Investigating Feed Mix Problem Approaches: An Overview and Potential Solution
Feed is one of the factors which play an important role in determining a successful development of an aquaculture industry. It is always critical to produce the best aquaculture diet at a minimum cost in order to trim down the operational cost and gain more profit. However, the feed mix problem becomes increasingly difficult since many issues need to be considered simultaneously. Thus, the purpose of this paper is to review the current techniques used by nutritionist and researchers to tackle the issues. Additionally, this paper introduce an enhance algorithm which is deemed suitable to deal with all the issues arise. The proposed technique refers to Hybrid Genetic Algorithm which is expected to obtain the minimum cost diet for farmed animal, while satisfying nutritional requirements. Hybrid GA technique with artificial bee algorithm is expected to reduce the penalty function and provide a better solution for the feed mix problem.
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world by linear and goal programming" The Journal of the Operational
Research Society, vol. 34, pp. 9-16., 1983.
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on Evolutionary Computation (CEC2001), Seoul, Korea. (2001).
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formulation software requirements," Journal of Applied Sciences
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Springer US. 2006.
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Academic Press. 2009. pp. 304.
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excretion in pigs by modifying the traditional least-cost formulation
algorithm," Livestock Production Science, vol. 72, pp. 199-211. 2001.
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linear programming of penaeid rations." Aquaculture, vol. 19, pp. 313-
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228, 2004.
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feed formulation: Economic, nutritional and environmental criteria,"
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application to stochastic dynamic programming'" Journal of Global
Optimization, vol. 35, pp. 423-441, 2006.
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feeds'" Operational Research Quarterly, vol 25, no. 1, pp. 19-26, 1974.
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genetic algorithms: A review," Engineering Letters, vol. 13, no. 2, 2006.
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Unpublished Bachelor degree thesis, University of Pretoria, Pretoria,
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based on principal operation and inequity degree," Computers and
Mathematics with Applications, vol. 56, pp. 1545-1555, 2008.
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and Emergent Computation, pp. 1-11, 1997.
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in Beef cattle feeding and nutrition, 2nd ed., T. W. Perry and M. J.
Cecava, Academic Press, Inc, 1995. pp. 68.
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mix design," Computers and Electronics in Agriculture, vol. 18, pp.
129-135, 1997.
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Production, 8 ed., Clifton Park, New York: Cengage Learning, 2009,
pp. 104-187.
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Optimization," Management Science, vol. 26, no. 5, pp. 524-535, 1980.
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production enterprise," The Journal of the Operational Research
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machine learning, Canada: Addison-Wesley Publishing Company, Inc.,
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performance response to energy density in broiler feed formulation,"
Poultry Science, vol. 83, pp. 147-151, 2004.
[26] D. Haibin, X. Zhihui, and X. Chunfang, "An improved quantum
evolutionary algorithm based on artificial bee colony optimization," in
W. Yu & E. N. Sanchez, Advances in Computational intelligence, New
York: Springer-Verlag Berlin Heidelberg, 2009.
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8th ed. New York: Mc Graw-Hill International Edition, 2005, pp. 617-
654.
[28] Holland, J. H. (1998). Adaptation in Natural and Artificial Systems: An
introductory analysis with applications to biology, control, and
artificial intelligence, 5th Ed, United States of America: MIT Press.
[29] M. S. Htun, T. T. Thein, and P. Tin, "Linear Programming Approach to
Diet Problem for Black Tiger Shrimp in Shrimp Aquaculture," in Proc.
Information and Telecommunication Technologies, 2005.
[30] Ismail Abu Hassan; Hambal Hj. Hanafi; Che Utama Che Musa; and S.
Pathmasothy. 1988. "Status of Shrimp and Finfish Feeds in Malaysia,"
Report of The Workshop on Shrimp and Finfish Feed Development,
Johor Bahru, Malaysia. 25-29 October 1988.
[31] P. Kaelo, & M.M. Ali, "Integrated crossover rules in real coded genetic
algorithms," European Journal of Operational Research, vol. 176, no.
1, pp. 60-76. 2007.
[32] D. Karaboga, "An idea based on honey bee swarm for numerical
optimization," Technical Report-TR06, Erciyes University, Engineering
Faculty, Computer Engineering Department, 2005.
[33] N. Karaboga, "A new design method based on artificial bee colony
algorithm for digital IIR filters," Journal of The Franklin Institute, vol.
346, pp. 328-348, 2009.
[34] H. C. D. Kock, and M. Sinclair, "Multi-Mix Feedstock Problems on
Microcomputers," The Journal of the Operational Research Society,
vol. 38, no. 7, pp. 585-590, 1987.
[35] J. S. H. Kornbluth, and R. E. Steuer, "Multiple objective linear
fractional programming," Management Science, vol. 27, no. 9, pp. 1024-
1039, 1981.
[36] P. Lara, "Multiple objective fractional programming and livestock ration
formulation: A case study for dairy cow diets in Spain," Agricultural
Systems, vol. 41, pp. 321-334, 1993.
[37] P. Lara, and C. Romero, "An interactive multigoal programming model
for determining livestock rations: An application to dairy cows in
Andalusia, Spain," The Journal of the Operational Research Society,
vol. 43, no. 10, pp. 945-953, 1992.
