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A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem
The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem
that continues to be studied extensively due to its practical implications in
manufacturing systems and emerging new variants, in order to model and optimize
more complex situations that reflect the current needs of the industry better.
This work presents a new meta-heuristic algorithm called GLNSA (Global-local
neighborhood search algorithm), in which the neighborhood concepts of a
cellular automaton are used, so that a set of leading solutions called
"smart_cells" generates and shares information that helps to optimize instances
of FJSP. The GLNSA algorithm is complemented with a tabu search that implements
a simplified version of the Nopt1 neighborhood defined in [1] to complement the
optimization task. The experiments carried out show a satisfactory performance
of the proposed algorithm, compared with other results published in recent
algorithms and widely cited in the specialized bibliography, using 86 test
problems, improving the optimal result reported in previous works in two of
them.Comment: 33 pages, 25 figures, Submitted to: PeerJ Comput. Sc