reasonable amount of computation time. Despite the fact that several studies have been published in the literature, there are still interesting and relevant questions to be addressed. For instance, when it comes to the stagnation phenomenon of local solutions and the premature convergence of the search process, it is crucial to execute the exploration and exploitation stages carefully as improperly performed stages may result in inefficient task mapping solutions. Consequently, to overcome the limitations of existing techniques in terms of local optimality trap and immature convergence, a novel hybrid optimization algorithm is proposed to deal with multi-objective task scheduling in heterogeneous IaaS cloud environments. It is based on the combination of the pollination behavior of flowers with the search exploration capability of the grey wolf optimizer strategy. In addition, it makes use of the evolutionary algorithms crossover operators to strike a good balance between exploring new solutions and exploiting the already discovered ones. Based on the CloudSim framework, different test-bed scenarios and both synthetic and standard workload traces were considered to assess the performance of the proposed algorithm by evaluating its objective function in terms of four optimization criteria, namely time makespan, resource utilization, degree of imbalance and throughput. Our proposal was compared to the well-known optimization-based scheduling techniques in the literature, like TSMGWO, GGWO, LPGWO and FPA approach. The obtained results corroborate the merits of the new designed hybrid algorithm. [ABSTRACT FROM AUTHOR]

In this paper we study the classical problem of throughput maximization. In this problem we have a collection J of n jobs, each having a release time r j , deadline d j , and processing time p j . They have to be scheduled non-preemptively on m identical parallel machines. The goal is to find a schedule which maximizes the number of jobs scheduled entirely in their [ r j , d j ] window. This problem has been studied extensively (even for the case of m = 1 ). Several special cases of the problem remain open. Bar-Noy et al. (Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, May 1–4, 1999, Atlanta, Georgia, USA, pp. 622–631. ACM, 1999, https://doi.org/10.1145/301250.301420) presented an algorithm with ratio 1 - 1 / (1 + 1 / m) m for m machines, which approaches 1 - 1 / e as m increases. For m = 1 , Chuzhoy et al. (42nd Annual Symposium on Foundations of Computer Science (FOCS) 2001, 14–17 October 2001, Las Vegas, Nevada, USA, pp. 348–356. IEEE Computer Society, 2001) presented an algorithm with approximation with ratio 1 - 1 e - ε (for any ε > 0 ). Recently Im et al. (SIAM J Discrete Math 34(3):1649–1669, 2020) presented an algorithm with ratio 1 - 1 / e + ε 0 for some absolute constant ε 0 > 0 for any fixed m. They also presented an algorithm with ratio 1 - O (log m / m) - ε for general m which approaches 1 as m grows. The approximability of the problem for m = O (1) remains a major open question. Even for the case of m = 1 and c = O (1) distinct processing times the problem is open (Sgall in: Algorithms - ESA 2012 - 20th Annual European Symposium, Ljubljana, Slovenia, September 10–12, 2012. Proceedings, pp 2–11, 2012). In this paper we study the case of m = O (1) and show that if there are c distinct processing times, i.e. p j 's come from a set of size c, then there is a randomized (1 - ε) -approximation that runs in time O (n m c 7 ε - 6 log T) , where T is the largest deadline. Therefore, for constant m and constant c this yields a PTAS. Our algorithm is based on proving structural properties for a near optimum solution that allows one to use a dynamic programming with pruning. [ABSTRACT FROM AUTHOR]

This paper studies the single-machine group scheduling problem with convex resource allocation and learning effect. The number of jobs in each group is different, and the corresponding common due dates are also different, where the processing time of jobs follows a convex function of resource allocation. Under common due date assignment, the objective is to minimize the weighted sum of earliness, tardiness, common due date, resource consumption, and makespan. To solve the problem, we present the heuristic, simulated annealing, and branch-and-bound algorithms. Computational experiments indicate that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

To perform a set of tasks in a robotic network cloud system as fast as possible, it is recommended to use a scheduling approach that minimizes the makespan. The makespan is defined as the time between the start of the first scheduled task and the completion of all scheduled tasks. Load balancing is a technique to distribute incoming tasks across processing units in a way that the resource utilization is optimized and the makespan is minimized. Robotic network cloud systems can be conceptualized as graphs, with nodes representing hardware with independent computing power and edges representing data transmissions between the nodes. The initial scheduler assigns a set of newly arrived tasks to the processing units capable of performing them. To reduce the response time we can replicate some of the tasks and assign them to different processing units. This results in some tasks becoming redundant. Assigning redundant tasks refers to determining which processing unit should receive the replicated tasks. Load balancing for redundant allocation can be viewed as assigning tasks to multiple processing units with different resource sizes so that the load is evenly distributed among the units. We propose a technique for load balancing, the ordered balancing algorithm, to minimize the makespan in the redundant allocation and scheduling problem. We prove theoretically the correctness of the proposed algorithm and illustrate with simulations, using R version 4.0.3, the obtained results that outperform other recent load balancing proposals. [ABSTRACT FROM AUTHOR]

With the rapid advancement of 5G mobile networks and Internet of Things technology, an increasing number of data-intensive applications are generating massive amounts of information, such as face recognition, video stream analysis, and augmented reality. These applications not only demand significant computational resources but also require high real-time performance, posing challenges to the existing cloud-computing model. Deploying these data-intensive applications in a mobile edge computing environment can reduce response time for processing user tasks and meet low-latency requirements. However, conventional CPU scheduling strategies fail to effectively enhance the performance of data-intensive applications on edge nodes with limited computing resources. In this study, we focus on real-time application feature awareness and propose a strategy considering data arrival rate and cost fair CPU scheduling (RCFS) with two components: CPU resource allocation based on process weights and process scheduling based on distributed weighted round-robin. In compared to the default scheduling strategy in Linux, named Completely Fair Scheduler, experiments on edge nodes show that the RCFS strategy can effectively enhance CPU utilization, reduce running time of data-intensive applications, and improve the system's data throughput. In the best-case scenario, the RCFS strategy achieved a remarkable increase in CPU utilization of data-intensive applications by up to 98.5%, reduced running time by up to 45.5%, and improved data throughput by up to 83.6%. [ABSTRACT FROM AUTHOR]