[38] P. Lara, and C. Romero, "Relaxation of nutrient requirements on
livestocks rations through interactive multigoal programming,"
Agricultural Systems, vol. 45, pp. 443-453, 1994.
[39] J. P. Lazo, and D. A. Davis, "Ingredient and feed evaluation," in
Encyclopedia of Aquaculture, R. R. Stickney, Texas: Wiley-Interscience
Publication, 2000, pp. 453-463.
[40] J. McCall, "Genetic algorithms for modelling and optimisation," Journal
of Computational and Applied Mathematics, vol. 184, pp. 205-222,
2005.
[41] K. Mitani, and H. Nakayama, "A multiobjective diet planning support
system using the satisficing trade-off method," Journal of Multi-Criteria
Decision Analysis, vol. 6, pp. 131-139, 1997.
[42] G. M. Mohr, "The bulk constraint and computer formulations of leastcost
feed mixes," Review of Marketing and Agricultural Economics, vol.
40, no. 1, pp. 15-28, 1972.
[43] A. G. Munford, "A microcomputer system for formulating animal diets
which may involve liquid raw materials," European Journal of
Operational Research, vol. 41, pp. 270-276, 1989.
[44] A. G. Munford, "The use of iterative linear programming in practical
applications of animal diet formulation," Mathematics and Computers in
Simulation, vol. 42, pp. 255-261, 1996.
[45] National Academy Press, United States-Canadian tables of feed
composition, 3rd Edition, Washington, D.C: National Academic Press,
1982.
[46] J. D. O'Connor, C. J. Sniffen, D. G. Fox, and R. A. Miligan, "Least cost
dairy cattle ration formulation model based on the degradable protein
system," Journal of Dairy Science, vol. 72, pp. 2733-2745, 1989.
[47] C. V. D. Panne, and W. Popp, "Minimum-cost cattle feed under
probabilistic protein constraints," Management Science, vol. 9, no. 3,
pp. 405-430, 1963.
[48] G. M. Pesti, and A. F. Seila, "The Use of an electronic spreadsheet to
solve linear and non-linear "stochastic" feed formulation problems,"
Journal of Applied Poultry Research, vol. 8(1), pp. 110-121, 1999.
[49] F. Polimeno, T. Rehman, H. Neal, and C. M. Yates, "Integrating the use
of Linear and Dynamic Programming Methods for Dairy Cow Diet
Formulation," The Journal of the Operational Research Society, vol. 50,
no. 9, pp. 931-942, 1999.
[50] C. Pomar, F. Dubeau, M. P. Létourneau-Montminy, C. Boucher, and P.-
O. Julien, "Reducing phosphorus concentration in pig diets by adding an
environmental objective to the traditional feed formulation algorithm,"
Livestock Science, vol. 111, pp. 16-27, 2007.
[51] C. A. Poojari, and B. Varghese, "Genetic algorithm based technique for
solving chance constrained problems," European Journal of
Operational Research, vol. 185, pp. 1128-1154, 2008.
[52] A. Prékopa, "Probabilistic programming," in Handbooks in OR and MS,
vol. 10, Elsevier Science, 2003, pp. 267-351.
[53] S. A. Rahman, and F. E. Bender, "Linear programming approximation
of least-cost feed mixes with probability restrictions," Amer. J. Ag.
Econ, vol. 53, pp. 612-618, 1971.
[54] T. Rehman, and C. Romero, "Multiple-criterion decision making
techniques and their role in livestock ration formulation," Agricultural
Systems, vol. 15, pp. 1, 23-49, 1984.
[55] T. Rehman, and C. Romero, "Goal programming with penalty functions
and livestock ration formulation," Agricultural Systems, vol. 23, pp.
117-132, 1987.
[56] B. Render, J. R. M. Stair, and M. E. Hanna, Quantitative analysis for
management, 9th ed., New Jersey: Pearson Education, Inc, 2006, pp.
315-317.
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@article{"International Journal of Biological, Life and Agricultural Sciences:59377", author = "Rosshairy Abd Rahman and Chooi-Leng Ang and Razamin Ramli", title = "Investigating Feed Mix Problem Approaches: An Overview and Potential Solution", abstract = "Feed is one of the factors which play an important role in determining a successful development of an aquaculture industry. It is always critical to produce the best aquaculture diet at a minimum cost in order to trim down the operational cost and gain more profit. However, the feed mix problem becomes increasingly difficult since many issues need to be considered simultaneously. Thus, the purpose of this paper is to review the current techniques used by nutritionist and researchers to tackle the issues. Additionally, this paper introduce an enhance algorithm which is deemed suitable to deal with all the issues arise. The proposed technique refers to Hybrid Genetic Algorithm which is expected to obtain the minimum cost diet for farmed animal, while satisfying nutritional requirements. Hybrid GA technique with artificial bee algorithm is expected to reduce the penalty function and provide a better solution for the feed mix problem.
", keywords = "Artificial bee algorithm, feed mix problem, hybrid genetic algorithm.", volume = "4", number = "10", pages = "747-9", }
