In the present day, one of the most common activities of everyday life is going to a supermarket or similar retail spaces to buy groceries. Many consumers organizations like The European Consumer Organization [1], advise buyers to prepare a “grocery list” in order to be ready for this activity. The present work proposes a system that helps to develop this activity in several ways: Firstly, it enables the user to create lists with different levels of abstraction: from concrete products to generic ones (or families of products). Secondly, the lists are collaborative and can be shared with other users. Finally, it automatically determines the best store to buy a given product using the proposed optimization algorithm. Furthermore, the optimization algorithm assigns a part of the list to each user balancing the cost that every user has to pay and choosing the cheapest supermarket where they have to buy.


Balanced shopping list Purchase optimization Collaborative list 



This work was partially supported by FEDER, the Spanish Ministry of Economy and Competitiveness (Project ECO2013-47129-C4-3-R) and the Regional Government of Castilla y León (Project BU329U14), Spain.


  1. 1.
    T.E.C. Organization: The European consumer organization (2016).
  2. 2.
    Ver Ploeg, M., Mancino, L., Todd, J.E., Clay, D.M., Benjamin, Scharadin: Where do Americans usually shop for food and how do they travel to get there? Initial Findings From the National Household Food Acquisition and Purchase Survey, U.S.D.o. Agriculture, Editor. Economic Research Service (2015)Google Scholar
  3. 3.
    Miller, M.A., et al.: Food purchasing habits of participants in the supplemental nutrition assistance program (SNAP) among shelby county, TN residents. J. Acad. Nutr. Diet. 115(9, Supplement), A76 (2015)CrossRefGoogle Scholar
  4. 4.
    Behrens, J.H., et al.: Consumer purchase habits and views on food safety: a Brazilian study. Food Control 21(7), 963–969 (2010)CrossRefGoogle Scholar
  5. 5.
    Feo, T.A., Resende, M.G.C.: Greedy Randomized Adaptive Search Procedures. J. Global Optim. 6(2), 109–133 (1995)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Festa, P., Resende, M.G.C.: An annotated bibliography of GRASP - part I: algorithms. Int. Trans. Oper. Res. 16(1), 1–24 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Festa, P., Resende, M.G.C.: An annotated bibliography of GRASP-part II: applications. Int. Trans. Oper. Res. 16(2), 131–172 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Resende, M.G.C.: Metaheuristic hybridization with greedy randomized adaptive search procedures. Tutorials Oper. Res. 295–319 (2008)Google Scholar
  9. 9.
    Hansen, P., Mladenović, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130(3), 449–467 (2001)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Mladenovic, N.: A variable neighborhood algorithm - a new metaheuristics for combinatorial optimization. In: Abstract of Papers Presented at Optimization Days, Montreal Canada, p. 112 (1995)Google Scholar
  11. 11.
    Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Nguyen, V.-P., Prins, C., Prodhon, C.: Solving the two-echelon location routing problem by a GRASP reinforced by a learning process and path relinking. Eur. J. Oper. Res. 216(1), 113–126 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Villegas, J.G., et al.: GRASP/VND and multi-start evolutionary local search for the single truck and trailer routing problem with satellite depots. Eng. Appl. Artif. Intell. 23(5), 780–794 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.University of BurgosBurgosSpain

Personalised recommendations