How to cite this paper
Babu, K., Babu, V & Medikondu, N. (2018). Implementation of heuristic algorithms to synchronized planning of machines and AGVs in FMS.Management Science Letters , 8(6), 543-554.
Refrences
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Biskup, D., & Herrmann, J. (2008). Single-machine scheduling against due dates with past-sequence dependent setup times. European Journal of Operational Research, 191(2), 587–592.
Campbell, H.G, Dudek, R.A., & Smith M.L. (1970). A heuristic algorithm for the n job m machine se-quencing problem. Management Science, 16, B630–B637.
Chawla, V., Chanda, A., & Angra, S. (2018). Automatic guided vehicles fleet size optimization for flexible manufacturing system by grey wolf optimization algorithm. Management Science Let-ters, 8(2), 79-90.
Cheng, T. C. E., & Lin, B. M. T. (2009). Johnson’s rule, composite jobs and the relocation problem. European Journal of Operational Research, 192, 1008–10013.
Dannenbring, D. G. (1977). An evaluation of flow shop sequencing heuristics. Management Science, 23(11), 1174-1182.
Eren, T., & Güner, E. (2008). A bicriterion flowshop scheduling with a learning effect. Applied Mathe-matical Modeling, 32, 1719–1733.
Gupta, J. N. D. (1971). A functional heuristic algorithm for the f1owshop scheduling problem. Opera-tional Research, 22, 39-47.
He, Y., & Hui, C. W. (2008). A rule-based genetic algorithm for the scheduling of single-stage multi-product batch plants with parallel units. Computers and Chemical Engineering, 32, 3067–3083.
Johnson, S.M. (1954). Optimal two-and-three-stage production schedules with set-up times included. Naval Research Logistic, 1, 61–68.
Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2005). An evaluation of se-quencing heuristics for flexible flowshop scheduling problems with unrelated parallel machines and dual criteria. Otto-von-Guericke-Universitat Magdeburg, 28(05), 1–23.
Li, X., Wang, Q., & Wu, C. (2009). Efficient composite heuristics for total flow time minimization in permutation flow shops. OMEGA, the International Journal of Management Science, 37(1), 155–164.
Modrak, V., Semanco, P., & Kulpa, W. (2013). Performance Measurement of Selected Heuristic Algo-rithms for Solving Scheduling Problems. In: 11th International Symposium on Applied Machine In-telligence and Informatics, 205–209.
Mosheiov, G., & Sarig, A. (2009). Due-Date Assignment on Uniform Machines. European Journal of Operational Research, 193(1), 49-58.
Nagar, A., Heragu, S. S., & Haddock, J. (1996). A combined branch and bound and genetic algorithm based approach for a flow shop-scheduling problem. Annals Operation Research, 63, 397–414.
Nageswararao, M., Narayanarao, K., & Rangajanardhana, G. (2017). Integrated scheduling of ma-chines and agvs in fms by using dispatching rules. Journal of Production Engineering, 20(1), 75-84.
Nawaz, M., Enscore Jr. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. OMEGA, 11(1), 91–95.
Neppalli, V. R., Chen, C. L., & Gupta, J. N. D. (1996). Genetic algorithms for the two stage criteria flowshop problem. European Journal of Operational Research, 95(2), 356–373.
Nowicki, E., & Smutnicki, C. (1996). A fast tabu search algorithm for the permutation flowshop prob-lem. European Journal of Operational Research, 91(1), 160–175.
Palmer, D.S. (1965). Sequencing jobs through a multistage process in the minimum total time: A quick method of obtaining a near-optimum. Operational Research, 16, 101–107.
Ronconi, D. P. (2004). A note on constructive heuristics for the flowshop problem with blocking. Inter-national Journal of Production Economics, 87(1), 39–48.
Stecke, K.E., & Solberg, J.J. (1981). Loading and control policies for a flexible manufacturing sys-tem. International Journal of Production Research, 19(5), 481 – 490.
Tseng, C. T., & Liao, C. J. (2008). A discrete particle swarm optimization for lot-streaming flowshop scheduling problem. European Journal of Operational Research, 191(2), 360-373.
Wu, C.C., & Lee, W.C. (2009). A note on the total completion time problem in a permutation flow shop with a learning effect. European Journal of Operational Research, 192, 343–347.
Wu, X., & Zhou, X. (2008). Stochastic scheduling to minimize expected maximum lateness. European Journal of Operational Research, 190(1), 103–115.
