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Symmetry 2019, 11(2), 165; https://doi.org/10.3390/sym11020165

A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

1
School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India
2
Department of IT and Computer Engineering Qazvin Branch, Islamic Azad University, Qazvin 1519534199, Iran
3
Department Hamun Islamic Azad University of Qazvin 1519534199, Iran
4
Department of Computer Engineering, Tehran North Branch, Islamic Azad University, Tehran 1651153311, Iran
5
Young Researchers and Elite Club, Ayatollah Amoli Branch, Islamic Azad University, Amol 4865116915, Iran
6
School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410004, China
*
Author to whom correspondence should be addressed.
Received: 17 December 2018 / Revised: 27 January 2019 / Accepted: 28 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
Full-Text   |   PDF [1281 KB, uploaded 1 February 2019]   |  

Abstract

In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem. View Full-Text
Keywords: Dynamic job-shop; Parallel Machines; Maximum flow-time of components; Genetic Algorithm Dynamic job-shop; Parallel Machines; Maximum flow-time of components; Genetic Algorithm
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Sangaiah, A.K.; Suraki, M.Y.; Sadeghilalimi, M.; Bozorgi, S.M.; Hosseinabadi, A.A.R.; Wang, J. A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines. Symmetry 2019, 11, 165.

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