Different Number Of Jobs Research Articles

Internet of Things Data Cloud Jobs Scheduling Using Modified Distance Cat Swarm Optimization

IoT cloud computing provides all functions of traditional computing as services through the Internet for the users. Big data processing is one of the most crucial advantages of IoT cloud computing. However, IoT cloud job scheduling is considered an NP-hard problem due to the hardness of allocating the clients’ jobs to suitable IoT cloud provider resources. Previous work on job scheduling tried to minimize the execution time of the job scheduling in the IoT cloud, but it still needs improvement. This paper proposes an enhanced job scheduling mechanism using cat swarm optimization (CSO) with modified distance to minimize the execution time. The proposed job scheduling mechanism first creates a set of jobs and resources to generate the population by randomly assigning the jobs to resources. Then, it evaluates the population using the fitness value, which represents the execution time of the jobs. In addition, we use iterations to regenerate populations based on the cat’s behaviour to produce the best job schedule that gives the minimum execution time for the jobs. We evaluated the proposed mechanism by implementing an initial simulation using Java Language and then conducted a complete simulation using the CloudSim simulator. We ran several experimentation scenarios using different numbers of jobs and resources to evaluate the proposed mechanism regarding the execution time. The proposed mechanism significantly reduces the execution time when we compare the proposed mechanism against the firefly algorithm and glowworm swarm optimization. The average execution time of the proposed cat swarm optimization was 131, while the average execution times for the firefly algorithm and glowworm optimization were 237 and 220, respectively. Hence, the experimental findings demonstrated that the proposed mechanism performs better than the firefly algorithm and glowworm swarm optimization in reducing the execution time of the jobs.

Three-Machine Flowshop Scheduling Problem to Minimize Makespan with Interval Processing Times

The three-machine flowshop scheduling problem with interval processing times, where processing times are within a lower and an upper bound, is studied. The objective is to minimize makespan. Given that the problem is NP-hard, some heuristics are proposed. The proposed heuristics are assessed based on numerous experiments. The data for the experiments were randomly generated for different number of jobs and different distributions, where both symmetric and skewed distributions are considered. The number of replications considered for each combination of the number of jobs and the distribution was one hundred. Error of each heuristic was reported. Computational experiments indicated that one of the proposed heuristics significantly performs better than the other heuristics.

Minimizing total tardiness in a two-machine flowshop with uncertain and bounded processing times

The two-machine flowshop scheduling problem with the performance measure of total tardiness is addressed. This performance measure is essential since meeting deadlines is a crucial part of scheduling and a major concern for some manufacturing systems. The processing times on both machines are uncertain variables and within some lower and upper bounds. This is due to uncertainty being an integral part of some manufacturing settings, making it impossible to predict processing times in advance. To the best of the author’s knowledge, this problem is addressed for the first time in this paper. A dominance relation is established and nineteen algorithms are proposed. These algorithms are extensively evaluated through randomly generated data for different numbers of jobs and four different distributions, representing both symmetric and non-symmetric distributions. Computational experiments show that the presented algorithms perform extremely well when compared with a random solution. In particular, the best of the considered 19 algorithms reduces the error of the random solution by 99.99% and the error of the worst algorithm (among the 19 algorithms) by 99.96%. The results are confirmed by a test of hypothesis and this algorithm is recommended.

A heuristic approach to minimize three criteria using efficient solutions

In <span>optimization, scheduling problems is concerning allocations of some resources which are usually limited. These allocations are done in order to fulfil some criterion by performing some tasks or jobs to optimize one or more objective functions. Simultaneous multi-criteria scheduling problem is known as np-hard optimization problem. Here, we consider three criteria for scheduling a number of jobs on a single machine. The problem is to minimize the sum of total completion time, maximum earliness and maximum tardiness. Every job is to be processed without interruption and becomes available for processing at time zero. The aim is to find a processing order of the jobs to minimize three-objective functions simultaneously. We present a new heuristic approach to find a best overall solution (accepted) of the problem using efficient solutions of one of the other related criteria. We establish a result to restrict the range of the optimal solution, and the lower bound depends on the decomposition of the problem into three subproblems. The approach is tested on a set of problems of different number of jobs. Computational results demonstrate the efficiency of the proposed approach.</span>