Biskup, D., & Herrmann, J. (2008). Single-machine scheduling against due dates with past-sequence dependent setup times. European Journal of Operational Research, 191(2), 587–592.
Campbell, H.G, Dudek, R.A., & Smith M.L. (1970). A heuristic algorithm for the n job m machine se-quencing problem. Management Science, 16, B630–B637.
Chawla, V., Chanda, A., & Angra, S. (2018). Automatic guided vehicles fleet size optimization for flexible manufacturing system by grey wolf optimization algorithm. Management Science Let-ters, 8(2), 79-90.
Cheng, T. C. E., & Lin, B. M. T. (2009). Johnson’s rule, composite jobs and the relocation problem. European Journal of Operational Research, 192, 1008–10013.
Dannenbring, D. G. (1977). An evaluation of flow shop sequencing heuristics. Management Science, 23(11), 1174-1182.
Eren, T., & Güner, E. (2008). A bicriterion flowshop scheduling with a learning effect. Applied Mathe-matical Modeling, 32, 1719–1733.
Gupta, J. N. D. (1971). A functional heuristic algorithm for the f1owshop scheduling problem. Opera-tional Research, 22, 39-47.
He, Y., & Hui, C. W. (2008). A rule-based genetic algorithm for the scheduling of single-stage multi-product batch plants with parallel units. Computers and Chemical Engineering, 32, 3067–3083.
Johnson, S.M. (1954). Optimal two-and-three-stage production schedules with set-up times included. Naval Research Logistic, 1, 61–68.
Jungwattanakit, J., Reodecha, M., Chaovalitwongse, P., & Werner, F. (2005). An evaluation of se-quencing heuristics for flexible flowshop scheduling problems with unrelated parallel machines and dual criteria. Otto-von-Guericke-Universitat Magdeburg, 28(05), 1–23.
Li, X., Wang, Q., & Wu, C. (2009). Efficient composite heuristics for total flow time minimization in permutation flow shops. OMEGA, the International Journal of Management Science, 37(1), 155–164.
Modrak, V., Semanco, P., & Kulpa, W. (2013). Performance Measurement of Selected Heuristic Algo-rithms for Solving Scheduling Problems. In: 11th International Symposium on Applied Machine In-telligence and Informatics, 205–209.
Mosheiov, G., & Sarig, A. (2009). Due-Date Assignment on Uniform Machines. European Journal of Operational Research, 193(1), 49-58.
Nagar, A., Heragu, S. S., & Haddock, J. (1996). A combined branch and bound and genetic algorithm based approach for a flow shop-scheduling problem. Annals Operation Research, 63, 397–414.
Nageswararao, M., Narayanarao, K., & Rangajanardhana, G. (2017). Integrated scheduling of ma-chines and agvs in fms by using dispatching rules. Journal of Production Engineering, 20(1), 75-84.
Nawaz, M., Enscore Jr. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. OMEGA, 11(1), 91–95.
Neppalli, V. R., Chen, C. L., & Gupta, J. N. D. (1996). Genetic algorithms for the two stage criteria flowshop problem. European Journal of Operational Research, 95(2), 356–373.
Nowicki, E., & Smutnicki, C. (1996). A fast tabu search algorithm for the permutation flowshop prob-lem. European Journal of Operational Research, 91(1), 160–175.
Palmer, D.S. (1965). Sequencing jobs through a multistage process in the minimum total time: A quick method of obtaining a near-optimum. Operational Research, 16, 101–107.
Ronconi, D. P. (2004). A note on constructive heuristics for the flowshop problem with blocking. Inter-national Journal of Production Economics, 87(1), 39–48.
Stecke, K.E., & Solberg, J.J. (1981). Loading and control policies for a flexible manufacturing sys-tem. International Journal of Production Research, 19(5), 481 – 490.
Tseng, C. T., & Liao, C. J. (2008). A discrete particle swarm optimization for lot-streaming flowshop scheduling problem. European Journal of Operational Research, 191(2), 360-373.
Wu, C.C., & Lee, W.C. (2009). A note on the total completion time problem in a permutation flow shop with a learning effect. European Journal of Operational Research, 192, 343–347.
Wu, X., & Zhou, X. (2008). Stochastic scheduling to minimize expected maximum lateness. European Journal of Operational Research, 190(1), 103–115.