A General Variable Neighborhood Search Algorithm for the No-Idle Permutation Flowshop Scheduling Problem
In this study, a general variable neighborhood search (GVNS) is presented to solve no-idle permutation flowshop scheduling problem (NIPFS), where idle times are not allowed on machines. GVNS is a metaheuristic, where inner loop operates a variable neighborhood descend (VND) algorithm whereas the outer loop carries out some perturbations on the current solution. We employ a simple insert and swap moves in the outer loop whereas iterated greedy (IG) and iterated local search (ILS) algorithms are employed in the VND as neighborhood structures. The results of the GVNS algorithm are compared to those generated by the variable iterated greedy algorithm with differential evolution (vIG_DE). The performance of the proposed algorithm is tested on the Ruben Ruiz’ benchmark suite that is presented in http://soa.iti.es/rruiz. Computational results showed that the GVNS algorithm further improved 85 out of 250 best solutions found so far in the literature.
Keywordsno-idle permutation flowshop scheduling problem general variable neighborhood search heuristic optimization metaheuristics
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