Abstract
Parallel machine scheduling, involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the due date related objectives. To solve the unrestricted parallel machine scheduling problem, this paper proposes MCMP-SRI and MCMP-SRSI, which are two multirecombination schemes that combine studs, random and seed immigrants. Evidence of the improved behaviour of the EAs when inserting problem-specific knowledge with respect to SCPC (an EA without multirecombination) is provided. Experiments and results are discussed.
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Ferretti, E., Esquivel, S., Gallard, R. (2004). Studs, Seeds and Immigrants in Evolutionary Algorithms for Unrestricted Parallel Machine Scheduling. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_18
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DOI: https://doi.org/10.1007/978-3-540-30498-2_18
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