Abstract
We tackle the job-shop scheduling problem with operators and makespan minimization. This problem has been recently proposed and it is motivated by manufacturing processes where each operation has to be assisted by one of a limited number of human operators. We propose a new schedule generation scheme for this problem, termed \( OG \& T \), and prove that it can generate optimal schedules for any instance. This scheme can be used in different settings such as heuristic search, to define a branching strategy, or evolutionary algorithms, to define a decoder. In order to evaluate \( OG \& T \), we herein consider the first option and exploit it to devise an any-time exact algorithm. This algorithm is enhanced with two heuristic estimations, designed from two problem relaxations, and with a pruning method which relies on dominance properties among states of the search. The algorithm is evaluated across several benchmarks and compared with other approaches. The results of the experimental study show that our approach outperforms the state-of-the-art methods.
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Acknowledgments
We are grateful to the anonymous referees for their reviews that improved the quality of the paper. We would also like to thank Andrea Pacifici and Marta Flamini for kindly making their benchmark instances available. This research has been supported by the Spanish Government under research project MICINN-FEDER TIN2010-20976-C02-02 and by the Principality of Asturias under Grant Severo Ochoa FICYT-BP09105.
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Sierra, M.R., Mencía, C. & Varela, R. New schedule generation schemes for the job-shop problem with operators. J Intell Manuf 26, 511–525 (2015). https://doi.org/10.1007/s10845-013-0810-6
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DOI: https://doi.org/10.1007/s10845-013-0810-6