2017 # Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling

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(33 citation statements)

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“…As both the UGN design and the AGVs scheduling problem in the FMS environment are nondeterministic polynomial time-(NP-) hard problems [31,32], the integration of these two problems will become a more complex NP-hard problem. As a high-performance multipopulation heuristic intelligent search algorithm, the collaborative evolutionary genetic algorithm (CEGA) [33] divides the system decision variables into several variable subgroups.…”

confidence: 99%

“…As both the UGN design and the AGVs scheduling problem in the FMS environment are nondeterministic polynomial time-(NP-) hard problems [31,32], the integration of these two problems will become a more complex NP-hard problem. As a high-performance multipopulation heuristic intelligent search algorithm, the collaborative evolutionary genetic algorithm (CEGA) [33] divides the system decision variables into several variable subgroups.…”

confidence: 99%

“…If operational flexibility is considered in these problems, the complexity of finding the optimal approximate solutions is increased [1]. Since FDJSP with operational flexibility is strongly NP-hard [8], this problem, with respect to parallel machines in a dynamic manufacturing environment, will also be strongly NP-hard.…”

confidence: 99%

“…Some operations can be processed not only on one station but also on a set of stations available in the workshop (flexibility due to operation). These conditions add another complexity to this problem, so finding an approximately optimal solution for these problems is very complicated and difficult [8][9][10][11][12].…”

confidence: 99%

“…In each frame of motion, the cost is minimized using a commercial software package (MATLAB release 2014a, The MathWorks, Inc., Natick, MA, USA) and the output which is a vector of joint angles (q) is saved for future uses. In the current work, a hybrid genetic algorithm (HGA) which combines both deterministic and stochastic routines [8] was used to solve the optimization problem. The Crossover fraction value was 0.8 and the probability of mutation rate was not considered to be a fixed value.…”

confidence: 99%