In this research, we pioneer a novel method to evaluate the maximum admissible load (MAL) for virtual machines (VMs) in physical machines (PMs) in data centre networks (DCNs), without restricting DCN topologies. This unique approach simplifies the issue into a single-source, multiple-sink maximum flow problem. It also resolves the maximum elastic scheduling problem by determining the optimal load for consistent growth without reassigning tasks. An effective strategy for these challenges is introduced and validated through extensive simulations. [ABSTRACT FROM AUTHOR]

In this research, we consider a two-machine job-shop scheduling problem with one joint job where a joint job is defined as a job whose operations are to be processed by different machines. The machine repetition and transportation times between machines are also considered. This problem is associated with real-world applications in the fields of production planning and supply chains. We first demonstrate that this problem is NP-hard in the strong sense. We then propose polynomial-time algorithms based on dynamic programming for cases with a fixed number of jobs. We also propose lemmas to reduce the number of states in dynamic programming without loss of optimality, so that the time complexity is improved. Other methodologies, including preprocessing and the two-pointers method, are also embedded to improve the algorithms. The proposed algorithms have better time complexities than those of variants of a well-known algorithm when applied to the cases of our problem. [ABSTRACT FROM AUTHOR]

Job scheduling plays a prominent part in cloud computing, and the production schedule of jobs can increase the cloud system's effectiveness. When serving millions of users at once, cloud computing must provide all user requests with excellent performance and ensure Quality of Service (QoS). A suitable task scheduling algorithm is needed to appropriately and effectively fulfil these requests. Several methods were proposed for job scheduling in cloud computing, but the existing techniques do not provide better efficiency. The Dual Interactive Wasserstein Generative Adversarial Network Optimized with Arithmetic Optimization Algorithm Based Job Scheduling in Cloud-Based Internet of Things is proposed to overcome this issue. Primarily, the data from the Alibaba dataset is preprocessed using the Kernel Co-Relation (KC) method. The preprocessed data is given to the Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) for task forecasting in the dynamic cloud environment, and it generates scheduled tasks as output. Then the Arithmetic Optimization Algorithm (AOA) is utilized to optimize the weight parameters of the Dual Interactive Wasserstein Generative Adversarial Network. The proposed method precisely predicts the future workload and diminishes extravagant power consumption at cloud data centres. The proposed method is implemented in MATLAB. The proposed method attains lower MSE (Mean Square Error), RMSE (Root Mean Square Error), MAPE (Mean Squared Prediction Error), MAE (Mean Absolute Error), and higher results without optimization algorithm before and after normalization compared to the current approaches. [ABSTRACT FROM AUTHOR]

The dynamic flexible job-shop problem (DFJSP) is a realistic and challenging problem that many production plants face. As the product line becomes more complex, the machines may suddenly break down or resume service, so we need a dynamic scheduling framework to cope with the changing number of machines over time. This issue has been rarely addressed in the literature. In this paper, we propose an improved learning-to-dispatch (L2D) model to generate a reasonable and good schedule to minimize the makespan. We formulate a DFJSP as a disjunctive graph and use graph neural networks (GINs) to embed the disjunctive graph into states for the agent to learn. The use of GINs enables the model to handle the dynamic number of machines and to effectively generalize to large-scale instances. The learning agent is a multi-layer feedforward network trained with a reinforcement learning algorithm, called proximal policy optimization. We trained the model on small-sized problems and tested it on various-sized problems. The experimental results show that our model outperforms the existing best priority dispatching rule algorithms, such as shortest processing time, most work remaining, flow due date per most work remaining, and most operations remaining. The results verify that the model has a good generalization capability and, thus, demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

We study the Non-preemptive Peak Demand Minimization (NPDM) problem, where we are given a set of jobs, specified by their processing times and energy requirements. The goal is to schedule all jobs within a fixed time period such that the peak load (the maximum total energy requirement at any time) is minimized. This problem has recently received significant attention due to its relevance in smart-grids. Theoretically, the problem is related to the classical strip packing problem (SP). In SP, a given set of axis-aligned rectangles must be packed into a fixed-width strip, such that the height of the strip is minimized. NPDM can be modeled as strip packing with slicing and stacking constraint: each rectangle may be cut vertically into multiple slices and the slices may be packed into the strip as individual pieces. The stacking constraint forbids solutions where two slices of the same rectangle are intersected by the same vertical line. Non-preemption enforces the slices to be placed in contiguous horizontal locations (but may be placed at different vertical locations). We obtain a (5 / 3 + ε) -approximation algorithm for the problem. We also provide an asymptotic efficient polynomial-time approximation scheme (AEPTAS) which generates a schedule for almost all jobs with energy consumption (1 + ε) OPT . The remaining jobs fit into a thin container of height 1. This AEPTAS is used as a subroutine to acquire the (5 / 3 + ε) -approximation algorithm. The previous best result for NPDM was a 2.7-approximation based on FFDH (Ranjan et al., in: 2015 IEEE symposium on computers and communication (ISCC), pp 758–763, IEEE, 2015). One of our key ideas is providing several new lower bounds on the optimal solution of a geometric packing, which could be useful in other related problems. These lower bounds help us to obtain approximative solutions based on Steinberg's algorithm in many cases. In addition, we show how to split schedules generated by the AEPTAS into few segments and to rearrange the corresponding jobs to insert the thin container mentioned above, such that it does not exceed the bound of (5 / 3 + ε) OPT . [ABSTRACT FROM AUTHOR]

