A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines
| Autor: | Jin Wang, Ali Asghar Rahmani Hosseinabadi, Arun Kumar Sangaiah, Mohsen Yaghoubi Suraki, Mehdi Sadeghilalimi, Seyed Mostafa Bozorgi |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Genetic Algorithm
Physics and Astronomy (miscellaneous) Computer science Job shop General Mathematics lcsh:Mathematics 020208 electrical & electronic engineering 02 engineering and technology Parallel Machines Maximum flow-time of components Manufacturing systems Dynamic job-shop lcsh:QA1-939 Machine failure Bottleneck Scheduling (computing) Chemistry (miscellaneous) 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Meta heuristic 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Symmetry, Vol 11, Iss 2, p 165 (2019) Symmetry; Volume 11; Issue 2; Pages: 165 |
| ISSN: | 2073-8994 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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