A DECOMPOSITION-BASED OPTIMIZATION ALGORITHM FOR SCHEDULING LARGE-SCALE JOB SHOPS

被引：0

作者：

Zhang, Rui ^{[1
]}

Wu, Cheng ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2009年 / 5卷 / 09期

关键词：

Job shop scheduling problem; Decomposition; Simulated annealing; Particle swarm optimization; Bottleneck; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; PARAMETERS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A decomposition-based optimization algorithm is presented for large-scale job shop scheduling problems in which the total weighted tardiness should be minimized. The algorithm adopts an iterative optimization framework. In each iteration, a new subproblem is first defined by a simulated annealing approach and then solved using a particle swarm optimization algorithm. In order to promote the optimization efficiency, the jobs' bottleneck characteristic values are calculated and utilized as an immune mechanism to guide the subproblem-solving process. Numerical computations and comparisons are conducted for both randomly generated test problems and the real-life production environment of a speed-reducer factory in China. Experiment results reveal the unique advantages of the proposed algorithm over the existing methods.

引用

页码：2769 / 2780

页数：12

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