Learning-Based Cuckoo Search Algorithm to Schedule a Flexible Job Shop With Sequencing Flexibility
Document Type
Article
Publication Date
10-1-2023
Abstract
This work considers an extended version of flexible job-shop problem from a postprinting or semiconductor manufacturing environment, which needs a directed acyclic graph rather than a linear order to describe the precedences among operations. To obtain its reliable and high-quality schedule in a reasonable time, a learning-based cuckoo search (LCS) algorithm is presented. In it, cuckoo search is selected as an optimizer. To produce promising solutions in a high-dimensional solution space, a sparse autoencoder is introduced to compress a high-dimensional solution into an informative low-dimensional one. It extends the application area of autoencoder-embedded evolutionary optimization methods into combinational optimization by developing an improved one-hot encoding method. Then, in order to reveal the linkages among decision variables and enhance the explore ability of the proposed method, a factorization machine (FM) is used, for the first time, to capture the relevant and complementary features of population. Hence, a parallel framework involving three co-evolved subpopulations is constructed. The first one is an autoencoder embedded subpopulation, the second one is assisted by an FM, and the last one undergoes a regular iteration process. To balance the exploration and exploitation of the proposed framework and avoid unnecessary computation, a reinforcement learning algorithm is used to adaptively adjust the proportion of subpopulations and tune parameters of each subpopulation iteratively. Numerical simulations with benchmarks are performed to compare it with CPLEX, some classical heuristics, and several recently developed methods. The results shows that it well outperforms them.
Identifier
85141645678 (Scopus)
Publication Title
IEEE Transactions on Cybernetics
External Full Text Location
https://doi.org/10.1109/TCYB.2022.3210228
e-ISSN
21682275
ISSN
21682267
PubMed ID
36355723
First Page
6663
Last Page
6675
Issue
10
Volume
53
Grant
Z191100006119031
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Lin, Cheng Ran; Cao, Zheng Cai; and Zhou, Meng Chu, "Learning-Based Cuckoo Search Algorithm to Schedule a Flexible Job Shop With Sequencing Flexibility" (2023). Faculty Publications. 1410.
https://digitalcommons.njit.edu/fac_pubs/1410