The scheduling of applications is one of the prominent challenges in cloud computing, due to run time mapping by task scheduler between upcoming workload and cloud resources. An efficient scheduling algorithm is needed to schedule the diverse workload and improve the performance metrics by minimizing makespan and maximizing resource utilization. Many of the existing scheduling techniques addressed only makespan and resource utilization parameters and did not consider some other significant parameters like Energy consumption, migration time etc. that directly impacts the performance of cloud services. To overcome the mentioned issues, authors have proposed a nature inspired multi-objective task scheduling Grey wolf optimization (MOTSGWO) algorithm that has the ability to take the scheduling decision at runtime based upon the status of cloud resources and upcoming workload demands. In addition, the proposed technique allocates the resources based upon the budget of end users as well as priorities of tasks. The proposed MOTSGWO approach implemented on Cloudsim toolkit and the workload is generated by creation of datasets (da01, da02, da03, da04) with different distributions of tasks and workload traces taken from HPC2N and NASA (da05, da06) parallel workload archives. The results of extensive experiment shows that the proposed MOTSGWO approach outperforms other baseline policies and improved the significant parameters. [ABSTRACT FROM AUTHOR]

This paper investigates a single-machine scheduling problem of a software test with shared common setup operations. Each job has a corresponding set of setup operations, and the job cannot be executed unless its setups are completed. If two jobs have the same supporting setups, the common setups are performed only once. No preemption of any processing is allowed. This problem is known to be computationally intractable. In this study, we propose sequence-based and position-based integer programming models and a branch-and-bound algorithm for finding optimal solutions. We also propose an ant colony optimization algorithm for finding approximate solutions, which will be used as the initial upper bound of the branch-and-bound algorithm. The computational experiments are designed and conducted to numerically appraise all of the proposed methods. [ABSTRACT FROM AUTHOR]

In the weighted flow-time problem on a single machine, we are given a set of n jobs, where each job has a processing requirement pj, release date rj, and weight wj. The goal is to find a preemptive schedule which minimizes the sum of weighted flow-time of jobs, where the flow-time of a job is the difference between its completion time and its released date. We give the first pseudopolynomial time constant approximation algorithm for this problem. The algorithm also extends directly to the problem of minimizing the lp norm of weighted flow-times. The running time of our algorithm is polynomial in n, the number of jobs, and P, which is the ratio of the largest to the smallest processing requirement of a job. Our algorithm relies on a novel reduction of this problem to a generalization of the multicut problem on trees, which we call the Demand MultiCut problem. Even though we do not give a constant factor approximation algorithm for the Demand MultiCut problem on trees, we show that the specific instances of Demand MultiCut obtained by reduction from weighted flow-time problem instances have more structure in them, and we are able to employ techniques based on dynamic programming. Our dynamic programming algorithm relies on showing that there are near optimal solutions which have nice smoothness properties, and we exploit these properties to reduce the size of the dynamic programming table. [ABSTRACT FROM AUTHOR]

Heterogeneous computer systems are becoming mainstream due to their disparate processing and performance capabilities. These systems consist of different types of devices, i.e., central processing units (CPUs), accelerators, and graphics processing units (GPUs). In the heterogeneous computing environment, if one device is more powerful in terms of computing capability, the scheduling schemes generally favor the powerful device, and that device becomes overloaded, while the other device is underutilized. This load imbalance problem results in increased execution time. In this research, we propose load-balanced task scheduler combined with machine learning-based device predictor. The device predictor is used to predict execution time both on CPU and GPU devices, and a device with shorter predicted execution time is considered as a suitable device for that particular task. However, it may happen that a high fraction of tasks map only on one type of device since that device is considered as a suitable device for them. It is due to the fact that a task is mapped to one device (with lower predicted execution time), although it can be executed on the other device as well. In this context, one device may become overloaded, while the other device may be underutilized. To solve this problem of load imbalance, we use work-stealing-based task scheduler as part of our solution that allows an idle device to process tasks from the queue of another's device. In this way, we can avoid load imbalance, minimize the overall execution time of tasks, and maximize the device utilization and throughput. We evaluate the performance of our proposed solution into two stages. Firstly, we measure the error rate of our machine learning predictor using three different algorithms (i.e., random forest, gradient boosting, and multiple linear regression). We demonstrate that random forest performs better with marginal error rate. Secondly, we compare the performance of work-stealing task scheduler with other scheduling alternatives. Our results show that the proposed solution reduces execution time by 65.63%, increased resource utilization by 93.3%, and throughput by 65.5% in comparison with baseline scheduling schemes. [ABSTRACT FROM AUTHOR]

To address the processing scheduling problem involving multiple molds, components, and floors, we propose the Genetic Grey Wolf Optimizer (GGA) as a means to optimize the production scheduling of components in a production line. This approach combines the Grey Wolf algorithm with the genetic algorithm. Previous methods have overlooked the storage requirements arising from the delivery characteristics of prefabricated components, often resulting in unnecessary storage costs. Intelligent algorithms have been demonstrated to be effective in production scheduling, and thus, to enhance the efficiency of prefabricated component production scheduling, our study presents a model incorporating a production objective function. This model takes into account production resources and delivery characteristics constraints. Subsequently, we develop a hybrid algorithm, combining the grey wolf algorithm with the genetic algorithm, to search for the optimal solution with a minimal storage cost. We validate the model using a case study, and the experimental results demonstrate that GAGWO successfully identifies the best precast production schedule. Furthermore, the precast production plan, considering the delivery method, is found to be reasonable. [ABSTRACT FROM AUTHOR]

