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Minimizing the weighted completion time of jobs in the unrelated parallel machines model is a fundamental scheduling problem. The first (3 / 2 - c) –approximation algorithm for this problem, for some constant c > 0 , was obtained in the work of Bansal et al. (SIAM J Comput, 2021). A key ingredient in this work was the first dependent-rounding algorithm with a certain guaranteed amount of negative correlation. We improve upon this guaranteed amount from 1/108 to 1/27, thus also improving upon the constant c in the algorithms of Bansal et al. and Li (SIAM J Comput, 2020) for weighted completion time. Given the now-ubiquitous role played by dependent rounding in scheduling and combinatorial optimization, our improved dependent rounding is also of independent interest. [ABSTRACT FROM AUTHOR]

The prediction of project completion time, which is important in project management, is only based on an estimate of three numbers, namely the fastest, slowest, and presumably time. The common practice of applying normal distribution through Monte Carlo simulation in Program Evaluation and Review Technique (PERT) research often fails to accurately represent project activity durations, leading to potentially biased project completion prediction. Based on these problems, a different method is proposed, namely, Discrete Event Simulation (DES). The research aims to evaluate the effectiveness of the simmer package in R in conducting PERT analysis. Specifically, there are three objectives in the research: 1) develop a simulation model to predict how long a project will take and find the critical path, 2) create an R script to simulate discrete events on a PERT network, and 3) explore the simulation output using the simmer package in the form of summary statistics and estimation of project risk. Then, a library research with a descriptive and exploratory method is used for data collection. The hypothetical network is used to obtain the numerical results, which provide the predicted value of the project completion, the critical path, and the risk level. Simulation, including 100 replications, results in a predicted project completion time and a standard deviation of 20.7 and 2.2 weeks, respectively. The DES method has been proven highly effective in predicting the completion time of a project described by the PERT network. In addition, it offers increased flexibility. [ABSTRACT FROM AUTHOR]

Summary: Resource management and job scheduling are essential in today's cloud computing world. Due to task scheduling and users' diverse submission of large‐scale requests, co‐located VM instances negatively impacted the performance of leased VM instances. This workload further led to resource rivalry across co‐located VMs. In order to address the aforementioned problems, numerous strategies have been presented, however, they fail to take the asynchronous nature of the cloud environment into account. To address this issue, a novel "CTA using DLFC‐NN model" is proposed. This proposed approach combines the coalition theory and DLFC‐NN techniques by including IRT‐OPTICS for task size clustering, digital metrology based on ionized information (DMBII) for defect detection in virtue machines (VM), and the dynamic levy flight hamster optimization algorithm for processing time optimization of the clusters. However, the implementation of task scheduling in an online environment is limited by a number of presumptions or oversimplifications made by current scheduling systems. As a result, a unique coalition theory is applied to efficiently schedule activities. In addition, the DLFC‐NN model is used to reduce resource consumption, span time, and be highly accurate and energy‐efficient when working on both online and offline jobs. Nevertheless, while optimizing the clusters' overall execution time, earlier approaches only decreased the make‐span time for task scheduling. However, the DLFC‐NN model solves the computation problem by using a fully weighted bipartite graph and the pseudo method to determine the fitness of the least makespan time. The enhanced methodology used in this study reduces the scheduling cost and minimizes job completion times according to different task counts when compared to the existing techniques. [ABSTRACT FROM AUTHOR]

To balance customer satisfaction across virtual and real-world interactions, we focus on enhancing service for dine-in customers at restaurants that typically prioritize online orders, such as those on Uber Eats. Utilizing three-agent scheduling strategies that adhere to each agent’s specific requirements—whether they are hard constraints or soft objectives—we effectively manage various types of orders, including immediate individual online orders, group reservations, and oral requests from dine-in customers. This approach significantly reduces waiting times and improves overall customer satisfaction. We propose a branch-and-bound algorithm with a tight lower bound based on preemption, which prioritizes agents A and B while reducing the total waiting time for agent C, representing dine-in customers. Computational experiments reveal that our algorithm significantly reduces total waiting times compared to existing two-agent scheduling strategies, demonstrating its effectiveness. Despite its efficiency, the algorithm incurs computational overheads, particularly with larger problem sizes. Our unique lower bound can be extended to other industries requiring multiple constraints or objectives. For example, in the film and television industry, real actors (represented by agent A) need to align their shooting times with 3D studios (represented by agent B) and stunt doubles for virtual avatars (represented by agent C) for scenes where they interact. That is, more agents are required to accommodate their constraints and objectives in such a scenario.

