Manufacturing lead time estimation is an important task in production system with machine breakdown and maintenance. This study presents a flexible algorithm for estimation and forecasting lead time based on artificial neural network (ANN), fuzzy regression (FR), and conventional regression (CR). First, an ANN is illustrated based on supervised multi-layer perceptron network for the lead time forecasting. The selected ANN model is then compared with fuzzy and conventional regression models with respect to Mean Absolute Percentage Error, hence the name neuro-fuzzy regression algorithm. To show the applicability and superiority of the flexible neuro-fuzzy regression, the proposed algorithm is used to estimate the weekly lead times of an actual assembly shop (producer of heavy electric motor). This is the first study that introduces a flexible neuro-fuzzy algorithm for improved estimation of lead time in manufacturing systems with machine breakdown and maintenance. In addition to accuracy, simplicity and short execution time of lead time estimation are desirable features of the presented flexible algorithm.
This paper presents a new mathematical model for a hybrid flow shop scheduling problem with a processor assignment that minimizes makespan (i.e., C max ) and cost of assigning a number of processors to stages. In this problem, it is assumed that there are a number of parallel identical processors which are assigned to all of the stages with an unlimited intermediate storage between any two successive stages. To solve such a hard problem, first a new heuristic algorithm is proposed to compute the makespan that is embedded in the proposed genetic algorithm in order to find the best sequence of jobs, and then processors are assigned to the stages simultaneously. A number of test problems have been solved and related results are illustrated and analyzed.
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