KPI Science News (Mar 2021)
TIGHT-TARDY PROGRESSIVE IDLING-FREE 1-MACHINE PREEMPTIVE SCHEDULING WITH JOB PRIORITY WEIGHTS BY HEURISTIC’S EFFICIENT JOB ORDER INPUT
Abstract
Background. In setting a problem of minimizing total weighted tardiness by the heuristic based on remaining available and processing periods, there are two opposite ways to input the data: the job release dates are given in either ascending or descending order. It was recently ascertained that scheduling a few equal-length jobs is expectedly faster by ascending order, whereas scheduling 30 to 70 equal-length jobs is 1.5 % to 2.5 % faster by descending order. For the number of equal-length jobs between roughly 90 and 250, the ascending job order again results in shorter computation times. In the case when the jobs have different lengths, the significance of the job order input is much lower. On average, the descending job order input gives a tiny advantage in computation time. This advantage decreases as the number of jobs increases. Objective. The goal is to ascertain whether the job order input is significant in scheduling by using the heuristic for the case when the jobs have different lengths with job priority weights. Job order efficiency will be studied on tight-tardy progressive idling-free 1-machine preemptive scheduling. Methods. To achieve the said goal, a computational study is carried out with a purpose to estimate the averaged computation time for both ascending and descending orders of job release dates. First, the computation time for the ascending job order input is estimated for a series of job scheduling problems. Then, in each instance of this series, job lengths, priority weights, release dates, and due dates are reversed making thus the respective instance for the descending job order input, for which computation time is estimated as well. Results. The significance of the job order input is much lower than that for the case of jobs without priorities. With assigning the job priority weights, the job order input becomes further “dithered”, adding randomly scattered priority weights to randomly scattered job lengths and partially randomized due dates. On average, the descending job order input is believed to give a tiny advantage in computation time in scheduling up to 100 jobs. However, this advantage, if any (being tinier than that in the case of random job lengths without priorities), quickly vanishes as the number of jobs increases. Conclusions. It is better to compose job scheduling problems which would be closer to the case with equal-length jobs without priorities, where the saved computational time can be counted in hours. Even if the job lengths and priority weights are scattered, it is recommended to artificially “flatten” them. When artificial manipulations over job processing periods and job priority weights are impossible, it is recommended to use the descending job order input in scheduling up to 100 jobs, and either job order input in scheduling more than 100 jobs, although substantial benefits are not expected in this case.
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