This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). The novelty of this study is fourfold. First, the model tackles the inventory problem along with the shortage amount to avoid the late fee. Second, due to the popularity of minimizing completion time (Makespan), each job is divided into small parts to be operated on various machines. Third, renewable resources are included to ensure the feasibility of the production process. Fourth, a mixed-integer linear programming formulation and the solution methodology are developed. To feed the metaheuristic algorithm with an initial viable solution, a heuristic algorithm is also fabricated. Also, the discrete version of the GWO algorithm for this specific problem is proposed to obtain the results. Our results confirmed that our proposed discrete GWO algorithm could efficiently solve a real case study in a timely manner. Finally, future research threads are suggested for academic and industrial communities. [ABSTRACT FROM AUTHOR]

This research zeroes in on improving the freezing process by synergistically employing a wavy wall and fins. To enhance cold penetration, the phase change material (PCM) is enriched with nanoparticles, and a single-phase model is adopted due to the low nanoparticle concentration. The numerical simulations leverage the Galerkin method and the validation procedure affirms the precision of the code, extensively evaluating the impacts of φ (concentration of additives) and dp (particle diameter). With an increase in particle diameter (dp), there is an initial 19.76% decrease in the required time, succeeded by a subsequent 50.56% increase when φ = 0.04. Furthermore, an escalation in φ results in an 11.04%, 40.91%, and 26.36% reduction in completion time for dp values of 50, 40, and 30 nm, respectively. Without the inclusion of powders, the solidification process lasts for 84.8 s. However, with the introduction of the optimal powder size, this duration significantly reduces to 50.1 s. This emphasizes the efficiency improvements attained through the strategic integration of a wavy wall, fins, and PCM infused with nanoparticles.

In this paper, the objective function is effectively optimized by improving the fitness function in the computer algorithm. The improvement mainly focuses on adjusting the weighting coefficients of completion time, load balance and execution cost. The article then proceeds to optimize the algorithm’s parameters based on the algorithm’s optimal parameterization criterion, and designs a hybrid hill-climbing-simulated annealing optimization algorithm based on the parameterized model. To verify the safety of the algorithm, avalanche effect experiments were conducted in the study. The experimental results show that when the number of iterations is 14, the number of changed bits reaches 32, indicating that the algorithm is susceptible to the avalanche effect. In addition, for the application effect of the algorithm, the study was tested in the Oliver30 dataset. When the number of iterations reaches 25000 times, the algorithm has reached the optimal value distance 432.82, which shows the superiority in urban path recognition performance. Meanwhile, the algorithm has achieved an accuracy 0.8286 for feature recognition in remote sensing datasets, with a maximum classification accuracy of more than 90%. The study provides critical practical data and theoretical support for further application of the computer algorithm.

In this paper, the bi-criteria machine scheduling problems (BMSP) are solved, where the discussed problem is represented by the sum of completion and the sum of late work times (1//(ΣCj, ΣVj)) simultaneously. In order to solve the suggested BMSP, some metaheurisitc methods are suggested which produce good results. The suggested local search methods are simulated annulling and bees algorithm. The results of the new metaheurisitc methods are compared with the complete enumeration method, which is considered an exact method, then compared results of the heuristics with each other to obtain the most efficient method. [ABSTRACT FROM AUTHOR]

This study aims to introduce an innovative method to schedule and analyze the discharge planning processes under uncertain individual activity completing times. Hospital care processes ending in discharging older adults represent a major challenge, as a number of interrelated tasks or activities are involved in the processes. The functional resonance analysis method (FRAM) is used to identify activities that constitute a descriptive model of the discharge planning processes and to assign the completion time to each task or activity. Then the critical path planning technique is utilized to identify the critical path of the discharge process. To this end, the Program Evaluation and Review Technique (PERT) is employed. Published data regarding six older patients are used to examine the potential utility of the introduced method to improve the discharge process of older adults. The results show that the integration of the FRAM and PERT is able to identify the critical activities that constitute the critical path of the discharge process. It also helps analyze the completion time assigned to each task/activity and calculate the completion time of the discharge process. The probability of completing the discharge process within a target completion time is also discussed. The results add further evidence showing the applicability of the FRAM and PERT to scheduling systems for the discharge planning processes. Health care professionals could use the findings of this study in designing a scheduling system to complete the discharge process of older adults in a reasonable time. [ABSTRACT FROM AUTHOR]

