A biased random-key genetic algorithm for single-round divisible load scheduling

A divisible load is an amount W of computational work that can be arbitrarily divided into chunks and distributed among a set P of worker processors to be processed in parallel. Divisible load applications occur in many fields of science and engineering. They can be parallelized in a master-worker fashion, but they pose several scheduling challenges. The divisible load scheduling problem consists in (a) selecting a subset AP of active workers, (b) defining the order in which the chunks will be transmitted to each of them, and (c) deciding the amount of load i that will be transmitted to each worker iA, with Sigma iAi=W, so as to minimize the makespan, i.e., the total elapsed time since the master began to send data to the first worker, until the last worker stops its computations. In this work, we propose a biased random-key genetic algorithm for solving the divisible load scheduling problem. Computational results show that the proposed heuristic outperforms the best heuristic in the literature.