Skip to main content
SpringerLink
Log in
Book cover

Applications of Metaheuristics in Process Engineering pp 373–394Cite as

A Multi-Objective Modelling and Optimization Framework for Operations Management of a Fresh Fruit Supply Chain: A Case Study on a Mexican Lime Company

  • Chapter
  • First Online:

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. 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)

    Article  MATH  Google Scholar 

  2. Allen, S., Schuster, E.: Controlling the risk for an agricultural harvest. Manuf. Serv. Oper. Manag. 6(3), 225–236 (2004)

    Google Scholar 

  3. Annevelink, E.: Operational planning in horticulture: Optimal space allocation in pot-plant nurseries using heuristic techniques. J. Agr. Eng. Res. 51, 167–177 (1992)

    Article  Google Scholar 

  4. Apaiah, R., Hendrix, E.: Design of a supply chain network for pea-based novel protein foods. J. Food Eng. 70(3), 383–391 (2005)

    Article  Google Scholar 

  5. Proceedings of bogor agricultural university’s seminars (2010)

    Google Scholar 

  6. Berbel, J.: Analysis of protected cropping: an application of multiobjective programming techniques to spanish horticulture. Eur. Rev. Agr. Econ. 16(2), 203–216 (1989)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Broekmeulen, R.: Operations management of distribution centers for vegetables and fruits. Int. Trans. Oper. Res. 5(6), 501–508 (1998)

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. 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)

    Article  MATH  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Hayashi, K.: Multicriteria analysis for agricultural resource management: a critical survey and future perspectives. Eur. J. Oper. Res. 122(2), 486–500 (2000)

    Article  MATH  Google Scholar 

  17. Hester, S.M., Cacho, O.: Modelling apple orchard systems. Agr. Syst. 77(2), 137–154 (2003)

    Article  Google Scholar 

  18. Ioannou, G.: Streamlining the supply chain of the hellenic sugar industry. J. Food Eng. 70(3), 323–332 (2005)

    Article  Google Scholar 

  19. Itoh, T., Ishii, H., Nanseki, T.: A model of crop planning under uncertainty in agricultural management. Int. J. Prod. Econ. 81, 555–558 (2003)

    Article  Google Scholar 

  20. Kazaz, B.: Production planning under yield and demand uncertainty with yield-dependent cost and price. Manuf. Serv. Oper. Manag. 6(3), 209–224 (2004)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. 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)

    Google Scholar 

  24. Ortmann, F., Van Vuuren, J., Van Dyk, F.: Modelling the south african fruit export infrastructure: A case study (2006)

    Google Scholar 

  25. Rantala, J.: Optimizing the supply chain strategy of a multi-unit finnish nursery company. Silva Fennica 38(2), 203–215 (2004)

    Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. 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)

    Article  Google Scholar 

  28. 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)

    Article  MATH  Google Scholar 

  29. 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)

    Article  MATH  Google Scholar 

Download references

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

  1. LGC UMR 5503 CNRS/INPT/UPS; ENSIACET, INPT, Université de Toulouse, 4, Allée Emile Monso, BP 84234, F-31432, Toulouse, France

    Marco A. Miranda-Ackerman & Catherine Azzaro-Pantel

  2. Research and Postgraduate Studies Division, Instituto Tecnológico de Orizaba, Av. Instituto Tecnológico 852 Col. Emiliano Zapata, 94300, Orizaba, Veracruz, México

    Marco A. Miranda-Ackerman & Alberto A. Aguilar-Lasserre

  3. Research and Postgraduate Studies Division, Instituto Tecnológico Superior de Misantla.Km 1.8 Carretera a Loma del Cojolite., 93821, Misantla, Veracruz, México

    Gregorio Fernández-Lambert

Authors
  1. Marco A. Miranda-Ackerman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gregorio Fernández-Lambert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Catherine Azzaro-Pantel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alberto A. Aguilar-Lasserre
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Catherine Azzaro-Pantel .

Editor information

Editors and Affiliations

  1. Evol. Comput. & Image Proc. Group, Center for Develop. of Adv. Computing, Pune, India

    Jayaraman Valadi

  2. Lab. LiSSi (EA 3956), Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

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

Reprints 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)

Publish with us

Policies and ethics

Search

Navigation