NEW PLANNING SOLUTIONS FOR PRODUCTION SITES OF VEHICLE ENTERPRISES

The article proposes the new planning solutions for the production sites and the layout of the production building of vehicle enterprises, including with in-line labor organization. (Research purpose) The research purpose is in developing and justifying the best planning solutions for the main production sites of vehicle enterprises. (Materials and methods) The basis for optimizing the planning solutions of production sites is the graph-analytical methods of research and calculation. (Results and discussion) In the areas of daily maintenance and maintenance No. 1, joined with workstations for diagnosing systems and aggregates, it is advisable to move cars between positions using a plate conveyor. The article justifies the choice of the number of different jobs. The organization of washing and cleaning works with regeneration of the cleaning solution, providing effective environmental protection has been proposed. Using the previously described method of selecting the composite center of production sites, the optimal layout of the production building with the location relative to the specified center of the associated production sites and warehouses was selected. The compositional center of the vehicle enterprise is a site of routine maintenance, equipped with dead-end or travel ditches and equipment in accordance with the nature of the work performed. Maintenance work No. 2 is performed at dead-end workplaces due to a small production program. The article presents the layouts of the main production sites. (Conclusions) The proposed technical solutions increase the technical level of vehicle enterprises, minimize the transport work on moving serviced and repaired products inside the production building and its production area.

The effect evaluation of Individual Placement and Support (IPS) for patients with substance use disorders: study protocol for a randomized controlled trial of IPS versus enhanced self-help

BackgroundEmployment is associated with better outcomes of substance use treatment and protects against relapse after treatment completion. Unemployment rates are high for people with substance use disorders (SUD) who undergo treatment, with Norwegian estimates ranging from 81 to 91%. Evidence-based vocational models are lacking for patients in SUD treatment but exist for patients with psychosis in terms of Individual Placement and Support (IPS). The aim of the IPS for substance use disorders (IPS-SUD) trial is to investigate the effect of IPS in a SUD population.Methods/designThe IPS-SUD trial is a randomized controlled trial (RCT) comparing IPS to an enhanced control intervention. The study is a seven-site, two-arm, pragmatic, parallel-group, superiority RCT. Participants are randomly assigned (1:1) to receive either IPS plus treatment as usual (TAU) or to receive a self-help guide book and 12-h workshop plus 1-h individual vocational guidance plus TAU. Aiming to recruit 200 participants, we will be able to detect a 20% difference in the main outcome of employment with 90% power. We will make assessments at inclusion and at 6- and 12-month follow-ups and obtain outcome data on employment from national mandatory registries. The primary outcome will be at least 1 day of competitive employment during the 18-month follow-up period. Secondary employment outcomes will capture the pattern and extent of employment in terms of total time worked (days/hours), time to first employment, number of different jobs, duration of the longest employment, and sustained employment. Secondary non-employment outcomes will be substance use, mental distress, and quality of life measured by validated instruments at 6, 12, and 18 months follow-up assessments. To be eligible, participants must be between 18 and 65 years, currently unemployed and in treatment for SUD.DiscussionThe IPS-SUD trial will provide evidence for the use of IPS in a SUD population. Findings from the study will have implications for service delivery.Trial registrationClinicalTrials.gov NCT04289415. Registered on February 28, 2020

Algorithms for four-machine flowshop scheduling problem with uncertain processing times to minimize makespan

We consider the four-machine flowshop scheduling problem to minimize makespan where processing times are uncertain. The processing times are within some intervals, where the only available information is the lower and upper bounds of job processing times. Some dominance relations are developed, and twelve algorithms are proposed. The proposed algorithms first convert the four-machine problem into two stages, then, use the well-known Johnson’s algorithm, known to yield the optimal solution for the two-stage problem. The algorithms also use the developed dominance relations. The proposed algorithms are extensively evaluated through randomly generated data for different numbers of jobs and different gaps between the lower and upper bounds of processing times. Computational experiments indicate that the proposed algorithms perform well. Moreover, the computational experiments reveal that one of the proposed algorithms, Algorithm A7, performs significantly better than the other eleven algorithms for all possible combinations of the number of jobs and the gaps between the lower and upper bounds. More specifically, error percentages of the other eleven algorithms range from 2.3 to 27.7 times that of Algorithm A7. The results have been confirmed by constructing 99% confidence intervals and tests of hypotheses using a significance level of 0.01.

