The PrePack Optimization Problem

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8451)


The goal of packing optimization is to provide a foundation for decisions related to inventory allocation as merchandise is brought to warehouses and then dispatched. Major retail chains must fulfill requests from hundreds of stores by dispatching items stored in their warehouses. The demand for clothing items may vary to a considerable extent from one store to the next. To take this into account, the warehouse must pack “boxes” containing different mixes of clothing items. The number of distinct box types has a major impact on the operating costs. Thus, the PrePack problem consists in determining the number and contents of the box types, as well as the allocation of boxes to stores. This paper introduces the PrePack problem and proposes CP and MIP models and a metaheuristic approach to address it.


Assignment Problem Constraint Programming Memetic Algorithm Large Neighborhood Search Metaheuristic Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Erie, C.W., Lee, J.S., Paske, R.T., Wilson, J.P.: Dynamic bulk packing and casing. International Business Machines Corporation, US20100049537 A1 (2010)Google Scholar
  2. 2.
    Vakhutinsky, A., Subramanian, S., Popkov, Y., Kushkuley, A.: Retail pre-pack optimizer. Oracle International Corporation, US20120284079 A1 (2012)Google Scholar
  3. 3.
    Pratt, R.W.: Computer-implemented systems and methods for pack optimization. SAS Institute, US20090271241 A1 (2009)Google Scholar
  4. 4.
    Chandra, A.K., Hirschberg, D.S., Wong, C.K.: Approximate algorithms for some generalized knapsack problems. Theoretical Computer Science 3(3), 293–304 (1976)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Mendoza, J.E., Medaglia, A.L., Velasco, N.: An evolutionary-based decision support system for vehicle routing: The case of a public utility. Decision Support Systems 46, 730–742 (2009)CrossRefGoogle Scholar
  6. 6.
    Medaglia, A.L., Gutérrez, E.J.: An object-oriented framework for rapid development of genetic algorithms. Handbook of Research on Nature Inspired Computing for Economics and Management. Idea Publishing Group (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CIRRELTÉcole Polytechnique de MontréalMontrealCanada
  2. 2.Université Catholique de l’Ouest, LARIS (EA 7315)AngersFrance
  3. 3.Université du Québec à MontréalMontrealCanada

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