Genetic Algorithm Nested with Simulated Annealing for Big Job Shop Scheduling Problems

Yin, Hong

doi:10.1109/cis.2013.18

Cited by 1 publication

(1 citation statement)

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“…On the other hand, the simulated annealing algorithm has strong local search capabilities to make up the shortcomings of the genetic algorithm. GA + SA has been used to solve job shop [25], open shop [26], and flexible flow shop problems [27]. However, to the best of our knowledge, there are no reports on its application in flow shop scheduling, especially with improved crossover and mutation operators and adaptive simulated annealing.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Wei

Jiang

et al. 2018

Applied Sciences

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Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

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Section: Introductionmentioning

confidence: 99%