Hybrid computer systems combine compute units (CUs) of different nature like CPUs, GPUs and FPGAs. Simultaneously exploiting the computing power of these CUs requires a careful decomposition of the applications into balanced parallel tasks according to both the performance of each CU type and the communication costs among them. This paper describes the design and implementation of runtime support for OpenMP hybrid GPU-CPU applications, when mixed with GPU-oriented programming models (e.g. CUDA/HIP). The paper describes the case for a hybrid multi-level parallelization of the NPB-MZ benchmark suite. The implementation exploits both coarse-grain and fine-grain parallelism, mapped to compute units of different nature (GPUs and CPUs). The paper describes the implementation of runtime support to bridge OpenMP and HIP, introducing the abstractions of Computing Unit and Data Placement. We compare hybrid and non-hybrid executions under state-of-the-art schedulers for OpenMP: static and dynamic task schedulings. Then, we improve the set of schedulers with two additional variants: a memorizing-dynamic task scheduling and a profile-based static task scheduling. On a computing node composed of one AMD EPYC 7742 @ 2.250 GHz (64 cores and 2 threads/core, totalling 128 threads per node) and 2 × GPU AMD Radeon Instinct MI50 with 32 GB, hybrid executions present speedups from 1.10× up to 3.5× with respect to a non-hybrid GPU implementation, depending on the number of activated CUs. [ABSTRACT FROM AUTHOR]

In this paper, the single-machine scheduling problems under the common and slack due date assignments are studied, where the actual processing time of the job needs to consider some factors, such as convex resource allocation, maintenance activity, and learning effects. The goal of this study is to find the optimal sequence, maintenance activity location, resource allocation and common due date (flow allowance). The objective function is (1) to minimize the sum of scheduling cost (including the weighted sum of earliness, tardiness and common due date (flow allowance), where the weights are position-dependent weights) and resource consumption cost, and (2) to minimize the scheduling cost under the resource consumption cost which is bounded. We prove that these problems can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

Cloud computing describes computer services provided through the internet and includes a wide range of virtualization resources. Because cloud computing is made up of a sizable number of heterogeneous autonomous systems with an adaptable computational architecture, it has been widely adopted by many businesses. The scheduling and management of resource utilization, however, have become more difficult as a result of cloud computing. Task scheduling is crucial, and this procedure must schedule tasks on the virtual machine while using the least amount of time possible. Utilizing an effective scheduling strategy enhances and expedites cloud computing services. Optimization techniques are used to resolve cloud scheduling problems. The purpose of this research is to address tasks distribution within the system to improve system performance overall and reduce task execution time. Two well-known optimization algorithms (the Bat, and Harmony search algorithms) were used in this approach as well as a combination approach Bat Algorithm Harmony Search ( BAHS ) that integrates the two. When compared to the other algorithms used for task scheduling, the (BAHS) method was chosen because it is flexible and produces effective results. Tests were run on a dataset that was created randomly. The suggested algorithm results were compared to the other popular algorithms in the field. The results show that the suggested swarm-based scheduling techniques can produce more accurate results than those of the competing algorithms in terms of the makespan, mean, and standard division. [ABSTRACT FROM AUTHOR]

In classical schedule problems, the actual processing time of a job is a fixed constant, but in the actual production process, the processing time of a job is affected by a variety of factors, two of which are the learning effect and resource allocation. In this paper, single-machine scheduling problems with resource allocation and a time-dependent learning effect are investigated. The actual processing time of a job depends on the sum of normal processing times of previous jobs and the allocation of non-renewable resources. With the convex resource consumption function, the goal is to determine the optimal schedule and optimal resource allocation. Three problems arising from two criteria (i.e., the total resource consumption cost and the scheduling cost) are studied. For some special cases of the problems, we prove that they can be solved in polynomial time. More generally, we propose some accurate and intelligent algorithms to solve these problems. [ABSTRACT FROM AUTHOR]

In the network control system, due to the limitation of network bandwidth and control system conditions, the information transmission of sampling and control must be completed in a certain time, otherwise it will cause information transmission delay, thus reducing the control performance of the network system. This paper builds a MOOC political teaching platform based on task scheduling and artificial intelligence system, and the use of MOOC ideological and political education model can improve students' participation and practicability. Under the MOOC model, students should be able to learn by participating in online video courses, skillfully applying theoretical knowledge of ideological and political education, and then by participating in physical classroom discussions. The simulation results show that the system can realize real-time data transmission, and the information transmission of sampling and control must be completed in a certain time, otherwise it will cause information transmission delay, thus reducing the control performance of the network system. Due to the limitation of network bandwidth, if multiple control systems work together, there will be transmission delays and other situations, which will directly affect the control performance of the system and cause system instability. [ABSTRACT FROM AUTHOR]

In enterprise management, there are often multiple agents competing for the same products to reduce production cost. On this basis, this paper investigates a two-agent slack due-date single-machine scheduling problem with deteriorating jobs, where the processing time of a job is extended as a function of position-dependent workload, resource allocation and a common deterioration rate. The goal is to find the optimal sequence and resource allocation that minimizes the maximal value of earliness, tardiness, and decision variables of one agent subject to an upper bound on cost value of the second agent. Through theoretical analysis, a polynomial time algorithm with O (N 3) time is proposed for the problem, where N is the maximum number of jobs between the two agents. [ABSTRACT FROM AUTHOR]

