Abstract

Manufacturing industries are under intense pressure from the increasingly competitive market. Shorter products life cycles, time-to-market and diverse customer needs have challenged manufacturers to improve the efficiency and productivity of their production activities. Proper scheduling of jobs is indispensable for the successful operation of a shop. Group technology has become an increasingly popular concept in manufacturing, which is designed to take advantage of mass production layout and techniques in smaller batch production system. Since the conventional scheduling methods need more computation time. An attempt has been made to optimize the scheduling for cellular manufacturing system by comparing Meta-heuristic methods named as simulated annealing and Tabu Search. In the first part of this work, different types of products in the job-shop environment are identified and grouping of cells is performed using Rank Order Clustering Method. In the second part, optimization procedure has been developed for the scheduling problem for processing in the machine cells. The objectives are minimization of total penalty cost, Comparison of the solutions are obtained by the proposed algorithm with the benchmark problems has been reported.

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