A Genetic Algorithm Based Permutation and Non-Permutation Scheduling Heuristics for Finite Capacity Material Requirement Planning Problem
This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average.
[1] W. H. M. Zijm, and R. Buitenhek, “Capacity planning and lead time management,” International Journal of Production Economics. vol. 46, pp. 165-179, 1996.
[2] M. Taal, and J. C. Wortmann, “Integrating MRP and finite capacity planning,” Production Planning & Control, vol. 8, no. 3, pp. 245-251, 1997.
[3] P. C. Pandey, P. Yenradee, S. Archariyapruek, “A finite capacity material requirement planning system,” Production Planning & Control, vol. 11, no. 2, pp.113–121, 2000.
[4] P. B., Nagendra, and S. K. Das, “Finite capacity scheduling method for MRP with lot size restrictions,” International Journal of Production Research, vol. 39 no. 8, pp. 1603-1623, 2001.
[5] T. Wuttipornpun, and P. Yenradee, “Development of finite capacity material requirement planning system for assembly operations,” Production Planning & Control, vol. 15, no. 4, pp. 534-549, 2004.
[6] J. Mula, R. Poler, J. P. Garcia, “MRP with flexible constraints: A fuzzy mathematical programming approach.” Fuzzy Sets and Systems, vol. 157, no. 1, pp. 74-97, 2006.
[7] M. Vanhoucke, and D. Debels, A finite-capacity production scheduling procedure for a Belgian steel company. International Journal of Production Research, vol. 47 no. 3, pp. 561–584, 2009.
[8] C. Öztürk, A. M. Örnek “Capacitated lot sizing with linked lots for general product structures in job shops,” Computers & Industrial Engineering vol. 58, no. 1, pp. 151–164, 2010.
[9] K. Thörnblad, A-B. Strömberg, M. Patriksson, and T. Almgren, “Scheduling optimisation of a real flexible job shop including fixture availability and preventive maintenance,” European Journal of Industrial Engineering, vol. 9, no. 1, pp. 126–145, 2015.
[10] C. Moon, Y. Seo, Y. Yum, and M. Gen, “Adaptive genetic algorithm for advanced planning in manufacturing supply chain,” Journal of Intelligent Manufacturing, vol. 17, no. 4, pp. 509-522, 2006.
[11] H. Kim, H. I. Jeong, and J. Park, “Integrated model for production planning and scheduling in a supply chain using benchmarked genetic algorithm,” International Journal of Advanced Manufacturing Technology, vol. 39, no. 11, pp. 1207–1226, 2008.
[12] M. H. F. Rahman, R. Sarkerm and D. Essam, “A real-time order acceptance and scheduling approach for permutation flow shop problems,” European Journal of Operational Research, vol. 247, no. 2, pp. 488-503, 2015.
[13] M. Zandieh, and N. Karimi, “An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times,” Journal of Intelligent Manufacturing, vol. 22, no. 6, pp. 979-989, 2011.
[14] P-C. Chang, W-H. Huang, J-L. Wu, and T. C. E. Cheng, “A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem,” International Journal of Production Economics, vol. 141, no. 1, pp. 45-55, 2013.
[15] T. Wuttipornpun, and P. Yenradee, “Finite capacity material requirement planning system for assembly flow shop with alternative work centres,” International Journal of Industrial and Systems Engineering, vol. 18, no. 1, pp. 95–124, 2014.
[16] R. Vanchipur, and R. Sridharan, “Development and analysis of hybrid genetic algorithms for flow shop scheduling with sequence dependent setup time” International Journal of Services and Operations Management, vol. 17, no. 2, 2014.
[1] W. H. M. Zijm, and R. Buitenhek, “Capacity planning and lead time management,” International Journal of Production Economics. vol. 46, pp. 165-179, 1996.
[2] M. Taal, and J. C. Wortmann, “Integrating MRP and finite capacity planning,” Production Planning & Control, vol. 8, no. 3, pp. 245-251, 1997.
[3] P. C. Pandey, P. Yenradee, S. Archariyapruek, “A finite capacity material requirement planning system,” Production Planning & Control, vol. 11, no. 2, pp.113–121, 2000.
[4] P. B., Nagendra, and S. K. Das, “Finite capacity scheduling method for MRP with lot size restrictions,” International Journal of Production Research, vol. 39 no. 8, pp. 1603-1623, 2001.
[5] T. Wuttipornpun, and P. Yenradee, “Development of finite capacity material requirement planning system for assembly operations,” Production Planning & Control, vol. 15, no. 4, pp. 534-549, 2004.
[6] J. Mula, R. Poler, J. P. Garcia, “MRP with flexible constraints: A fuzzy mathematical programming approach.” Fuzzy Sets and Systems, vol. 157, no. 1, pp. 74-97, 2006.
[7] M. Vanhoucke, and D. Debels, A finite-capacity production scheduling procedure for a Belgian steel company. International Journal of Production Research, vol. 47 no. 3, pp. 561–584, 2009.
[8] C. Öztürk, A. M. Örnek “Capacitated lot sizing with linked lots for general product structures in job shops,” Computers & Industrial Engineering vol. 58, no. 1, pp. 151–164, 2010.
[9] K. Thörnblad, A-B. Strömberg, M. Patriksson, and T. Almgren, “Scheduling optimisation of a real flexible job shop including fixture availability and preventive maintenance,” European Journal of Industrial Engineering, vol. 9, no. 1, pp. 126–145, 2015.
[10] C. Moon, Y. Seo, Y. Yum, and M. Gen, “Adaptive genetic algorithm for advanced planning in manufacturing supply chain,” Journal of Intelligent Manufacturing, vol. 17, no. 4, pp. 509-522, 2006.
[11] H. Kim, H. I. Jeong, and J. Park, “Integrated model for production planning and scheduling in a supply chain using benchmarked genetic algorithm,” International Journal of Advanced Manufacturing Technology, vol. 39, no. 11, pp. 1207–1226, 2008.
[12] M. H. F. Rahman, R. Sarkerm and D. Essam, “A real-time order acceptance and scheduling approach for permutation flow shop problems,” European Journal of Operational Research, vol. 247, no. 2, pp. 488-503, 2015.
[13] M. Zandieh, and N. Karimi, “An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times,” Journal of Intelligent Manufacturing, vol. 22, no. 6, pp. 979-989, 2011.
[14] P-C. Chang, W-H. Huang, J-L. Wu, and T. C. E. Cheng, “A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem,” International Journal of Production Economics, vol. 141, no. 1, pp. 45-55, 2013.
[15] T. Wuttipornpun, and P. Yenradee, “Finite capacity material requirement planning system for assembly flow shop with alternative work centres,” International Journal of Industrial and Systems Engineering, vol. 18, no. 1, pp. 95–124, 2014.
[16] R. Vanchipur, and R. Sridharan, “Development and analysis of hybrid genetic algorithms for flow shop scheduling with sequence dependent setup time” International Journal of Services and Operations Management, vol. 17, no. 2, 2014.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:75182", author = "Watchara Songserm and Teeradej Wuttipornpun", title = "A Genetic Algorithm Based Permutation and Non-Permutation Scheduling Heuristics for Finite Capacity Material Requirement Planning Problem", abstract = "This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average.
", keywords = "Finite capacity MRP, genetic algorithm, linear programming, flow shop, unrelated parallel machines, application in industries.", volume = "11", number = "3", pages = "655-6", }