We consider the problem of weighted throughput in the single machine preemptive scheduling with continuous controllable processing times. A set of tasks can be scheduled on a single machine. Each task j is associated with a nonnegative weight w j , a release date, a due date, and an interval of possible processing times. A task j can either be scheduled with a total processing time p j which is in the given interval, or rejected (not participating in the schedule). The reward for processing j for p j time units is w j p j , and we are interested in constructing a feasible preemptive schedule such that the sum of rewards is maximized. We present a dynamic programming algorithm that solves the problem in pseudo-polynomial time and use it to obtain an FPTAS. Afterward, as our main contribution we propose an interesting efficient frontier approach for improved complexity bounds. [ABSTRACT FROM AUTHOR]

This paper considers the single-machine problem with job release times and flexible preventive maintenance activities to minimize total weighted tardiness, a complicated scheduling problem for which many algorithms have been proposed in the literature. However, the considered problems are rarely solved by genetic algorithms (GAs), even though it has successfully solved various complicated combinatorial optimization problems. For the problem, we propose a trajectory-based immigration strategy, where immigrant generation is based on the given information of solution extraction knowledge matrices. We embed the immigration strategy into the GA method to improve the population's diversification process. To examine the performance of the proposed GA method, two versions of GA methods (the GA without immigration and the GA method with random immigration) and a mixed integer programming (MIP) model are also developed. Comprehensive experiments demonstrate the effectiveness of the proposed GA method by comparing the MIP model with two versions of GA methods. Overall, the proposed GA method significantly outperforms the other GA methods regarding solution quality due to the trajectory-based immigration strategy. [ABSTRACT FROM AUTHOR]

The big data explosion has sparked a strong demand for high-performance data processing. Meanwhile, the rapid development of networked computing systems, coupled with the growth of Divisible-Load Theory (DLT) as an innovative technology with competent scheduling strategies, provides a practical way of conducting parallel processing with big data. Existing studies in the area of DLT usually consider the scheduling problem with regard to fine-grained divisible workloads. However, numerous big data loads nowadays can only be abstracted as coarse-grained workloads, such as large-scale image classification, context-dependent emotional analysis and so on. In view of this, this paper extends DLT from fine-grained to coarse-grained divisible loads by establishing a new multi-installment scheduling model. With this model, a subtle heuristic algorithm was proposed to find a feasible load partitioning scheme that minimizes the makespan of the entire workload. Simulation results show that the proposed algorithm is superior to the up-to-date multi-installment scheduling strategy in terms of achieving a shorter makespan of workloads when dealing with coarse-grained divisible loads. [ABSTRACT FROM AUTHOR]

Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species' obligate brood parasitism (laying eggs in other species' nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior. [ABSTRACT FROM AUTHOR]

Cloud computing has become one of the most popular frameworks for big data processing and analysis. To have better performance, the data chunks and their corresponding process nodes must be near each other, which is known as data locality. Moreover, to meet the deadline constraints, the available processing power next to the data locations is crucial. Considering the time and resource constraints, designing an appropriate job scheduler is essential for the effective management of memory and processing capacity. These concerns coalesce into the problem of joint data placement and job scheduling with data locality and deadline constraints. In this paper, two kinds of job sets are determined, one with equal deadlines and the other one with various deadlines. The problem of joint data placement and job scheduling with a single deadline is formulated as the problem of bin packing with splittable items and cardinality constraints, which is strongly NP-hard. Moreover, this paper proposes the polynomial-time Meddal algorithm to find a feasible solution with a few processing nodes. Experiment results have shown that the proposed method is effective, with the number of required nodes reduced by 33 % when compared to the algorithms Cred and First Fit. [ABSTRACT FROM AUTHOR]

The prefabricated building construction (PBC) project is sensitive to uncertainties due to the highly required coordination and interdependency among the installation activities, which may cause progress delay. Hence, it is necessary to monitor the installation progress and evaluate the schedule in terms of the project duration to take proactive control actions to avoid actual project delay. This study focuses on intelligent monitoring and evaluation for the PBC schedule by combining the computer vision‐based (CVB) technology, a weighted kernel density estimation (WKDE) method, and the earned duration management (EDM) method. Intelligent and real‐time far‐field detection of the prefabricated components (PCs) and workers is achieved through the CVB technology, which is, respectively, used to measure the progress status of the PC installation works and other manual works by means of the WKDE method. The PBC project duration is then predicted based on the monitored progress status to evaluate the schedule through the EDM method. The proposed intelligent schedule monitoring and evaluation method have been illustrated and justified through a field application. This study contributes to achieving intelligent schedule monitoring and evaluation for the PBC project. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule a given application onto the available machines in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The proposed approach is called Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF). Our proposed approach starts by listing the tasks according to priority defined by Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution by applying Greedy Randomized Adaptive Search Procedure (GRASP) and finally schedules tasks onto machines by applying Enhanced Binary Artificial Bee Colony (BABC). Further, several modifications are considered with BABC to improve the local searching process by applying circular shift operator then mutation operator on the food sources of the population considering the improvement rate. The proposed approach is simulated and implemented in the WorkflowSim which extends the existing CloudSim tool. The performance of the proposed approach is compared with Heterogeneous Earliest Finish Time (HEFT) algorithm, Deadline Heterogeneous Earliest Finish Time (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) and standard Binary Artificial Bee Colony (BABC) algorithm using different sizes of tasks and various benchmark workflows. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization. [ABSTRACT FROM AUTHOR]