The technological advances we are witnessing today have stimulated the creation of many 3D virtual environments for various purposes, from entertainment to industry to education. While the majority of these environments are perfectly suited for the healthy population, we should not forget about impaired people living among us. Regarding children's education, one may wonder how impaired children handle them. Do they find them usable and attractive? How well do they handle basic activities in 3D environments, including orientation and interaction with objects? The experiment presented in this article provides answers to these questions within a specific setup. The experiment used a custom web application with several 3D virtual environments in a desktop virtual reality setting. The participants were 12 children, aged 8–14, with multiple impairments, predominantly hearing impairment, borderline and mild degree of mental retardation, and inferior communication skills. The answers can be regarded as positive and are based on results gathered in the form of completion times and the System Usability Scale questionnaire scores. The article also reports on a significant relation found between completion times and questionnaire scores. Future research directions, including those related to the Metaverse concept, are discussed, too. [ABSTRACT FROM AUTHOR]

We prove that in a stable multi-server system, where different machines are allowed to have different speeds, all work-conserving disciplines are asymptotic optimal for minimizing total completion time, if job size and interarrival time distributions have finite moments. As a byproduct of our analysis, we obtain a tight upper bound on the competitive ratios of work-conserving disciplines on minimizing the metric of flow time. [ABSTRACT FROM AUTHOR]

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers' actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others. [ABSTRACT FROM AUTHOR]

This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). The novelty of this study is fourfold. First, the model tackles the inventory problem along with the shortage amount to avoid the late fee. Second, due to the popularity of minimizing completion time (Makespan), each job is divided into small parts to be operated on various machines. Third, renewable resources are included to ensure the feasibility of the production process. Fourth, a mixed-integer linear programming formulation and the solution methodology are developed. To feed the metaheuristic algorithm with an initial viable solution, a heuristic algorithm is also fabricated. Also, the discrete version of the GWO algorithm for this specific problem is proposed to obtain the results. Our results confirmed that our proposed discrete GWO algorithm could efficiently solve a real case study in a timely manner. Finally, future research threads are suggested for academic and industrial communities.

A production run is typically referred to as a group of identical goods that is produced by a particular manufacturing process. In many discrete manufacturing practices, the manufacturing activity is carried out by deploying a series of production runs of different products according to customer orders. If the volume of a production run is relatively small and process changeovers are necessary, the production system operates partially (or entirely) in the transient regime, especially at the beginning and near the end of a production run. In this case, the traditional steady-state analysis approach may become inapplicable. In this paper, we consider finite production run-based manufacturing in serial lines with machines obeying the geometric reliability model and buffers having finite capacity. Exact Markovian analysis is first used to derive the closed-form formulae to calculate the transient performance of the production line during a production run as well as the distribution, mean, and standard deviation of its completion time in one- and two-machine lines. For multi-machine lines, an aggregation-based approach is proposed to approximate the system performance measures with high accuracy and computational efficiency. In addition, system-theoretic properties of production run completion time with respect to machine and buffer parameters are discussed. [ABSTRACT FROM AUTHOR]

One of the most valuable and promising applications for Unmanned aerial vehicles (UAVs) is in natural disaster management, where these aircraft can operate autonomously without any need for human intervention during their flights. In this paper, we foster the interface of Operational Research with computer science in general and sensor networking in particular by focusing on managing a post-disaster emergency scenario where the use of a fleet of UAVs helps rescue teams identify people needing help inside an affected area. We model this situation as an original graph theoretical problem called Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Time minimization (MDMT-VRP-TCT). The main novelty of the MDMT-VRP-TCT is the combination of the following three features: multi-depot, multi-trip, and completion time minimization. We propose a mixed-integer linear programming (MILP) formulation, develop a matheuristic framework to address large instances, and present an extended set of experiments to test the performance of the proposed matheuristic: first, we compare the matheuristic with the MILP formulation on a set of small instances (up to 30 nodes); then, we compare our matheuristic with two heuristics from networking literature, showing that it outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have been progressively used in environmental monitoring and disaster sensing. The drones fly over a set of waypoints in the area to achieve their surveillance task. Despite their potential advantages, UAVs have several drawbacks, including a limited energy supply and a short operational duration which make all attempts at planning ineffective. In this paper, we propose a novel UAV path planning for the monitoring task that considerably reduces energy consumption and minimizes the number of turns with minimum overlapping. In addition, the proposed path gives the whole area the same level of importance. The proposed path was compared with four static paths: back and forth, spiral, LMAT, and Zamboni. Results show that the proposed path allows a better coverage with less energy consumption than the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

