Abstract
The the agriculture sector in developing countries has a large production share in the global fresh fruit market. Yet, in many cases, the land production yield indices at the orchard level are lower than the values related to more technologically developed countries. This situation leads to economic losses due to poor performance in productivity, efficiency and quality, which in turn is related to a technological and managerial gap. In this chapter, an operations management framework is proposed that tries to balance the market requirements (i.e. quality and quantity) with the capacity of the production system. This is performed through a multi-objective optimization approach that helps orchard managers synchronize the production yields with market demand and quality requirements. The model also allows the production managers to have a forecasting tool based on historical data. The model integrates the full supply chain through a set of sub-models for each stage of the production life cycle. The objective of the model is to minimize cost while maximizing sales. The optimization strategy involves a variant of the so-called NSGA II algorithm. The case study of an exporting lime packaging company is developed to illustrate the proposed framework and its possible impact on performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahumada, O., Villalobos, J.: Application of planning models in the agri-food supply chain: A review. Eur. J. Oper. Res. 196(1), 1–20 (2009)
Allen, S., Schuster, E.: Controlling the risk for an agricultural harvest. Manuf. Serv. Oper. Manag. 6(3), 225–236 (2004)
Annevelink, E.: Operational planning in horticulture: Optimal space allocation in pot-plant nurseries using heuristic techniques. J. Agr. Eng. Res. 51, 167–177 (1992)
Apaiah, R., Hendrix, E.: Design of a supply chain network for pea-based novel protein foods. J. Food Eng. 70(3), 383–391 (2005)
Proceedings of bogor agricultural university’s seminars (2010)
Berbel, J.: Analysis of protected cropping: an application of multiobjective programming techniques to spanish horticulture. Eur. Rev. Agr. Econ. 16(2), 203–216 (1989)
Blanco, A., Masini, G., Petracci, N., Bandoni, J.: Operations management of a packaging plant in the fruit industry. J. Food Eng. 70(3), 299–307 (2005)
Broekmeulen, R.: Operations management of distribution centers for vegetables and fruits. Int. Trans. Oper. Res. 5(6), 501–508 (1998)
Caixeta-Filho, J., van Swaay-Neto, J., de Pádua-Wagemaker, A.: Optimization of the production planning and trade of lily flowers at jan de wit company. Interfaces 32(1), 35–46 (2002)
Darby-Dowman, K., Barker, S., Audsley, E., Parsons, D.: A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture. J. Oper. Res. Soc. 83–89 (2000)
Dietz, A., Azzaro-Pantel, C., Pibouleau, L., Domenech, S.: Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations. Comput. Chem. Eng. 30(4), 599–613 (2006)
Diop, N., Jaffee, S.: Fruits and vegetables: global trade and competition in fresh and processed product markets. Global Agricultural Trade and Developing Countries, pp. 237–57 (2005)
Georgiadis, P., Vlachos, D., Iakovou, E.: A system dynamics modeling framework for the strategic supply chain management of food chains. J. Food Eng. 70(3), 351–364 (2005)
Gigler, J., Hendrix, E., Heesen, R., Van den Hazelkamp, V., Meerdink, G.: On optimisation of agri chains by dynamic programming. Eur. J. Oper. Res. 139(3), 613–625 (2002)
Gomez, A., Pibouleau, L., Azzaro-Pantel, C., Domenech, S., Latgé, C., Haubensack, D.: Multiobjective genetic algorithm strategies for electricity production from generation iv nuclear technology. Energy Convers. Manag. 51(4), 859–871 (2010)
Hayashi, K.: Multicriteria analysis for agricultural resource management: a critical survey and future perspectives. Eur. J. Oper. Res. 122(2), 486–500 (2000)
Hester, S.M., Cacho, O.: Modelling apple orchard systems. Agr. Syst. 77(2), 137–154 (2003)
Ioannou, G.: Streamlining the supply chain of the hellenic sugar industry. J. Food Eng. 70(3), 323–332 (2005)
Itoh, T., Ishii, H., Nanseki, T.: A model of crop planning under uncertainty in agricultural management. Int. J. Prod. Econ. 81, 555–558 (2003)
Kazaz, B.: Production planning under yield and demand uncertainty with yield-dependent cost and price. Manuf. Serv. Oper. Manag. 6(3), 209–224 (2004)
Lemanowicz, M., Krukowski, A.: Quantitative description of the fruit industry and fruit supply chains in poland. In: 113th Seminar, September 3–6, 2009, Chania, Crete, Greece, 58083. European Association of Agricultural Economists (2009)
Márquez, A., Baños, R., Gil, C., Montoya, M., Manzano-Agugliaro, F., Montoya, F.: Multi-objective crop planning using pareto-based evolutionary algorithms. Agr. Econ. 42(6), 649–656 (2011)
Masini, G., Blanco, A., Petracci, N., Bandoni, J.: Supply chain tactical optimization in the fruit industry. Process Systems Engineering: Supply Chain Optimization, vol. 4, pp. 121–172 (2007)
Ortmann, F., Van Vuuren, J., Van Dyk, F.: Modelling the south african fruit export infrastructure: A case study (2006)
Rantala, J.: Optimizing the supply chain strategy of a multi-unit finnish nursery company. Silva Fennica 38(2), 203–215 (2004)
Saedt, A., Hendriks, T., Smits, F.: A transition planning method applied in a decision support system for potplant nurseries. Eur. J. Oper. Res. 52(2), 142–154 (1991)
Ten Berge, H., Van Ittersum, M., Rossing, W., Van de Ven, G., Schans, J.: Farming options for the netherlands explored by multi-objective modelling. Eur. J. Agron. 13(2), 263–277 (2000)
Vitoriano, B., Ortuño, M., Recio, B., Rubio, F., Alonso-Ayuso, A.: Two alternative models for farm management: discrete versus continuous time horizon. Eur. J. Oper. Res. 144(3), 613–628 (2003)
Widodo, K., Nagasawa, H., Morizawa, K., Ota, M.: A periodical flowering–harvesting model for delivering agricultural fresh products. Eur. J. Oper. Res. 170(1), 24–43 (2006)
Acknowledgements
We would like to give special thanks to the Mexican National Council of Science and Technology (CONACYT) and the Ministry of Education (SEP) for funding this research project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendices
Appendix 1
16.1.1 Nomenclature
- p j :
-
sale price of each target market j
- Qs j :
-
quantity to be sold or sold during planned time period per target market quality type j
- Co j, h :
-
costs derived at orchard h due to cultivation of j quality type
- Cp j, h :
-
cost derived at packaging plant from processing j quality type fruit from h orchard.