With the rapid development of projects, firms are facing challenges in planning and controlling complex multiple construction projects. This research, therefore, aims at developing blockchain of optimal scheduling and sequencing of multiple construction projects under probabilistic arrival and stochastic durations. Each project task was considered as a block. Then, a framework for electronic project recording (EPR) system was developed. The EPRs are records for project tasks that make information available directly and securely to authorized users. In this framework, two optimization models were developed for scheduling and sequencing project blocks. The scheduling model aims to assign project tasks to available resources at minimal total cost and maximal the number of assigned project tasks. On the other hand, the sequencing model seeks to determine the start time of block execution while minimizing delay costs and minimizing the sum of task's start times. The project arrival date and the task's execution duration were assumed probabilistic and stochastic (normally distributed), respectively. The developed EPR system was implemented on a real case study of five projects with total of 121 tasks. Further, the system was developed when the task's execution duration follows the Program Evaluation and Review Technique (PERT) model with four replications. The project costs (idle time and overtime costs) at optimal plan were then compared between the task's execution duration normally distributed and PERT modelled. The results revealed negligible differences between project costs and slight changes in the sequence of project activities. Consequently, both distributions can be used interchangeably to model the task's execution duration. Furthermore, the project costs were also compared between four solution replications and were found very close, which indicates the robustness of model solutions to random generation of task's execution duration at both models. In conclusion, the developed EPR framework including the optimization models provided an effective planning and monitoring of construction projects that can be used to make decisions through project progress and efficient sharing of project resources at minimal idle and overtime costs. Future research considers developing a Blockchain of optimal maintenance planning. [ABSTRACT FROM AUTHOR]

The energy Internet operation platform provides market entities such as energy users, energy enterprises, suppliers, and governments with the ability to interact, transact, and manage various operations. Owing to the large number of platform users, complex businesses, and large amounts of data-mining tasks, it is necessary to solve the problems afflicting platform task scheduling and the provision of simultaneous access to a large number of users. This study examines the two core technologies of platform task scheduling and multiuser concurrent processing, proposing a distributed taskscheduling method and a technical implementation scheme based on the particle swarm optimization algorithm, and presents a systematic solution in concurrent processing for massive user numbers. Based on the results of this study, the energy internet operation platform can effectively deal with the concurrent access of tens of millions of users and complex task-scheduling problems. [ABSTRACT FROM AUTHOR]

Flow-shop scheduling is an extremely popular optimization model in industrial production, where each job must be scheduled on a series of machines following an identical process. The optimal criteria, makespan and maximum delivery–completion time, are investigated separately. A mixed integer programming model is built to evaluate the exact algorithm in simulation experiments. Given that these problems are NP hard and cannot be solved in polynomial time, exact and metaheuristic methods are proposed for different problem sizes. For small-scale instances, optimal schedules are achieved using an effective branch-and-bound algorithm, in which elaborately designed lower bound and branching rules significantly improve computational efficiency. For medium-scale instances, satisfactory solutions are obtained using a hybrid discrete differential evolution algorithm with improvement schemes to promote its performance. The experimental results show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This article considers the min–max version of a single-machine scheduling problem with generalized due dates under processing time uncertainty. The objective is to minimize the maximum number of tardy jobs over all scenarios. For a problem with a common due date, denoted d, it is shown that the case with a fixed number of scenarios is weakly NP-hard and has no fully polynomial time approximation scheme, although it has a polynomial time approximation scheme. Furthermore, it is shown that the case with an arbitrary number of scenarios has no α-approximation algorithm for any constant 1 < α < d. For a problem with identical due date intervals, it is shown that the case with two scenarios is strongly NP-hard and has no α-approximation algorithm for any constant α > 1. As a practical solution approach, a genetic algorithm is proposed and numerical experiments are conducted to verify its performance. [ABSTRACT FROM AUTHOR]

In modern discrete flexible manufacturing systems, dynamic disturbances frequently occur in real time and each job may contain several special operations in partial-no-wait constraint due to technological requirements. In this regard, a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named hierarchical multi-agent proximal policy optimization (HMAPPO) is developed to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem (DMOFJSP-PNW) with new job insertions and machine breakdowns. The proposed HMAPPO contains three proximal policy optimization (PPO)-based agents operating in different spatiotemporal scales, namely, objective agent, job agent, and machine agent. The objective agent acts as a higher controller periodically determining the temporary objectives to be optimized. The job agent and machine agent are lower actuators, respectively, choosing a job selection rule and machine assignment rule to achieve the temporary objective at each rescheduling point. Five job selection rules and six machine assignment rules are designed to select an uncompleted job and assign the next operation of which together with its successors in no-wait constraint on the corresponding processing machines. A hierarchical PPO-based training algorithm is developed. Extensive numerical experiments have confirmed the effectiveness and superiority of the proposed HMAPPO compared with other well-known dynamic scheduling methods. Note to Practitioners—The motivation of this article stems from the need to develop real-time scheduling methods for modern discrete flexible manufacturing factories, such as aerospace product manufacturing and steel manufacturing, where dynamic events frequently occur, and each job may contain several operations subjected to the no-wait constraint. Traditional dynamic scheduling methods, such as metaheuristics or dispatching rules, either suffer from poor time efficiency or fail to ensure good solution quality for multiple objectives in the long-term run. Meanwhile, few of the previous studies have considered the partial-no-wait constraint among several operations from the same job, which widely exists in many industries. In this article, we propose a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named HMAPPO to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem (DMOFJSP-PNW) with new job insertions and machine breakdowns. The proposed HMAPPO uses three DRL-based agents to adaptively select the temporary objectives and choose the most feasible dispatching rules to achieve them at different rescheduling points, through which the rescheduling can be made in real time and a good compromise among different objectives can be obtained in the long-term schedule. Extensive experimental results have demonstrated the effectiveness and superiority of the proposed HMAPPO. For industrial applications, this method can be extended to many other production scheduling problems, such as hybrid flow shops and open shop with different uncertainties and objectives. [ABSTRACT FROM AUTHOR]

