Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times

Zhou, Shengchao; Jin, Mingzhou; Du, Ni

doi:10.1016/j.energy.2020.118420

Cited by 32 publications

(18 citation statements)

References 43 publications

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“…The user creates a script file, including an executable file and input parameters needed for the execution of his/her application program in a certain format based on the job scheduler and submits it to the batch job scheduler. The batch job scheduler matches the user-requested resources and available resources and allocates available resources sequentially according to a certain service policy, involving first in first out (FIFO) [3], fairness-ware [4], or energy-efficient [5]. Thereafter, the user job is executed using the allocated computing resources.…”

Section: Introductionmentioning

confidence: 99%

Queue congestion prediction for large-scale high performance computing systems using a hidden Markov model

2022

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To share limited, large-capacity resources, the high-performance computing field provides services by allocating available resources to jobs through batch job schedulers. Therefore, it is natural that a queue waiting time occurs until the resources are available if resources are not sufficient. The prediction of queue waiting time is very useful to improve overall resource utilization. However, the queue waiting time is very difficult to predict because it is significantly affected by the many factors such as applied scheduling algorithm and characteristics of the executed job. In this study, a method of predicting queue waiting time using only the historical log data created by the batch job scheduler is examined. Specifically, a method of predicting queue waiting time based on a hidden Markov model is proposed. It has the following three stages. First, outliers are removed by applying the outlier detection algorithm using a statistics-based parametric method. Second, the parameters of the hidden state are estimated using the observed queue waiting time sequence based on the historical job log. Third, the queue waiting interval at time $$t+1$$ t + 1 is provided using the estimated parameters at time t. Comparing the prediction accuracy with those of the other prediction methods, experimental results show that the proposed algorithm improves the prediction accuracy by up to 60%.

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Section: Introductionmentioning

confidence: 99%

Queue congestion prediction for large-scale high performance computing systems using a hidden Markov model

2022

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“…Zarook et al, (2014) suggested a new mathematical model to minimize makespan on the single batch-processing machine scheduling problem with considering machine's aging effect and maintenance activities, they proposed two meta-heuristic algorithms (GA & ICA) to solve the real size problem. Zhou et al, (2020) presented a multiobjective model for a single batch processing machine scheduling problem with dynamic job arrival times, due to computational complexity, they proposed a hybrid multi-objective metaheuristic algorithm to find the Pareto front. Another machine environment in the BP is identical batch processing in the parallel machines that machines have similarity characters.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times

et al. 2021

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This paper considers the minimization of makespan in the unrelated parallel batch processing machines scheduling problem with considering non-identical job size and dynamic job ready time. The considered unrelated machines have different capacity and different processing speed. Each machine processes a number of the jobs as a batch at the same time so that the machine’s capacity is not exceeded. The batch processing time and the batch ready time are equal to the largest processing time and the largest ready time of jobs in the same batch, respectively. In this paper, a Mixed Integer Linear Programming (MILP) model, two categories of the heuristic procedures (six heuristics) and a meta-heuristic algorithm are proposed to solve the problem. A lower bound is also presented by relaxing of the original problem to evaluate the quality of the proposed algorithms. The computational experiments show the performance of the proposed algorithms under the considered measures.

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“…Currently, there is an increasing number of publications about EES [16][17][18][19][20][21][22]. Different models of machine energy consumptions processes [14] and all shop floor types have been investigated in the literature, such as single machine scheduling [23][24][25][26][27], flow shop [28][29][30][31][32][33][34][35][36][37][38][39][40][41], job shop [42][43][44][45][46][47], and hybrid systems for specific issues [48][49][50]. For many machine tools, the idling power consumption is only slightly less than the operating power consumption [51].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times

Kampa

Paprocka

2021

Energies

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The aim of this paper is to present a model of energy efficient scheduling for series production systems during operation, including setup and shutdown activities. The flow shop system together with setup, shutdown times and energy consumption are considered. Production tasks enter the system with exponentially distributed interarrival times and are carried out according to the times assumed as predefined. Tasks arriving from one waiting queue are handled in the order set by the Multi Objective Immune Algorithm. Tasks are stored in a finite-capacity buffer if machines are busy, or setup activities are being performed. Whenever a production system is idle, machines are stopped according to shutdown times in order to save energy. A machine requires setup time before executing the first batch of jobs after the idle time. Scientists agree that turning off an idle machine is a common measure that is appropriate for all types of workshops, but usually requires more steps, such as setup and shutdown. Literature analysis shows that there is a research gap regarding multi-objective algorithms, as minimizing energy consumption is not the only factor affecting the total manufacturing cost—there are other factors, such as late delivery cost or early delivery cost with additional storage cost, which make the optimization of the total cost of the production process more complicated. Another goal is to develop previous scheduling algorithms and research framework for energy efficient scheduling. The impact of the input data on the production system performance and energy consumption for series production is investigated in serial, parallel or serial–parallel flows. Parallel flow of upcoming tasks achieves minimum values of makespan criterion. Serial and serial–parallel flows of arriving tasks ensure minimum cost of energy consumption. Parallel flow of arriving tasks ensures minimum values of the costs of tardiness or premature execution. Parallel flow or serial–parallel flow of incoming tasks allows one to implement schedules with tasks that are not delayed.

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Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times

Cited by 32 publications

References 43 publications

Queue congestion prediction for large-scale high performance computing systems using a hidden Markov model

Queue congestion prediction for large-scale high performance computing systems using a hidden Markov model

Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times

Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times

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