Abstract
We present a general methodology to model the behavior of heuristics for the Job-Shop Scheduling (JSS) that address the problem by solving conflicts between different operations on the same machine. Our models estimate the gaps between consecutive operations on a machine given measures that characteristics the JSS instance and those operations. These models can be used for a better understanding of the behavior of the heuristics as well as to estimate the performance of the methods. We tested it using two well know heuristics: Shortest Processing Time and Longest Processing Time, that were tested on a large number of random JSS instances. Our results show that it is possible to predict the value of the gaps between consecutive operations from on the job, on random instances. However, the prediction the relative performance of the two heuristics based on those estimates is not successful. Concerning the main goal of this work, we show that the models provide interesting information about the behavior of the heuristics.
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© 2009 Springer-Verlag Berlin Heidelberg
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Abreu, P., Soares, C., Valente, J.M.S. (2009). Selection of Heuristics for the Job-Shop Scheduling Problem Based on the Prediction of Gaps in Machines. In: Stützle, T. (eds) Learning and Intelligent Optimization. LION 2009. Lecture Notes in Computer Science, vol 5851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11169-3_10
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DOI: https://doi.org/10.1007/978-3-642-11169-3_10
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