Two meta-heuristic algorithms for solving multi-objective flexible job-shop scheduling with parallel machine and maintenance constraints

There are different reasons, such as a preventive maintenance, for the lack of machines in the planning horizon in real industrial environments. This paper focuses on the multi-objective flexible job-shop scheduling problem with parallel machines and maintenance cost. A new mathematical modeling was developed for the problem. Two meta-heuristic algorithms, a hybrid genetic algorithm and a simulated annealing algorithm, were applied after modeling the problem. Then, solutions of these meta-heuristic methods were compared with solutions obtained by using the software LINGO for small-scale, medium-scale, and large-scale problems in terms of time and optimality. The results showed that the applied hybrid genetic and simulated annealing algorithms were much more effective than the solutions obtained using LINGO. Finally, solutions using the simulated annealing approach were compared with solutions of the hybrid genetic algorithm.

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