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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Raeesi N, M.R. Kobti, Z. |
| Copyright Year | 2011 |
| Description | Author affiliation: School of Computer Science, University of Windsor, Windsor, ON, Canada N9B-3P4 (Raeesi N, M.R.; Kobti, Z.) |
| Abstract | In this article, a new Memetic Algorithm (MA) has been proposed to solve Job Shop Scheduling Problems. The proposed MA is based on Machine Operation Lists (MOL), which is the exact sequence of operations for each machine. Machine Operation Lists representation is a modification of Preference List-Based representation. Linear Order Crossover (LOX) and Random operations are first considered as crossover and mutation operators for the proposed MA. Local Search heuristic (LS) of the proposed MA reconsiders all the operations of a job. It chooses a job and removes all of its operations and finally reassigns them again one by one in their sequencing order to improve the fitness value of the schedule. The proposed algorithm has been applied on the well-known benchmark of classical Job Shop Scheduling Problems (JSSP). Comparing it with the existing methods shows that the proposed MA and the proposed Genetic Algorithm (GA) without LS are effective in JSSP. Moreover, comparing the results of MA and GA shows that using LS not only improves the final results but also helps GA to converge to the final solution. |
| Starting Page | 2436 |
| Ending Page | 2443 |
| File Size | 1281901 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424478347 |
| e-ISBN | 9781424478354 |
| DOI | 10.1109/CEC.2011.5949919 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Schedules Job shop scheduling Biological cells Genetic algorithms Memetics System recovery Evolutionary computation |
| Content Type | Text |
| Resource Type | Article |
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