Considering stockers in reentrant hybrid flow shop scheduling with limited buffer capacity

Diversification of products has increased the involvement of reentrant manufacturing processes, in which a job returns multiple times to a machine at the preceding workflow stage to continue the manufacturing process. Reentrant flow shop manufacturing can substantially improve manufacturing efficiency when scheduled properly. In practice, advanced manufacturing companies (e.g., semiconductor foundries) have introduced automated material handling system (AMHS), including stockers that serve as centralized inventory buffer space for temporarily storing the inventories owing to limited buffer capacity of each machine. However, no previous studies on reentrant flow shop scheduling have considered the impact of limited buffer capacity or stockers on scheduling efficiency. Consequently, this study investigated the application of stockers in solving the reentrant hybrid flow shop scheduling problem with limited buffer capacity. With the objective of optimizing the makespan and mean flowtime of a schedule, this problem is NP-hard because it generalizes the flow shop problem. Therefore, this study developed a hybrid harmony search and genetic algorithm (HHSGA) for the problem, in which limited buffer capacity and stockers cause solution decoding to be non-trivial. Experimental comparison on scheduling problems with different numbers of jobs showed that the HHSGA performed better than conventional algorithms. Moreover, among three manufacturing conditions (i.e., with buffers and stockers, with buffers only, and with stockers only), the results indicated that the condition using inventory buffers and stockers was more beneficial.

Heuristics for periodical batch job scheduling in a MapReduce computing framework

Task scheduling has a significant impact on the performance of the MapReduce computing framework. In this paper, a scheduling problem of periodical batch jobs with makespan minimization is considered. The problem is modeled as a general two-stage hybrid flow shop scheduling problem with schedule-dependent setup times. The new model incorporates the data locality of tasks and is formulated as an integer program. Three heuristics are developed to solve the problem and an improvement policy based on data locality is presented to enhance the methods. A lower bound of the makespan is derived. 150 instances are randomly generated from data distributions drawn from a real cluster. The parameters involved in the methods are set according to different cluster setups. The proposed heuristics are compared over different numbers of jobs and cluster setups. Computational results show that the performance of the methods is highly dependent on both the number of jobs and the cluster setups. The proposed improvement policy is effective and the impact of the input data distribution on the policy is analyzed and tested.

Scheduling of Jobs on Identical Parallel Machines to Minimize Total Flow Time Subject to Optimal Makespan

Algorithms based on Simulated Annealing and Tabu search has been proposed and implemented on scheduling a problem of parallel machines. The identical parallel machine scheduling problem has been considered to minimize the total flow-time subject to optimal makespan. The proposed algorithms have two phases. In the first phase, an initial solution has been obtained using Longest Processing Time (LPT) dispatching rule and in the second phase, simulated annealing and tabu search have been applied to reach a near optimal solution. The performance of the both proposed algorithms have been evaluated by comparing their results for different number of jobs and processing times. The computational results indicate that the proposed Tabu Search algorithm is capable of obtaining better solutions for the given scheduling problem as compared to the Simulated Annealing algorithm. Although both of these algorithms provide the best solutions as compared to the other heuristic algorithms but in comparison of these two; Tabu Search provides the better solutions for the given problem.

Minimum CDT based Scheduling Algorithm versus FCFS in Grid Environment

To achieve the promising potentials of tremendous distributed resources, effective and efficient scheduling algorithms are fundamentally important. Unfortunately, scheduling algorithms in traditional parallel and distributed systems, which usually run on homogeneous and dedicated resources, e.g. computer clusters, cannot work well in the new circumstances. In this research paper, MJ_CDTmin [1] (multiple jobs based on the minimum cumulative departure time) algorithm is compared with the already existing FCFS (First Come First Serve) algorithm in terms of the execution time (in secs).Since there were no results for minimizing the execution time in for existing algorithms. Hence the comparison is done only for the proposed algorithms. This is achieved with the experimental test bed by specifying deadline, while submitting the jobs. Simulation was carried out by different number of jobs varying from 1000 to 10,000. In the experimental testing heterogeneous machines were used and tested for different number of tasks/jobs. During the experiment, the comparison was carried out by considering the six different values for the Service time ( of the jobs. The main aim of proposed scheduling algorithm is to increase the system efficiency and to satisfy the job requirements from the available resources. The experimental results showed a significant improvement in terms of a smaller makespan time as compared to the already existing FCFS scheduling algorithm. Keyword

