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International Journal of Scientific and Research Publications, Volume 3, Issue 2, February 2013 777 ISSN 2250-3153 IV. RESULTS OBTAINED THROUGH ANN The part types are assigned on various machines through SPT procedure for the FMS loading problem given in Table 2.This sequence obtained [6,1,4,7,3,5,2] with system unbalance of 158 and throughput of 43. Further this sequence is perturbed through method given in section. The fourteen (14) numbers of sequences and their output results are as in Table 2. It is to be mentioned that these sequences outputs (system unbalance and throughput) are evaluated with fulfilling all the constraints of the loading problem. Artificial Neural network exploits these sequences for training the network. On the basis of these trained data ANN developed an input-output relationship of its own. This input output relationship is able to predict the output values for given input sequence. Though number of arbitrary sequences can be put as input of this predictive model, only fewer numbers of sequences is explored once it has been seen the change in sequence offers no better results. Table 2 (Various Part type Sequences obtained for Training the Network) Part type sequence System unbalance Through Put 2,1,4,7,3,5,6 108 39 6,2,4,7,3,5,1 178 37 6,1,2,7,3,5,4 299 40 6,1,4,2,3,5,7 371 38 6,1,4,7,2,5,3 158 43 5,1,4,7,3,6,2 158 43 6,5,4,7,3,1,2 158 43 5,1,4,7,3,6,2 158 43 5,1,4,2,3,7,6 233 40 6,1,4,5,2,7,3 158 43 7,1,4,5,3,6,2 158 43 2,1,4,5,6,7,3 371 38 7,1,4,5,3,2,6 158 43 6,1,4,2,3,7,5 371 38 After training of these sequences through Artificial Neural Network, newer set of sequences are generated randomly to explore for getting optimal/ near optimal solution. These following given sequences provide reasonably good results over other sequences (which are not being reported). 6,1,4,5,3,7,2 62 49 6,2,4,5,3,7,1 83 42 2,1,4,5,3,7,6 18 46 The sequences which produce best results in terms of minimum system balance and maximum throughput are 2,1,4,5,3,7,6 with through put. The value of these objective functions is 18 and 46. The Artificial neural network has been trained with help of Programming in MATLAB. V. CONCLUSION The loading problem of FMS environment has been solved with the help of Artificial Neural Network. Previous authors also solved the FMS loading problem with other heuristic techniques like tabu search, Simulated Annealing and Genetic algorithm. The substantial effort was required to solve the problem with the se computer programming based techniques. On the other hand Artificial Neural Network is naive tool to solve this problem. This method also gives second benefit of obtaining a predictive model which was lacking in previous techniques. The bi-criterion objectives of minimum system unbalance and maximizing the throughput are predicted for any part type sequences. These benefits may especially be exploited in Dynamic FMS environment where some times few part types are required to be manufactured in priority. The results of part type sequences can also be predicted with ease of placing their values in Artificial Neural Network based Model. REFERENCES [1] Ammonus, J. C., Lofgren , C. B. and Mcginnis, L. F.,” A large scale machine loading problem in flexibility assembly”. Annals of Operation Research, 3,1985 pp 319 – 322. [2] Basnet, C and Mize, J. H.,“A rule based object-oriente d framework for operating flexible manufacturing systems” International Journal of Production Researc h, 33(5),1995.pp 1417 – 1431. [3] Chen, I. J. and Chung, C.H., “An examination ,of flexibility measurements and performance of flexible manufacturing systems”, Internationa l Journal of Production Research, 34(2),1996,pp 379 – 394. www.ijsrp.<strong>org</strong>
International Journal of Scientific and Research Publications, Volume 3, Issue 2, February 2013 778 ISSN 2250-3153 [4] Chen, Y. J. and Askin, R.G, “A multi-objective evaluationof flexible manufacturing system loading heuristic” Interna- tional Journal of Production Researc h, 28(5),1990,pp895 – 911. [5] Esfahani Mohsen Botlani, Toroghinejad Mohammad Reza and Abbasi Shahram ““ Artificial Neural Network Moedeling the tensile strength of Hot Strip Mill Products, ISIJ International, 49(10), 2009 pp 1583-1587 [6] Gershenson, C., “ Artificial Neural Network for beginners” Formal computational skills teaching package, COGS. 2011 [7] Guerrero, F., Lozana, S., Kotial,. and Larreneta, J., “Machine loading and part type selection in flexible manufacturing systems” Internationa l Journal of Production Research , 37(6),1999,1303 – 1317. [8] Jain, S., Barber, K. and Osterfeld, D.,“Expert simulation for on-line scheduling.” Proceedings of the 1989 winter simulation conference,1989,pp 930 – 935. 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[13] Liang, M. and Dutta, S.P.,“ Combined part-selection,load-sharing and machine-loading problem in flexible manufacturing system”. International Journal of Production Research , 30(10),1992,pp 2335 – 2350. [14] Liang, M. and Dutta, S. P.,” A integrated approach to partselection and machine loading problem in a class of flexible manufacturing system”. Europea n Journa l of Operationa l Research , 67(3),1993, pp387 – 404. [15] Morino, A. A. and Ding, F. Y.,” Heuristics for FMS-loading and part-type selection problems”. International Journal of Flexible Manufacturing Systems, 5,1993, pp287 – 300. [16] Mukhopadhyay , S. K., Maiti, B. and Garg, S.,”Heuristic solution to the scheduling problems in flexible manufacturing systems”. Internationa l Journal of Production Researc h,29(10),1991,pp2003 – 2024. [17] Mukhopadhyay , S. K., Midha, S. and Krishna,V. A.,”A heuristic procedure for loading problem in flexible manufacturing system”. 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Europea n Journal of Operational Research, 45,1990, pp 177 – 190 [22] Sawik, T., “A lexicographic approach to bi-objective loading of a flexible assembly system”. Europea n Journal of Operational Research, 107,1998,pp 656 – 668. [23] Shanker, K. and Srinivasulu, A., “Some solution meth-odologies for a loading problem in a flexible manufacturing system”. International Journal of Production Researc h, 27(6),1989,pp1019 – 1034. [24] Shanker, K. and Tzen, Y. J., “A loading and dispatching problem in a random FMS”. Internationa l Journal of Production Research, 23(3),1985,pp 579 – 595. [25] Stecke, K. E. and Solberg , J. J.,“Loading and controlpolicies for a flexible manufacturing system”. International Journal of Production Research, 19,1981,pp 481 – 490. [26] Stecke, K. E., “Formulation and solution of nonlinear integer production planning problem for flexible manufacturing system”. Managemen t Science, 29(3),1983,pp 273 – 288. [27] Tiwari , M. K., Hazarika, B., Vidyarathi, N. 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