A mixed-criticality system refers to an integrated embedded system in which tasks with different criticality levels run on a shared computing platform. In the design and development of mixed-criticality systems, how to schedule tasks to ensure that high-criticality tasks are executed in time and low-criticality tasks are served as much as possible is a major problem to be studied. Existing studies tend to consider pessimistic processing strategies to ensure the schedulability of functional tasks with high-criticality requirements. However, excessive pessimistic processing can lead to waste of system resources, thereby reducing the performance of functional tasks with low-criticality requirements. In this paper, we propose an adaptive-service-level adjustment strategy for low-criticality tasks, which solves the problem of waste of resources caused by invalid compensation in the low-criticality task compensation method of flexible mixed-criticality systems. In view of the problem that the existing methods mostly use static budget allocation and static independent mode switching without considering the actual operation of the task, this paper also proposes a flexible and dynamic mixed-criticality system scheduling scheme and designs a system execution framework, scheduling algorithm, and dynamic allocation strategy of maximum execution budget, in order to reduce unnecessary redundant resource expenditures and system switching costs and to improve the performance of low-criticality tasks. Experiments show that the proposed methods are effective compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Recently, wide-area distributed computing environments have become popular owing to their huge resource capability. In a wide-area distributed computing environment, joint scheduling of tasks and data is the main strategy to improve system performance. However, the geographically distributed diverse resources exhibit high variations, making it challenging to design efficient joint scheduling of tasks and data. To accurately adapt to the dynamic variations of geographically distributed diverse resources and achieve a high system performance, this study proposes a hypergraph-partitioning-based online joint scheduling method. The proposed method constructs a hypergraph of geographically distributed tasks, data, and diverse resources to clearly describe the correlation among the three elements and quantitatively reflect the time cost of different process in the environment. The hypergraph is dynamically updated according to the generated scheduling scheme and the collected information to reflect the dynamic variations of resource states. Then, a hypergraph partition optimization mechanism is proposed to generate efficient joint scheduling schemes, thus reducing the overall completion time in the system. The experimental results indicate that compared with the state-of-the-art joint scheduling methods, the proposed method reduces the overall completion time by up to 25.67% and significantly reduces the task waiting time, although it makes a concession in the data migration time. [ABSTRACT FROM AUTHOR]

Integer linear programs of configurations, or configuration IPs, are a classical tool in the design of algorithms for scheduling and packing problems where a set of items has to be placed in multiple target locations. Herein, a configuration describes a possible placement on one of the target locations, and the IP is used to choose suitable configurations covering the items. We give an augmented IP formulation, which we call the module configuration IP. It can be described within the framework of n-fold integer programming and, therefore, be solved efficiently. As an application, we consider scheduling problems with setup times in which a set of jobs has to be scheduled on a set of identical machines with the objective of minimizing the makespan. For instance, we investigate the case that jobs can be split and scheduled on multiple machines. However, before a part of a job can be processed, an uninterrupted setup depending on the job has to be paid. For both of the variants that jobs can be executed in parallel or not, we obtain an efficient polynomial time approximation scheme (EPTAS) of running time f (1 / ε) · poly (| I |) . Previously, only constant factor approximations of 5/3 and 4 / 3 + ε , respectively, were known. Furthermore, we present an EPTAS for a problem where classes of (non-splittable) jobs are given, and a setup has to be paid for each class of jobs being executed on one machine. [ABSTRACT FROM AUTHOR]

This paper deals with simultaneous scheduling of machines and tools with alternate machines in a multimachine flexible manufacturing system (FMS) to minimize makespan (MS). Only one copy of each type of tools is made available due to economic restrictions, and the tools are stored in a central tool magazine (CTM) shared with and serves for several machines. The problem is to select machines from alternate machines for job-operations, allocation of tools to job-operations, and job-operations’ sequencing on machines for MS minimization. This paper presents a nonlinear mixed integer programming (MIP) formulation to model the combined scheduling of machines and tools with alternate machines and a symbiotic organisms search algorithm (SOSA) built on the symbiotic interaction strategies that organisms employ to continue to exist in the ecosystem for solving the scheduling of machines and tools with alternate machines. The results have been tabulated and analyzed. There will be reduction in MS when the alternate machines are considered for job-operation. [ABSTRACT FROM AUTHOR]

This article deals with the creation of new methods and algorithms for resource scheduling in heterogeneous distributed computing systems using the example of cloud computing environments (CCEs) that reduce the execution time of many incoming tasks by using those computing resources which give the highest real performance in relation to a specific task received. To this end, it is proposed to apply a multiagent approach to organizing the scheduling process: each element of the CCE includes a software agent that has the most complete and up-to-date information about the features of its computer, and the set of these agents jointly select the most appropriate tasks and subtasks taking into account the available information. The principles of construction and the method of operation of the adaptive multiagent CCE resource manager and the algorithms for the operation of resource agents and tasks are described, and the efficiency of the algorithms developed is studied using a distributed software model. [ABSTRACT FROM AUTHOR]

This paper considers the single-group scheduling models with Pegels' and DeJong's learning effect and the single-group scheduling models with Pegels' and DeJong's aging effect. In a classical scheduling model, Pegels' and DeJong's learning effect is a constant or position-dependent, while the learning effect and aging effect are job-dependent in this paper. Compared with the classical learning model and aging model for scheduling, the proposed models are more general and realistic. The objective functions are to minimize the total completion time and makespan. We propose polynomial time methods to solve all the studied problems. [ABSTRACT FROM AUTHOR]

