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

Abstract

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 A ⊆ P 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 α i that will be transmitted to each worker i ∈ A, with i∈A α i = 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.

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

“…Biased‐randomization techniques make use of Monte Carlo simulation to enhance the performance of constructive heuristics (Faulin and Juan, ; Faulin et al., ). These techniques have been successfully employed to deal with different optimization problems, including vehicle routing problems (Fikar et al., ; Belloso et al., ), scheduling problems (Juan et al., ; Brandão et al., , ), facility‐location problems (Quintero‐Araujo et al., , ), open stacks problems (Gonçalves et al., ), and quasi‐clique problems (Pinto et al., ), among many others.…”

A biased random‐key genetic algorithm for scheduling heterogeneous multi‐round systems

“…In our implementation, the population size was set to , with the sizes of sets , and set to , and , respectively, as suggested in Brandão et al. (), Buriol et al. (, ), Chan et al.…”

“…BRKGAs have been successfully used for solving many permutation based combinatorial optimization problems, see, e.g. (Duarte et al., ; Gonçalves and Resende, , , ; Gonçalves et al., ; Noronha et al., ), including the single‐round divisible load scheduling (DLS) problem (Brandão et al., ). Computational results showed that the proposed genetic algorithm outperformed a closed‐form state‐of‐the‐art heuristic (Shokripour et al., ), obtaining makespans that are 11.68% smaller on average for a set of benchmark problems.…”

On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems

“…For instance, they have been used to solve different rich and realistic variants of the well-known vehicle routing problem (VRP), including the two-dimensional VRP [13], VRP variants with horizontal cooperation [14], multi-agent versions of the VRP [15], the location routing problem [16], the fleet mixed VRP with backhauls [17,18], the multi-period VRP [19], and even other versions of the multi-depot VRP [20]. BRAs have also been employed in solving other OPs, such as the single-round divisible load scheduling [21], the stochastic flow-shop scheduling [22], scheduling heterogeneous multi-round systems [23], the minimization of open stacks problem [24], the dynamic home service routing [25], waste collection management [26], or the maximum quasi-clique problem [27].…”