Minimizing the total completion time on a parallel machine system with tool changes

We study a parallel-machine tool-change scheduling problem.We develop a mixed-integer linear programming model.We develop a hybrid genetic algorithm.We compare the proposed metaheuristic with other methods.A statistical analysis emphasizes the effectiveness of the proposed approach. In this paper, the identical parallel machine scheduling problem with periodic tool changes due to wear is addressed under the total completion time minimization objective. Due to machine availability restrictions induced by tool replacement operations, the problem is NP-hard in the strong sense. A mixed integer linear programming (MILP) model has been developed with the aim to provide the global optimum for small-sized test cases. Furthermore, a hybrid metaheuristic procedure based on genetic algorithms has been specifically designed to cope with larger instances. A comprehensive experimental analysis supported by a non-parametric statistical test has been fulfilled to select the best metaheuristic configuration in terms of decoding strategy and parameters driving the search mechanism as well. Then, the proposed optimization procedure has been compared with three alternative methods arising from the relevant literature on the basis of a wide benchmark of test cases. The obtained results, also supported by a proper statistical analysis, demonstrate the effectiveness of the proposed approach for solving the tool change scheduling problem at hand.

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