- Ct j, h :
-
cost derived aimed at distribution and transportation
- Q j :
-
total fruit quantity of type j from all d orchards h
- R j, h :
-
ratio of each quality type j obtained for each orchard h
- c i :
-
cost of each application of the agricultural practice
- Qt h :
-
total quantity produced (e.g. kilograms) of fruit produced from a given orchard h
- Cm j :
-
manufacturing cost incurred during packaging process to obtain j quality fruit for sale
- Cpp j :
-
average cost of fruit processing and packaging per quality type j
- Qr j :
-
quantity of fruit quality type j coming from third party suppliers
- Cw j :
-
cost due to warehousing product of quality type j
- Cu j :
-
average cost of maintaining one unit of quantity during one unit of time in storage of j quality type fruit
- Qw j :
-
quantity of stored j quality type fruit in warehouse during a given time period
- Cr j :
-
total cost due to outsourced vendor supplied fresh fruit to be processed.
- Cvr j :
-
average price to pay for outsourced vendor supplied fruit per quantity unit of j type quality fruit.
- Co j :
-
total cost incurred during production from all supplying orchards to obtain j quality fruit
16.1.2 Abbreviation
- DC:
-
Developing countries
- MLR:
-
Multiple linear regression
- OPS:
-
Orchard production system
- PPS:
-
Packaging plant system
- SC:
-
Supply chain
- TPS:
-
Transportation and distribution system
- SO:
-
Seasonal orchards
- IO:
-
Irrigated orchard
- PC:
-
Pessimistic case
- OC:
-
Optimistic case
- HC:
-
Historically modelled case
Appendix 2
Variables used to control and predict orchard output yield by case study orchard.
For orchard 1:
- x1:
-
Average estimated rain fall for period in mm
- x2:
-
Average estimated temperature for period in∘C
- x3:
-
Number of application of Phosphoric acid to be made in period
- x4:
-
Number of application of Sulfur to be made in period
- x5:
-
Number of application of Magnesium to be made in period
- x6:
-
Number of applications of Potassium to be made in period
- x7:
-
Number of applications of Micronutrients to be made in period
- x8:
-
Number of applications of Micronutrients to be made in period
- x9:
-
Number of applications of NPK compound to be made in period
- x10:
-
Number of applications of Nitrogen to be made in period
- x11:
-
Number of applications of Ammonium sulfate to be made in period
- x12:
-
Number of applications of Benomile to be made in period
- x13:
-
Number of applications of Dicofol to be made in period
- x14:
-
Number of applications of Glifosate to be made in period
- x15:
-
Number of applications of Malathion to be made in period
- x16:
-
Number of applications of Tricarboxides to be made in period
For orchard 2:
- x1:
-
Average estimated rain fall for period in mm
- x2:
-
Average estimated temperature for period in∘C
- x3:
-
Number of applications of Phosphoric acid to be made in period
- x4:
-
Number of applications of Sulfur to be made in period
- x5:
-
Number of applications of Magnesium to be made in period
- x6:
-
Number of applications of Potassium to be made in period
- x7:
-
Number of applications of Micronutrients to be made in period
- x8:
-
Number of applications of Micronutrients to be made in period
- x9:
-
Number of applications of NPK compound to be made in period
- x10:
-
Number of applications of Nitrogen to be made in period
- x11:
-
Number of applications of Ammonium sulfate to be made in period
- x12:
-
Number of applications of Benomile to be made in period
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Miranda-Ackerman, M.A., Fernández-Lambert, G., Azzaro-Pantel, C., Aguilar-Lasserre, A.A. (2014). A Multi-Objective Modelling and Optimization Framework for Operations Management of a Fresh Fruit Supply Chain: A Case Study on a Mexican Lime Company. In: Valadi, J., Siarry, P. (eds) Applications of Metaheuristics in Process Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06508-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-06508-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06507-6
Online ISBN: 978-3-319-06508-3
eBook Packages: Computer ScienceComputer Science (R0)