In recent years, the increase in general interest in methods for measuring cognitive load and subjectively perceived mental effort when solving various tasks and in the interpersonal communication was accompanied by an increase in the specific interest of social researchers in the multimodal assessment of the cognitive load of interviewers and respondents based on objective and subjective indicators, including paradata and webcam data, in order to control this loads impact on the quality of survey data. The authors argue that the possibilities of relatively new approaches to measuring cognitive load with neurophysiological methods (such as the use of wearable devices for oculography - eye tracking and pupillometry - which do not disrupt the natural course of respondents and interviewers activity) are still underestimated, although they allow an accurate time linkage of measured parameters dynamics (primarily the size of the pupil) to the question format, mode and phase of survey completion, external influences localized in time, etc. As a rule, quantitative studies of surveys cognitive load and its possible impact on the quality of survey data focus on computer-assisted (CAPI) or paper-based (PAPI) interviewing, while the specificity of the cognitive load in the self-completed computerized (CASI) and paper (PPSI) surveys was not studied. The article presents the results of the methodological experiment based on a modified version of the multimodal approach to the comparative assessment of the cognitive load of interviewers working with paper and computerized questionnaire. We expanded the range of methods for assessing cognitive load by using a wearable oculographic device (eye tracker) to measure the dynamics of pupil size when answering different survey questions. The results of the experiment confirmed the hypothesis about the approximate equivalence of the two modes of survey completion in terms of their cognitive load for younger respondents with a high level of functional computer literacy, and allowed an initial assessment of the technical and metrological capabilities and limitations of the use of pupil dynamics indicators, measured with a wearable oculographic device, to study the respondents cognitive load.

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This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others.

Vertical-slot fishway (VSF) has been used in many water conservancy projects to restore the river connectivity. A high-quality fishway project should facilitate fish to discovering the exit and passing through, avoiding to long stay in the fishway and delay the migration. Current research on fishway engineering has not yielded an expected passing ratio of fish migration, and it is therefore of great significance to further study the assisting effect of VSF in fish migration. To begin with, we preliminarily determined the attractive and repelling colors of grass carps based on their swimming behavior in a static water pool configured with local colors. Combined with the migration route of the grass carp in a VSF pool without local coloring, four local coloring cases were designed. Based on the camera results of the four experimental local coloring cases, a comparative analysis was conducted with the blank control group frame by frame. This was followed by the statistics of the number of successfully migrated grass carps and their total completion time. On that basis, the assisting effect of VSF in fish migration under the four cases was evaluated in terms of the reduction rate of migration route length, the reduction rate of completion time, and the improvement rate of passing ratio. The research outcomes indicated that green and blue act as attractive colors while yellow and red serve as repelling colors for grass carp. Adding colors to the training wall and dividing wall in the VSF pool, the migration route of grass carp was appropriately adjusted, alongside a shortened completion time and an improved passing ratio. Of the four local coloring cases, the recommended case showed a significant effect on migration route, with more concentrated moving trajectories and shortened route length. Typically, the migration route length decreased by 26%, and the frequency of fish long staying at the junction between the training wall and dividing wall was markedly reduced, as well as the frequency of fish swimming along the water flow from upstream to downstream. The completion time was shortened by 26%, and the passing ratio was enhanced by 44%. The approach of combining local coloring with fish behavior and fishway hydraulics in the pool surpassed the method that optimizes the fishway design only from the fishway hydraulics. The improved method greatly shortened the migration route length, reduced the completion time, and significantly improved the passing ratio of fish passage objects in the VSF. The present research mainly focuses on using model experiments to evaluate the local coloring cases. In the future studies, we will configure local colors to the sidewalls of on-site fishways using environmentally friendly paint or colored organic glass panels. With the monitoring results of the completion time and passing ratio of fish passage objects, the recommended case can be further verified and optimized, thereby providing a more reasonable and feasible local coloring case for assisting fish migration in the VSF project. [Display omitted] • Four local coloring schemes were proposed and model experiments were carried out. • The migration route, passing ratio and completion time of grass carp were analyzed. • The recommended scheme which the passage behavior was reasonably adjusted, and the completion time was shortened by 26 % and the passing ratio was improved by 44 %. [ABSTRACT FROM AUTHOR]

