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Optimal job scheduling is a class of optimization problems related to scheduling. The inputs to such problems are a list of jobs (also called processes or tasks) and a list of machines (also called processors or workers). The required output is a schedule – an assignment of jobs to machines. The schedule should optimize a certain objective ...
The modified due date scheduling is a scheduling heuristic created in 1982 by Baker and Bertrand, [1] used to solve the NP-hard single machine total-weighted tardiness problem. This problem is centered around reducing the global tardiness of a list of tasks which are characterized by their processing time, due date and weight by re-ordering them.
Uniform machine scheduling (also called uniformly-related machine scheduling or related machine scheduling) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. We are given n jobs J 1, J 2, ..., J n of varying processing times, which need to be scheduled on m different machines.
Single-machine scheduling or single-resource scheduling or Dhinchak Pooja is an optimization problem in computer science and operations research. We are given n jobs J 1 , J 2 , ..., J n of varying processing times, which need to be scheduled on a single machine, in a way that optimizes a certain objective, such as the throughput .
Identical-machines scheduling is an optimization problem in computer science and operations research.We are given n jobs J 1, J 2, ..., J n of varying processing times, which need to be scheduled on m identical machines, such that a certain objective function is optimized, for example, the makespan is minimized.
The list scheduling algorithm by Garey and Graham [9] has an absolute ratio , as pointed out by Turek et al. [10] and Ludwig and Tiwari. [11] Feldmann, Sgall and Teng [ 12 ] observed that the length of a non-preemptive schedule produced by the list scheduling algorithm is actually at most ( 2 − 1 / m ) {\displaystyle (2-1/m)} times the ...
The task with the highest priority for which all dependent tasks have finished is scheduled on the worker which will result in the earliest finish time of that task. This finish time depends on the communication time to send all necessary inputs to the worker, the computation time of the task on the worker, and the time when that processor ...
A 1.945-competitive algorithm was presented by Karger, Philips and Torng in 1994. [11] In 1992, Albers provided a different algorithm that is 1.923-competitive. [12] Currently, the best known result is an algorithm given by Fleischer and Wahl, which achieves a competitive ratio of 1.9201. [13] A lower bound of 1.852 was presented by Albers. [14]