Resource Allocation Mechanism with New Models for Grid Environment

Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

Algorithms for no-wait flowshops with total completion time subject to makespan

The m-machine no-wait flowshop scheduling problem, with the objective of minimizing total completion time subject to the constraint that makespan has to be less than or equal to a certain value, is addressed in this paper. First, an algorithm is proposed to find an upper bound on the makespan in case the upper bound is not given or unknown. Given the upper bound on makespan, a proposed algorithm (PAL) with five versions L (1, 5, 10, 15, and 20) and a genetic algorithm (GA) are utilized for solving the problem. Furthermore, a dominance relation is established for the case of four machines. The five versions of PAL and GA are evaluated on randomly generated problems with different number of jobs and number of machines. Computational experiments show that the errors of PA10, PA15, and PA20 are much smaller than that of GA while the CPU times of PA10, PA15, and PA20 are significantly smaller than that of GA. Therefore, the algorithms PA10, PA15, and PA20 are superior to the GA algorithm.

Maintenance parameters based production policies optimization

PurposePlanning of manufacturing and maintenance activities together, creating a balance between maintenance and production parameters and developments on maintenance will prevent technical and economic losses and increase production efficiency. Optimizing production and maintenance scheduling enable us to see how maintenance parameters (β, η, tp, tr, a[o]) will affect production performance, completion time (Ec.) and maximum machine availability, and shows which maintenance parameters minimum completion time (Ecmin) will be provided. Difference between Ecmin and maximum completion time (Ecmak) effect to the production costs will be calculated. The purpose of this paper is to show how a genetic algorithm (GA) procedure is successfully applied to the integrated optimization model to determine optimum production policies based maintenance parameters.Design/methodology/approachGA is used for optimization and a computer program is prepared to make optimization for integrated preventive maintenance and production planning (IPMPP). Using the program, experimental studies are carried out with different number of jobs be done, to optimize production policy taking maintenance parameters into account.FindingsNumerous experiments have been conducted with developed GA computer program and see maintenance parameters (β, η, tp, tr, a[o]) effect to the production performance, Ec and maximum machine availability and at which maintenance parameters Ecmin will be provided, and also operating cost saving and maintenance parameters how affect Ec subjects are examined. Due to optimal preventive maintenance (PM) and production sequence arrangement and application of PM provided by GA, Ecmin is greatly decreased.Originality/valueIn this paper, GA procedure is successfully applied to the integrated optimization model to determine optimum production policies based maintenance parameters.

Self-reported bullying in childhood: Relationships with employment in adulthood

Objective. To our knowledge, relationships between being bullied in childhood and employment functioning in adulthood have not been previously studied—hence the focus of the present study. Method. Using a cross-sectional approach and a self-report survey methodology in a consecutive sample of 328 internal medicine outpatients, we examined relationships between being bullied (i.e., have you ever been bullied, how many years, how many bullies) and history of employment (i.e., since age 18, number of full-time jobs, percentage of time employed, ever paid “under the table,” and ever fired). Results. Participants reporting a history of having been bullied reported a relatively greater number of different jobs as well as a greater likelihood of having been “paid under the table” and having been fired. While the number of years bullied evidenced no statistically significant correlations with employment variables, the number of different bullies was statistically significantly correlated with the number of different jobs held and ever having been fired. Conclusions. Findings indicate a relationship between being bullied in childhood and experiencing reduced employment viability in adulthood.