In this research, a single batch machine scheduling problem with sequence dependent setup time is studied with the aim of minimizing the completion time. This problem has been proven to be NP-hard and therefore, a tabu search algorithm is developed to solve the single batch machine scheduling problem. Furthermore, a genetic algorithm and several dispatching heuristics are also developed to study the capabilities of all the algorithms in providing the average completion time. The computational result reveals that the developed tabu search algorithm is capable of producing a better outcome compared to the other algorithms. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate scheduling problems in manufacturing. After a brief introduction to the modelling approach to the scheduling problem, the study focuses on the optimization approach to the scheduling problem. Firstly, the different optimization approaches are categorised and their respective advantages and disadvantages are shown. This is followed by detailed analysis of the characteristics and applicability of each of the commonly used optimization approaches. Finally, a case study is presented. mathematical model is developed with the objective of minimising the maximum completion time for a mixed flow shop scheduling problem, and a genetic algorithm is used to solve the problem. The validity of the model is verified through the case study, which can provide a reasonable scheduling solution for actual manufacturing. This provides a reference for the selection and use of methods for solving scheduling problems in practical production. [ABSTRACT FROM AUTHOR]

The demand for real-time cloud applications has seen an unprecedented growth over the past decade. These applications require rapidly data transfer and fast computations. This article considers a scenario where multiple IoT devices update information on the cloud, and request a computation from the cloud at certain times. The time required to complete the request for computation includes the time to wait for computation to start on busy virtual machines, performing the computation, waiting and service in the networking stage for delivering the output to the end user. In this context, the freshness of the information is an important concern and is different from the completion time. This article proposes novel scheduling strategies for both computation and networking stages. Based on these strategies, the age-of-information (AoI) metric and the completion time are characterized. A convex combination of the two metrics is optimized over the scheduling parameters. The problem is shown to be convex and thus can be solved optimally. Moreover, based on the offline policy, an online algorithm for job scheduling is developed. Numerical results demonstrate significant improvement as compared to the considered baselines. [ABSTRACT FROM AUTHOR]

Given the unpredictable nature of the nodes in distributed computing systems, some of the tasks can be significantly delayed. Such delayed tasks are called stragglers. Straggler mitigation can be achieved by redundant computation. In maximum distance separable (MDS) redundancy method, a task is divided into $k$ subtasks which are encoded to $n$ coded subtasks, such that a task is completed if any $k$ out of $n$ coded subtasks are completed. Two important metrics of interest are task completion time, and server utilization which is the aggregate completed work by all servers in this duration. We consider a proactive straggler mitigation strategy where $n_{0}$ out of $n$ coded subtasks are started at time 0 while the remaining $n-n_{0}$ coded subtasks are launched when $\ell _{0}\le \min \left \{{n_{0},k}\right \}$ of the initial ones finish. The coded subtasks are halted when $k$ of them finish. For this flexible forking strategy with multiple parameters, we analyze the mean of two performance metrics when the random service completion time at each server is independent and distributed identically (i.i.d.) to a shifted exponential. From this study, we find a tradeoff between the metrics which provides insights into the parameter choices. Experiments on Intel DevCloud illustrate that the shifted exponential distribution adequately captures the random coded subtask completion times, and our derived insights continue to hold. [ABSTRACT FROM AUTHOR]

Most existing Cloudlet selection strategies only used the resources of one Cloudlet to compute offloading,which couldn’t obtain the superior performance improvement for mobile data streaming application with many parallel components.To address this issue,a Cloudlet selection strategy based on chemical reaction optimization algorithm was proposed.The strategy aims to reduce application’s completion time and energy consumption of mobile device.When the dependencies among application’s components was satisfied,the strategy can take full advantage of the computing capability of multi-cloudlet to execute the parallel components of mobile data stream application simultaneously.Therefore the strategy can improve the execution efficiency and reduce the energy consumption of mobile device.The simulation results reveal that the proposed strategy can achieves 18.3% on average performance improvement than POCSS strategy does in multi-Cloudlet environment.

