Integration of Process Planning and Scheduling of a Manufacturing Systems using Petri nets and Genetic Algorithm

Sreeramulu, D.; Sagar, Y.; Suman, P. Vijay; Kumar, Amit

doi:10.17485/ijst/2016/v9i41/100874

Cited by 3 publications

(1 citation statement)

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“…ECs have been successfully adopted to solve the IPPS problem, as proven by the growing number of papers on the topic. Examples include GAs [48,35,32,34,58,55,40,39,71,72,60,10,67], Symbolic Evolutionary Algorithms (SEAs) [31,30], and hybrid approaches of EC [37,1,49]. However, the current encoding strategies for the IPPS problem reported in the literature make almost all the classic genetic operators inapplicable due to the fact that a vast of offspring will be infeasible or illegal.…”

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confidence: 99%

An enhanced Genetic Algorithm with an innovative encoding strategy for flexible job-shop scheduling with operation and processing flexibility

Huang¹,

Zhang²,

Islam³

et al. 2020

Journal of Industrial &Amp; Management Optimization

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This paper considers the Flexible Job-shop Scheduling Problem with Operation and Processing flexibility (FJSP-OP) with the objective of minimizing the makespan. A Genetic Algorithm based approach is presented to solve the FJSP-OP. For the performance improvement, a new and concise Four-Tuple Scheme (FTS) is proposed for modeling a job with operation and processing flexibility. Then, with the FTS, an enhanced Genetic Algorithm employing a more efficient encoding strategy is developed. The use of this encoding strategy ensures that the classic genetic operators can be adopted to the utmost extent without generating infeasible offspring. Experiments have validated the proposed approach, and the results have shown the effectiveness and high performance of the proposed approach.

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