In this paper a sequential stopping rule is developed for the multistart algorithm. A statistical model for the values of the observed local maxima of an objective function is introduced in the framework of Bayesian n...
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In this paper a sequential stopping rule is developed for the multistart algorithm. A statistical model for the values of the observed local maxima of an objective function is introduced in the framework of Bayesian non-parametric statistics. A suitablea-priori distribution is proposed which is general enough and which leads to computationally manageable expressions for thea-posteriori distribution. Sequential stopping rules of thek-step look-ahead kind are then explicitly derived, and their numerical effectiveness compared.
In real industrial environments, the assumption that preventive maintenance completely eliminates unexpected failures may be too idealistic. Therefore, this work incorporates unexpected failures into the single machin...
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In real industrial environments, the assumption that preventive maintenance completely eliminates unexpected failures may be too idealistic. Therefore, this work incorporates unexpected failures into the single machine scheduling problem with sequence-dependent setup times under a flexible maintenance scheme, where corrective maintenance is taken when unexpected failures occur to minimize the makespan. To this end, two lemmas and valid inequalities are obtained and totally four models are proposed. Specifically, the unexpected failures of the first two models are represented by individual jobs, and by integrating jobs within each batch, respectively, and the first two models are extended to obtain the latter two models by adding valid inequalities. In addition, we design a metaheuristic algorithm based on multistart strategy for larger instances. Finally, we perform extensive numerical experiments to efficiency validate the model, as well as to verify the performance of the valid inequalities. Managerial insights are also derived that considering unexpected failures under flexible maintenance can achieve a trade-off between preventive maintenance and corrective maintenance, thereby reducing both the makespan and unexpected failures of the machine. In addition, this work provides a new perspective on controlling the degree of fluctuation in job completion time when unexpected failures occur in real production.
In this paper a new algorithm is proposed for global optimization problems. The main idea is that of modifying a standard clustering approach by sequentially sampling the objective function while adaptively deciding a...
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In this paper a new algorithm is proposed for global optimization problems. The main idea is that of modifying a standard clustering approach by sequentially sampling the objective function while adaptively deciding an appropriate sample size. Theoretical as well as computational results are presented.
This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fi...
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This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared. The hybridization technique produces two desirable effects: a significant improvement in the final performance and the acquisition of subset-size control. The hybrid GAs showed better convergence properties compared to the classical GAs. A method of performing rigorous timing analysis was developed, in order to compare the timing requirement of the conventional and the proposed algorithms. Experiments performed with various standard data sets revealed that the proposed hybrid GA is superior to both a simple GA and sequential search algorithms.
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