Abstract
In this paper, we tackle the job shop scheduling problem (JSP) with skilled operators (JSPSO). This is an extension of the classic JSP in which the processing of a task in a machine has to be assisted by one operator skilled for the task. The JSPSO is a challenging problem because of its high complexity and because it models many real-life situations in production environments. To solve the JSPSO, we propose a genetic algorithm that incorporates a new coding schema as well as genetic operators tailored to dealing with skilled operators. This algorithm is analyzed and evaluated over a benchmark set designed from conventional JSP instances. The results of the experimental study show that the proposed algorithm performs well and at the same time they allowed us to gain insight into the problem characteristics and to draw ideas for further improvements.
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Mencía, R., Sierra, M.R., Varela, R. (2015). Genetic Algorithm for the Job-Shop Scheduling with Skilled Operators. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_5
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DOI: https://doi.org/10.1007/978-3-319-18833-1_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18832-4
Online ISBN: 978-3-319-18833-1
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