Abstract
Job shop scheduling, especially the large scale job shop scheduling problem has earned a reputation for being difficult to solve. Genetic algorithms have demonstrated considerable success in providing efficient solutions to many non-polynomial hard optimization problems. But unsuitable parameters may cause poor solution or even no solution for a specific scheduling problem when evolution generation is limited. Many researchers have used various values of genetic parameters by their experience, but when problem is large and complex, they cannot tell which parameters are good enough to be selected since the trial and error method need unaffordable time-consuming computing. This paper attempts to firstly find the fittest control parameters, namely, number of generations, probability of crossover, probability of mutation, for a given job shop problem with a fraction of time. And then those parameters are used in the genetic algorithm for further more search operation to find optimal solution. For large-scale problem, this compound genetic algorithm can get optimal solution efficiently and effectively; avoid wasting time caused by unfitted parameters. The results are validated based on some benchmarks in job shop scheduling problems.
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Yong-Ming, W., Nan-Feng, X., Hong-Li, Y., Cheng-Gui, Z. (2007). Optimal Computing Budget Allocation Based Compound Genetic Algorithm for Large Scale Job Shop Scheduling. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_43
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DOI: https://doi.org/10.1007/978-3-540-71441-5_43
Publisher Name: Springer, Berlin, Heidelberg
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