Heuristics for no-wait flowshops with makespan subject to mean completion time

This paper addresses the m-machine no-wait flowshop scheduling problem. Two performance measures are considered; makespan and mean completion time. The objective is to find a sequence that minimizes makespan such that mean completion time is not greater than a certain value. In case such a certain value is not known, an algorithm is presented to find an upper bound on the mean completion time. A heuristic, called HA, a simulated annealing (SA) heuristic, a modified simulated annealing (mSA) heuristic, and two more heuristics, called HH1 and HH2, which are combination of HA and mSA, are proposed for the problem. The proposed heuristics are assessed on randomly generated problems with different number of jobs and machines. Computational experiments revealed that the overall average relative errors of SA, HA, mSA, HH1 and HH2, were 2.99, 1.33, 0.78, 0.04, and 0.87, respectively. These results are also statistically verified. Therefore, the heuristic HH1 significantly outperforms the other heuristics.

Aggressive behavior and employment histories in patients from an internal medicine outpatient clinic

A number of studies have examined the relationships between aggression and various facets of the work environment. However, to our knowledge, the relationship between self-reported aggressive behaviors and corresponding employment histories has never been previously explored. Using a cross-sectional approach and a self-report survey methodology in a consecutive sample of 325 internal medicine outpatients, we examined lifetime histories of 21 aggressive behaviors and 4 employment variables. In both men and women, a higher number of aggressive behaviors correlated with a greater number of different jobs held, greater likelihood of being paid “under the table,” and a greater number of firings. However, only in men was the number of aggressive behaviors related to the percentage of time employed as an adult. According to findings, a history of aggressive behavior appears to be associated with compromised employment viability in adulthood.

The Abuse of Prescription Medications and Employment History

The relationship between prescription medication abuse and employment history is rarely studied and is the focus of the present study. Using a cross-sectional consecutive sample of 325 internal medicine outpatients and a self-report survey methodology, we examined prescription medication abuse ("Have you ever intentionally, or on purpose, abused prescription medications?") and four employment variables. We found statistically significant relationships between the abuse of prescription medications and all four employment variables--i.e., number of different jobs held since age 18 (5.05 in nonabusers versus 7.81 in past abusers), estimated percentage of time employed since age 18 (79.0% in nonabusers versus 63.3% in past abusers), being paid "under the table" (24.9% in nonabusers versus 77.4% in past abusers), and being fired (37.2% in non-abusers versus 64.5% in past abusers)--all indicating less employment viability in participants with past histories of prescription medication abuse. The abuse of prescription medications is associated with impaired functionality in the work arena.

Flexible service selection with user-specific QoS support in service-oriented architecture

Along with the development of the service-oriented architecture (SOA) and cloud computing, a large number of service providers have created an intense competitive world of business. Consequently, it is becoming increasingly complex to select a service provider for a user as a result of their various economic and social attributes. In this paper, we state the problem of how to find the appropriate services with satisfying the users' multiple QoS requirements. We consider the service's response time, trust degree and monetary cost. And inspired from the mode of Web search engine, such as Yahoo, Google, we propose an innovative service selection algorithm for SOA systems. The algorithm can recommend a number of suitable services based on the user's QoS requirements.Compared with the existing scheduling algorithms, our solution is much more flexible in supporting the multiple objectives and user personalization. We study the scalability of the algorithm with different numbers of jobs, service providers and QoS criteria. And we find that it can capture user's preferences value in less than six times of job submissions.

Makespan minimization of a flowshop sequence-dependent group scheduling problem

The flowshop sequence dependent group sched- uling problem with minimization of makespan as the objective (Fm|fmls, Splk, prmu|Cmax) is considered in this paper. It is assumed that several groups with different number of jobs are assigned to a flow shop cell that has m machines. The goal is to find the best sequence of processing the jobs in each group and the groups themselves with minimization of makespan as the objec- tive. A mathematical model for the research problem is developed in this paper. As the research problem is shown to be NP-hard, a hybrid ant colony optimization (HACO) algorithm is developed to solve the problem. A lower bounding technique based on relaxing a few constraints of the mathematical model developed for the original problem is proposed to evaluate the quality of the HACO algorithm. Three different problem structures, with two, three, and six machines, are used in the generation of the test problems to test the performance of the algorithm and the lower bounding technique developed. The results obtained from the HACO algorithm and those that have appeared in the published literature are also compared. The comparative results show that the HACO algorithm has a superior performance compared to the best available algorithm based on memetic algorithm with an average percentage deviation of around 1.0% from the lower bound.