Parallel machine scheduling with splitting jobs in MapReduce system

被引：0
|
作者：
Huang J.-D. [1 ]
Zheng F.-F. [1 ]
Xu Y.-F. [1 ]
Liu M. [2 ]
机构：
[1] Glorious Sun School of Business and Management, Donghua University, Shanghai
[2] School of Economics and Management, Tongji University, Shanghai
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 07期
关键词：
Job splitting; MapReduce; Mixed integer programming; Parallel machines scheduling; Parallel processing; Whale optimization algorithm;
D O I：
10.13195/j.kzyjc.2017.1677
中图分类号：
学科分类号：
摘要：
Based on the MapReduce model, a two-phase parallel machine scheduling problem is studied. In the model, each job consists of two operations named Map and Reduce. The Map operation can be split and processed simultaneously, while the Reduce shall be processed on a single machine. Considering the arrival time, and due date of each job, we establish a mixed integer linear programming (MILP) model, aiming at minimizing the weighted makespan and total tardiness. An improved whale optimization algorithm (IWOA) is proposed, which uses differential perturbation and dimension-by-dimension Levy perturbation to obtain a near-optimal solution. The numerical results show that the IWOA outperforms both the particle swarm optimization and the whale optimization algorithms for the considered problem. © 2019, Editorial Office of Control and Decision. All right reserved.
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页码：1514 / 1520
页数：6
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