Grid-Based Hybrid Genetic Approach to Relaxed Flexible Flow Shop with Sequence-Dependent Setup Times

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.

A New Hybrid Multi-Objective Scheduling Model for Hierarchical Hub and Flexible Flow Shop Problems

“…This paper developed a hybrid scheduling model for hierarchical hubs and flexible flow shops. Although Juárez-Pérez et al [45], Zhang et al [41], and Wu et al [47] tried to solve the FFS scheduling problem with different approaches, none of them solved the scheduling problem using HHLP and FFS. This is one of the first studies to hybridize a hierarchical hub problem with an FFS problem and create a multi-objective problem considering cost minimization and delivery time reduction.…”

“…There are more examples of research articles confronting the same flexible flow-shop problem. Using a hybrid genetic algorithm, Juárez-Pérez et al (2022) solved the FFS scheduling problem with sequence-dependent setup times in a grid environment [45]. Zhang et al (2021) developed a hybrid flow-shop scheduling problem containing consistent subplots with the aim of optimizing two conflicting objectives simultaneously: the makespan and the total number of subplots [46].…”