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A comparison of genetic algorithm and artificial bee colony approaches in solving blocking hybrid flowshop scheduling problem with sequence dependent setup/changeover times |
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| รหัสดีโอไอ | |
| Creator | 1. Pongpan Nakkaew 2. Nantachai Kantanantha 3. Wuthichai Wongthatsanekorn |
| Title | A comparison of genetic algorithm and artificial bee colony approaches in solving blocking hybrid flowshop scheduling problem with sequence dependent setup/changeover times |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2559 |
| Journal Title | KKU Engineering Journal |
| Journal Vol. | 43 |
| Journal No. | 2 |
| Page no. | 62-68 |
| Keyword | Genetic algorithm,Artificial bee colony,Sequence,Blocking,Flowshop |
| ISSN | 0125-8274 |
| Abstract | In manufacturing process where efficiency is crucial in order to remain competitive, flowshop is a common configuration in which machines are arranged in series and products are produced through the stages one by one. In certain production processes, the machines are frequently configured in the way that each production stage may contain multiple processing units in parallel or hybrid. Moreover, along with precedent conditions, the sequence dependent setup times may exist. Finally, in case there is no buffer, a machine is said to be blocked if the next stage to handle its output is being occupied. Such NP-Hard problem, referred as Blocking Hybrid Flowshop Scheduling Problem with Sequence Dependent Setup/Changeover Times, is usually not possible to find the best exact solution to satisfy optimization objectives such as minimization of the overall production time. Thus, it is usually solved by approximate algorithms such as metaheuristics. In this paper, we investigate comparatively the effectiveness of the two approaches: a Genetic Algorithm (GA) and an Artificial Bee Colony (ABC) algorithm. GA is inspired by the process of natural selection. ABC, in the same manner, resembles the way types of bees perform specific functions and work collectively to find their foods by means of division of labor. Additionally, we apply an algorithm to improve the GA and ABC algorithms so that they can take advantage of parallel processing resources of modern multiple core processors while eliminate the need for screening the optimal parameters of both algorithms in advance. |
