Abstract
Determining an optimal schedule to minimise the completion time of the last job abandoning the system (makespan) become a very difficult problem when there are more than two machines in the flow shop. Due, both to its economical impact and complexity, attention to solve this problem has been paid by many researchers. Starting with the Johnson’s exact algorithm for the twomachine makespan problem [1], over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling (FSSP) by means of well-known heuristics which, are successfully used for certain instances of the problem and providing a single acceptable solution. Current trends to solve the FSSP involve Evolutionary Computation and Ant Colony paradigms. This work shows different bio-inspired heuristics for the FSSP, including hybrid versions of enhanced multirecombined evolutionary algorithms and ant colony algorithms [2], on a set of flow shop scheduling instances. A discussion on implementation details, analysis and a comparison of different approaches to the problem is shown.
Keywords
- Flow Shop
- Flow Shop Schedule Problem
- Travelling Salesperson Problem
- Flow Shop Problem
- Multiple Knapsack Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Esquivel, S., Leguizamón, G., Zuppa, F., Gallard, R. (2002). A Performance Comparison of Alternative Heuristics for the Flow Shop Scheduling Problem. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_6
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