A study of production and harvesting planning for the chicken industry

https://doi.org/10.17221/255/2016-AGRICECONCitation:You P., Hsieh Y. (2018): A study of production and harvesting planning for the chicken industry. Agric. Econ. – Czech, 64: 316-327.
download PDF

To order to raise chickens for meat, chicken farmers must select an appropriate breed and determine how many broilers to raise in each henhouse. This study proposes a mathematical programming model to develop a production planning and harvesting schedule for chicken farmers. The production planning comprises the number of batches of chickens to be raised in each henhouse, the number of chicks to be raised for each batch, what breed of chicken to raise, when to start raising and the duration of the raising period. The harvesting schedule focuses on when to harvest and how many broilers to harvest each time. Our aim was to develop proper production and harvesting schedules that enable chicken farmers to maximise profits over a planning period. The problem is a highly complicated one. We developed a hybrid heuristic approach to address the issue. The computational results have shown that the proposed model can help chicken farmers to deal with the problems of chicken-henhouse assignment, chicken raising and harvesting, and may thus contribute to increasing profits. A case study of a chicken farmer in Yunlin County (Taiwan) was carried out to illustrate the application of the proposed model. Sensitivity analysis was also conducted to explore the influence of parameter variations.

References:
BJØRNDAL TROND (1988): Optimal Harvesting of Farmed Fish. Marine Resource Economics, 5, 139-159  https://doi.org/10.1086/mre.5.2.42628926
 
Coléno F.C., Duru M. (1999): A model to find and test decision rules for turnout date and grazing area allocation for a dairy cow system in spring. Agricultural Systems, 61, 151-164  https://doi.org/10.1016/S0308-521X(99)00037-2
 
Crosson P., O’Kiely P., O’Mara F.P., Wallace M. (2006): The development of a mathematical model to investigate Irish beef production systems. Agricultural Systems, 89, 349-370  https://doi.org/10.1016/j.agsy.2005.09.008
 
Engle Carole R. (1997): Optimal Resource Allocation by Fish Farmers in Rwanda. Journal of Applied Aquaculture, 7, 1-17  https://doi.org/10.1300/J028v07n01_01
 
Engle Carole R., Kumar Ganesh, Bouras David (2010): THE ECONOMIC TRADE-OFFS BETWEEN STOCKING FINGERLINGS AND STOCKERS: A MIXED INTEGER MULTI-STAGE PROGRAMMING APPROACH. Aquaculture Economics & Management, 14, 315-331  https://doi.org/10.1080/13657305.2010.526020
 
Forsberg Odd Inge (1996): Optimal stocking and harvesting of size-structured farmed fish: A multi-period linear programming approach. Mathematics and Computers in Simulation, 42, 299-305  https://doi.org/10.1016/0378-4754(95)00132-8
 
Gradiz L., Sugimoto A., Ujihara K., Fukuhara S., Kahi A.K., Hirooka H. (2007): Beef cow–calf production system integrated with sugarcane production: Simulation model development and application in Japan. Agricultural Systems, 94, 750-762  https://doi.org/10.1016/j.agsy.2007.03.003
 
Hean Robyn L. (1994): AN OPTIMAL MANAGEMENT MODEL FOR INTENSIVE AQUACULTURE - AN APPLICATION IN ATLANTIC SALMON*. Australian Journal of Agricultural Economics, 38, 31-47  https://doi.org/10.1111/j.1467-8489.1994.tb00718.x
 
Kristensen Anders Ringgaard, Søllested Thomas Algot (2004): A sow replacement model using Bayesian updating in a three-level hierarchic Markov process. Livestock Production Science, 87, 25-36  https://doi.org/10.1016/j.livprodsci.2003.07.005
 
McCarthy Nancy, de Janvry Alain, Sadoulet Elisabeth (1998): Land allocation under dual individual–collective use in Mexico. Journal of Development Economics, 56, 239-264  https://doi.org/10.1016/S0304-3878(98)00065-0
 
Moghaddam Kamran S., DePuy Gail W. (2011): Farm management optimization using chance constrained programming method. Computers and Electronics in Agriculture, 77, 229-237  https://doi.org/10.1016/j.compag.2011.05.006
 
Ohlmann Jeffrey W., Jones Philip C. (2011): An integer programming model for optimal pork marketing. Annals of Operations Research, 190, 271-287  https://doi.org/10.1007/s10479-008-0466-3
 
Pathumnakul Supachai, Piewthongngam Kullapapruk, Khamjan Sakda (2009): Integrating a shrimp-growth function, farming skills information, and a supply allocation algorithm to manage the shrimp supply chain. Computers and Electronics in Agriculture, 66, 93-105  https://doi.org/10.1016/j.compag.2008.12.008
 
Plà-Aragonés L.M. (2005): A stochastic model for planning swine facilities. In: WSC ‘05 Proceedings of the 37th conference on Winter simulation, Orlando, Dec 4–7, 2015: 2378–2384.
 
Rodríguez Sara V., Albornoz Victor M., Plà Lluís M. (2009): A two-stage stochastic programming model for scheduling replacements in sow farms. TOP, 17, 171-189  https://doi.org/10.1007/s11750-009-0087-2
 
Rodríguez-Sánchez Sara V., Plà-Aragonés Lluís M., Albornoz Victor M. (2012): Modeling tactical planning decisions through a linear optimization model in sow farms. Livestock Science, 143, 162-171  https://doi.org/10.1016/j.livsci.2011.09.006
 
Rupasinghe J.W., Kennedy J.O.S. (2006): Optimal batch lengths for barramundi farming under seasonal variations: A dynamic programming approach. In: Proceedings of the Thirteenth Biennial Conference of the International Institute of Fisheries Economics & Trade, July 11–14, 2006, Portsmouth.
 
Stygar M., Makulska J. (2010): Application of mathematical modelling in beef herd management – a review. Annals of Animal Science, 10: 333–348.
 
Tian X., Leung P.S., Lee D.J. (2000): Size economies and optimal scheduling in shrimp production: results from a computer simulation model. Aquacultural Engineering, 22, 289-307  https://doi.org/10.1016/S0144-8609(00)00055-8
 
Villalba D., Ripoll G., Ruiz R., Bernués A. (2010): Long-term stochastic simulation of mountain beef cattle herds under diverse management strategies. Agricultural Systems, 103, 210-220  https://doi.org/10.1016/j.agsy.2010.01.003
 
Wang Jaw-Kai, Leiman Junghans (2000): Optimizing multi-stage shrimp production systems. Aquacultural Engineering, 22, 243-254  https://doi.org/10.1016/S0144-8609(00)00038-8
 
Yu Run, Leung PingSun (2005): Optimal harvesting strategies for a multi-cycle and multi-pond shrimp operation: A practical network model. Mathematics and Computers in Simulation, 68, 339-354  https://doi.org/10.1016/j.matcom.2005.01.018
 
Yu Run, Leung PingSun, Bienfang Paul (2009): Modeling partial harvesting in intensive shrimp culture: A network-flow approach. European Journal of Operational Research, 193, 262-271  https://doi.org/10.1016/j.ejor.2007.10.031
 
YU RUN, LEUNG PINGSUN (2006): Optimal Partial Harvesting Schedule for Aquaculture Operations. Marine Resource Economics, 21, 301-315  https://doi.org/10.1086/mre.21.3.42629513
 
download PDF

© 2019 Czech Academy of Agricultural Sciences