Abstract
This paper presents a combination of evolutionary algorithm and mathematical programming with an efficient local search procedure for a just-in-time job-shop scheduling problem (JITJSSP). Each job on the JITTSSP is composed by a sequence of operations, each operation having a specific machine where it must be scheduled and a due date when it should be completed. There is a tardiness cost if an operation is finished later than its due date and also an earliness cost if finished before. The objective is to find a feasible scheduling obeying precedence and machine constraints, minimizing the total earliness and tardiness costs. The experimental results with instances from the literature show the efficiency of the proposed hybrid method: it was able to improve the known upper bound for most of the instances tested, in very little computational time.
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dos Santos, A.G., Araujo, R.P., Arroyo, J.E.C. (2010). A Combination of Evolutionary Algorithm, Mathematical Programming, and a New Local Search Procedure for the Just-In-Time Job-Shop Scheduling Problem. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_2
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DOI: https://doi.org/10.1007/978-3-642-13800-3_2
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