With the widespread availability of multi-homed devices, enabling multiple paths by utilizing multipath TCP (MPTCP) in current networks is a common practice to improve the performance and robustness. Although MPTCP improves both bandwidth efficiency and network reliability, the transmission performance for short flows can become worse than regular single path TCP when the concurrent subflows transferred through a shared bottleneck due to MPTCP’s aggressive slow start behavior, which is uncoupled, and each subflow behaves independently as regular TCP, affecting MPTCP and concurrent traffic at the bottleneck. In this paper, we first reveal that the MPTCP’s aggressive behavior in slow start causes timeouts and throughput collapse. We further present the design and implementation of GSAM, which employs the theoretical analysis to derive the appropriate threshold for smoothing the congestion window growth in GSAM according to the network conditions in slow start phase and leverages congestion detection and control at end-host to avoid buffer overflow under concurrent MPTCP connections with multiple subflows sharing the bottleneck. The experimental results based on the real implementations show that the GSAM reduces the completion time by up to 80% while retaining high Goodput for large flows as MPTCP.

Task offloading is the hot issue for current network. The rapid development of intelligent terminals brings new opportunity for offloading. This paper studies the problem of task offloading in a mobile edge computing system, where both the fixed devices with power supply of electricity and mobile terminals are computation resources. Considering tasks in an application are not independent, the task-dependency is introduced to describe the relationship of tasks. Then, the offloading details are analyzed to illustrate energy consumption and completion time during task assignment. Based on task-dependency and offloading process, the problem of task offloading is formulated to minimize the completion time of whole applications under energy and time constraints. Since it is the NP-hard problem, the heuristic offloading algorithm is proposed to assign tasks of whole applications. In the proposed algorithm, based on the task urgency, tasks of whole applications are assigned to chosen devices to achieve the low offloading cost during the initial assignment. To reduce the completion time, the relative remaining cost is introduced to reassign tasks under constraints. Simulation results illustrate that the proposed heuristic algorithm significantly reduces the completion time under system constraints.

Cyber-Physical Production Systems (CPPS) are inherent to i4.0 to enhance actual manufacturing system control and management processes, and, consequently, the decision making process. This paper presents research on the influence of decision making duration time in CPPS on manufacturing system performance, for different scheduling paradigms. Different scenarios were investigated considering different decision making duration times and different variations of the environment dynamics. Results show that with the increase of the decision making duration time, the completion time of the jobs given at the input, as one of the principal manufacturing system performance measures, increases. Also, considering stable environments, the completion time variation growth in function of CPPS decision making duration time is approximately linear, while when considering dynamic environments, completion time variation growth in the function of CPPS decision making duration time is nonlinear.

Dyer, B. Insight into the development and competitiveness of male UK-based stand-up paddleboard flatwater distance racing from 2013 to 2017. J Strength Cond Res 35(2): 535-541, 2021-An analysis of 2 stand-up paddleboard "distance" events was undertaken to investigate any change in their participation, racing behavior, and whether such events should be approached differently by practitioners. The completion time paddler-to-paddler gap was investigated as a means of assessing the performances in 2 flatwater distance events from 2013 to 2017. The level of participation at these events had not noticeably changed. An analysis indicated that both events did not significantly change in their completion time paddler-to-paddler range from year to year when looking at 3 subgroups (p > 0.05) and did not differ significantly in behavior when normalized against each other (p > 0.05). Post hoc tests revealed that the paddler time gaps at an event held in 2015 were significantly different when compared with all the other years it had taken place (p < 0.05). The use of a performance intensity chart indicated that there was different finishing paddler-to-paddler gap behavior between the 2 events. Ultimately, it is proposed that athletes and coaches should be aware that 2 events with the same generic classification of being designated a "distance event" may warrant different training methodologies and tactical decision-making. [ABSTRACT FROM AUTHOR]

Abstract Flexible manufacturing system (FMS) readily addresses the dynamic needs of the customers in terms of variety and quality. At present, there is a need to produce a wide range of quality products in limited time span. On-time delivery of customers’ orders is critical in make-to-order (MTO) manufacturing systems. The completion time of the orders depends on several factors including arrival rate, variability, and batch size, to name a few. Among those, batch size is a significant construct for effective scheduling of an FMS, as it directly affects completion time. On the other hand, constant batch size makes MTO less responsive to customers’ demands. In this paper, an FMS scheduling problem with n jobs and m machines is studied to minimize lateness in meeting due dates, with focus on the impact of batch size. The effect of batch size on completion time of the orders is investigated under following strategies: (1) constant batch size, (2) minimum part set, and (3) optimal batch size. A mathematical model is developed to optimize batch size considering completion time, lateness penalties and setup times. Scheduling of an FMS is not only a combinatorial optimization problem but also NP-hard problem. Suitable solutions of such problems through exact methods are difficult. Hence, a meta-heuristic Genetic algorithm is used to optimize scheduling of the FMS.