Abstract
In this paper, a hybrid genetic algorithm implemented in a grid environment to solve hard instances of the flexible flow shop scheduling problem with sequence-dependent setup times is introduced. The genetic algorithm takes advantage of the distributed computing power on the grid to apply a hybrid local search to each individual in the population and reach a near optimal solution in a reduced number of generations. Ant colony systems and simulated annealing are used to apply a combination of iterative and cooperative local searches, respectively. This algorithm is implemented using a master–slave scheme, where the master process distributes the population on the slave process and coordinates the communication on the computational grid elements. The experimental results point out that the proposed scheme obtains the upper bound in a broad set of test instances. Also, an efficiency analysis of the proposed algorithm indicates its competitive use of the computational resources of the grid.
Highlights
A scheduling problem (SP) involves the efficient assignment of disposable resources to complete one or several related tasks over time [1].SP is a relevant topic in science and engineering with a wide range of applications in diverse areas such as telecommunications [2], industry [3,4], health [5,6], agriculture [7], and education [8]
Are the experimental results of the hybrid genetic algorithm in grid environment (HGAG)
Comparing this value with the 2,457,600 bits that the Hybrid Genetic Algorithm in Grid Environment (HGAG) uses per packet, it is clear that there is no traffic congestion between clusters since the packet size sent by the HGAG only represents 29.3% of the bandwidth available to send packages between the grid clusters
Summary
A scheduling problem (SP) involves the efficient assignment of disposable resources (processors or machines) to complete one or several related tasks (jobs) over time [1]. A relaxed FFSP with sequence-dependent setup time constraints is described as one 0–1 integer programming problem and solved using an ensemble of three metaheuristics, including genetic algorithms (GAs), ant colony systems (ACO), and simulated annealing (SA). This ensemble runs in a grid-based environment using two clusters, where the candidate solutions are distributed in the grid cores.
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