Production and Remanufacturing of Returned Products in Supply Chain using Modified Genetic Algorithm
In recent years, environment regulation forcing
manufactures to consider recovery activity of end-of- life products
and/or return products for refurbishing, recycling,
remanufacturing/repair and disposal in supply chain management. In
this paper, a mathematical model is formulated for single product
production-inventory system considering remanufacturing/reuse of
return products and rate of return products follows a demand like
function, dependent on purchasing price and acceptance quality level.
It is useful in decision making to determine whether to go for
remanufacturing or disposal of returned products along with newly
produced products to satisfy a stationary demand. In addition, a
modified genetic algorithm approach is proposed, inspired by particle
swarm optimization method. Numerical analysis of the case study is
carried out to validate the model.
[1] A. M. A. ElSaadany, M. Y. Jaber, "A production/remanufacturing
inventory model with price and quality dependant return rate,"
Computers & Industrial Engineering, vol. 58, pp. 352-362, 2010.
[2] U. Merschmann, U. W. Thonemann, "Supply chain flexibility,
uncertainty and firm performance: An empirical analysis of German
manufacturing firms." Int. J. Production Economics, vol.130, pp. 43-
53, 2011.
[3] Yanzhi. Li, Mark. Daskin, Saif. Benjaafar, "Carbon Footprint and the
Management of Supply Chains: Insights from Simple Models." January
25, 2010.
[4] Balan. Sundarakani, Robert. deSouza, Mark. Goh, Stephan M. Wagner,
Sushmera. Manikandan, "Modeling carbon footprints across the supply
chain." Int. J. Production Economics, vol. 128, pp.43-50, 2010.
[5] E.U. Olugu, K.Y. Wong, A. M. Shaharoun, "Development of key
performance measures for the automobile green supply chain."
Resources, Conservation and Recycling, 2010.
[6] J.Kennedy, and R.C.Eberhart, "Particle swarm optimization", In
Proceedings of the IEEE international conference on neural networks,
vol. 4, pp. 1942-1948, NJ: IEEE Service Center, Piscataway, 1995.
[7] J. Kennedy, and W.Spears, "Matching algorithms to problems: An
experimental test of the particle swarm and some genetic algorithms on
the multimodal problem generator." In Proceedings of the IEEE
international conference on evolutionary computation, pp.78-83,
Anchorage, Alaska, 1998.
[8] Adam. Heying, Whitney. Sanzero. "A Case Study of Wal-Mart-s Green
Supply Chain Management," Operations Management, MGT 520, May
4, 2009.
[1] A. M. A. ElSaadany, M. Y. Jaber, "A production/remanufacturing
inventory model with price and quality dependant return rate,"
Computers & Industrial Engineering, vol. 58, pp. 352-362, 2010.
[2] U. Merschmann, U. W. Thonemann, "Supply chain flexibility,
uncertainty and firm performance: An empirical analysis of German
manufacturing firms." Int. J. Production Economics, vol.130, pp. 43-
53, 2011.
[3] Yanzhi. Li, Mark. Daskin, Saif. Benjaafar, "Carbon Footprint and the
Management of Supply Chains: Insights from Simple Models." January
25, 2010.
[4] Balan. Sundarakani, Robert. deSouza, Mark. Goh, Stephan M. Wagner,
Sushmera. Manikandan, "Modeling carbon footprints across the supply
chain." Int. J. Production Economics, vol. 128, pp.43-50, 2010.
[5] E.U. Olugu, K.Y. Wong, A. M. Shaharoun, "Development of key
performance measures for the automobile green supply chain."
Resources, Conservation and Recycling, 2010.
[6] J.Kennedy, and R.C.Eberhart, "Particle swarm optimization", In
Proceedings of the IEEE international conference on neural networks,
vol. 4, pp. 1942-1948, NJ: IEEE Service Center, Piscataway, 1995.
[7] J. Kennedy, and W.Spears, "Matching algorithms to problems: An
experimental test of the particle swarm and some genetic algorithms on
the multimodal problem generator." In Proceedings of the IEEE
international conference on evolutionary computation, pp.78-83,
Anchorage, Alaska, 1998.
[8] Adam. Heying, Whitney. Sanzero. "A Case Study of Wal-Mart-s Green
Supply Chain Management," Operations Management, MGT 520, May
4, 2009.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52070", author = "Siva Prasad Darla and C. D. Naiju and K. Annamalai and Y. Upendra Sravan", title = "Production and Remanufacturing of Returned Products in Supply Chain using Modified Genetic Algorithm", abstract = "In recent years, environment regulation forcing
manufactures to consider recovery activity of end-of- life products
and/or return products for refurbishing, recycling,
remanufacturing/repair and disposal in supply chain management. In
this paper, a mathematical model is formulated for single product
production-inventory system considering remanufacturing/reuse of
return products and rate of return products follows a demand like
function, dependent on purchasing price and acceptance quality level.
It is useful in decision making to determine whether to go for
remanufacturing or disposal of returned products along with newly
produced products to satisfy a stationary demand. In addition, a
modified genetic algorithm approach is proposed, inspired by particle
swarm optimization method. Numerical analysis of the case study is
carried out to validate the model.", keywords = "Genetic Algorithm, Particle Swarm Optimization,
Production, Remanufacturing.", volume = "6", number = "3", pages = "582-4", }