The off-line scheduling scheme of existing wireless sensor network is improved in this paper. Firstly, Bayesian method is introduced and Poisson distribution parameter followed by the number of sensor nodes is regarded as a random variable. We estimate the parameter by Bayesian posteriori estimate and obtain Bayesian estimated correction value. Then, we discuss the relationship between instantaneous event capture probability, average capture probability, average capture energy efficiency and Bayesian estimate value of the distribution parameter. Finally, considering the fact that Bayesian estimation value can be adjusted automatically after posterior samples are included, we propose an online scheduling scheme for asynchronous wireless sensor network. Through simulation comparison between online scheduling scheme and off-line scheduling scheme, our results show that online scheduling scheme can lower the probability of failure to capture stochastic events, increase the probability of capturing events and further save the energy of wireless sensor network, and be more flexible to capturing stochastic events. Moreover, expanding the perception radius of sensors can also enhance capture efficiency on the basis of controlling the working duration of wireless sensor network. [ABSTRACT FROM AUTHOR]

Assembly scheduling problems have broad applications in discrete manufacturing enterprises and hence gained extensive attention in academics. However, some important and actual factors including lot streaming, batch transfer of components and mixed-model assembly are yet not well studied. To adapt to actual production and enhance competitiveness, a three-stage mixed-model assembly job shop scheduling with lot streaming and batch transfer is investigated in this work. To tackle this problem, spatial-temporal links among three stages with differentiated production lot size are established and a novel mathematical model is formulated to minimize total completion time and production costs including setup costs, transport costs, and inventory costs. Besides, an improved multi-objective coevolutionary simulated annealing algorithm is developed. To promote the coevolution of processing, transfer, and assembly sub-problems, the multi-attribute combination rule, similarity rule, and consistency rule are respectively designed for each sub-problem. An adaptive execution strategy of sub-problems is trained by Q-learning to maximize the overall benefits. Experimental results show that both the discovered rule and the adaptive strategy are effective, and the proposed algorithm significantly outperforms seven competitive multi-objective algorithms in fixing the studied problem. More importantly, the developed production mode of "processing with lot streaming-batch transfer-mixed-model assembly considering pull production" has a better comprehensive performance than the other three actual production modes. • Build a MILP model for the three-stage MAJSP with lot streaming and batch transfer • Propose a multi-attribute combination rule, similarity rule and consistency rule • Develop a Q-learning-based adaptive execution strategy to promote coevolution • Put a mode for the coordination and synchronization of production and logistics [ABSTRACT FROM AUTHOR]

The toolpath smoothing and feedrate scheduling in computer numerical control (CNC) system are critical for high-speed and high-precision machining. The existing toolpath smoothing schemes perform insufficiently in raising the smoothness of feedrate trajectory. Aiming at increasing the smoothness of toolpath and reducing machining vibration, a smooth S-curve acceleration/deceleration(AD) control algorithm based on the jounce limited profile is proposed. In the paper, for the curves that do not meet the complete smooth S-curve acceleration/deceleration conditions, they are divided into three cases according to the length of the curve and give a detailed calculation formula. All trajectory parameters are derived with the analytical algorithm to ensure an acceptable computational complexity. Finally, a series of numerical simulations and actual machining and measurement experiments are conducted, and the simulation and experimental results have showed the good performance of the proposed algorithm compared with previous method both in motion smooth and surface quality. [ABSTRACT FROM AUTHOR]

Planning problems are particularly important for the production processes of precast reinforced concrete elements. Currently used modeling of these processes is based on the flow shop problem. Flow shop models are usually used in Enterprise Resource Planning systems, which, however, may not take into account the specifics of the production of such elements. The article presents a new model for scheduling the production of reinforced concrete prefabricated elements, which is distinguished by the possibility of carrying out activities by more than one working group. An additional new constraint is the possibility of parallel performance of some works, which may occur during their production. Also, there will be an individual order of elements assumed for each of the activities. New objective functions will be considered - the sum of idle times of working groups and the total type changes of precast components. The presented scheduling model contains an NP-hard discrete optimization problem. For this reason, metaheuristics were used in the article to solve optimization problems: the simulated annealing algorithm and the tabu search algorithm. Verification of the results obtained with the use of these algorithms confirmed their high efficiency. The application of the presented scheduling model illustrates a practical case study showing the effectiveness of the used algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with machines, tool transporter (TT) and tools simultaneous scheduling in multi machine flexible manufacturing system (FMS) with the lowest possible number of copies of every tool type without tool delay taking into account tool transfer times to minimize makespan (MSN). The tools are stored in a central tool magazine (CTM) that shares with and serves for several machines. The problem is to determine the lowest possible number of copies of every tool variety, allocation of copies of tools to job-operations, job-operations' sequencing on machines and corresponding trip operations of TT, including the dead heading trip and loaded trip times of TT without tool delay for MSN minimization. This paper proposes nonlinear mixed integer programming (MIP) formulation to model this simultaneous scheduling problem and crow search algorithm (CSA) built on the crows' intelligent behavior to solve this problem. The results have been tabulated, analyzed and compared. [ABSTRACT FROM AUTHOR]

Heterogeneous multi-core processors (HMP) are dual-objective hardware platforms which integrate both high-performance and low power consumption processors. Investigation of simultaneous fairness and energy efficiency could widen their applicability and reveal their strengths. This paper proposes a scheduling framework considering energy efficiency, shared resource contention, and fairness for heterogeneous multi-core processors. The presented framework is implemented and evaluated on a real HMP platform. The obtained experimental results via SPEC CPU2006 benchmark indicate that the proposed framework surpasses Linux and four other schedulers in terms of fairness (58% on average) and energy efficiency (37% on average). The source code of the proposed framework and the opponent algorithms are available online at https://github.com/baghers/CEEF. [ABSTRACT FROM AUTHOR]