Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm
Flexible manufacturing system (FMS) has been introduced by the researchers as an integrated manufacturing environment. Automated guided vehicles (AGVs) introduced as the main tool of material handling systems in FMS. While the scheduling of AGVs and machines are highly related; simultaneous sched...
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| フォーマット: | 学位論文 |
| 言語: | English English |
| 出版事項: |
2009
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| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/7354/1/FK_2009_45a.pdf http://psasir.upm.edu.my/id/eprint/7354/ |
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| 要約: | Flexible manufacturing system (FMS) has been introduced by the researchers as an
integrated manufacturing environment. Automated guided vehicles (AGVs)
introduced as the main tool of material handling systems in FMS. While the
scheduling of AGVs and machines are highly related; simultaneous scheduling of
machines and AGVs has been proposed in the literature. Genetic algorithm (GA)
proposed as a robust tool for optimization of scheduling problems. Setting the proper
crossover and mutation rates are of vital importance for the performance of the GA.
Fuzzy logic controllers (FLCs) have been used in the literature to control key
parameters of the GA which is addressed as fuzzy GA (FGA). A new application of
FGA method in simultaneous scheduling of AGVs and machines is presented. The
general GA is modified for the aforementioned application; more over an FLC is
developed to control mutation and crossover rates of the GA. The objective of
proposed FGA method is to minimize the makespan, production completion time of all jobs that they are produced simultaneously. An optimal sequence of operations is
obtained by GA. There is a heuristic algorithm to assign the AGVs to the operations.
As the main findings, the performance of GA in simultaneous scheduling of AGVs
and machines is enhanced by using proposed method, furthermore a new mutation
operator has been proposed. Several experiments have been done to the proposed test
cases. The results showed that tournament selection scheme may outperform roulette
wheel in this problem. Various combinations of population size and number of
generations are compared to each other in terms of their objective function. In large
scale problems FGA method may outperforms GA method, while in small and
medium problems they have the same performance. The fluctuation of obtained
makespan in FGA method is less than GA method which means that it is more
probable to find a better solution by FGA rather than GA. |
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