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An Ant-Based Optimization Approach for Inventory Routing

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EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 227))

Abstract

The inventory routing problem (IRP) is a major concern in operation management of a supply chain because it integrates transportation activities with inventory management. Such problems are usually tackled by independently solving the underlying inventory and vehicle routing sub-problems. The present study introduces an ant-based solution framework by modeling the IRP problem as a vehicle routing task. In this context, a mixed-integer mathematical model for the IRP is developed, where a fleet of capacitated homogeneous vehicles transport different products from multiple suppliers to a retailer to meet the demand for each period over a finite planning horizon. In our model, shortages are allowed while unsatisfied demand is backlogged and can be met in future periods. The mathematical model is used to find the best compromise among transportation, holding, and backlogging costs. The corresponding vehicle routing problem is solved using an ant-based optimization algorithm. Preliminary results on randomly generated test problems are reported and assessed with respect to the optimal solutions found by established linear solvers such as CPLEX.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ioannina, GR-45110, Ioannina, Greece

    Vasileios A. Tatsis & Konstantinos E. Parsopoulos

  2. Department of Mathematics, University of Ioannina, GR-45110, Ioannina, Greece

    Konstantina Skouri

  3. Department of Business Administration, University of Macedonia, GR-54006, Thessaloniki, Greece

    Ioannis Konstantaras

Authors
  1. Vasileios A. Tatsis
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  2. Konstantinos E. Parsopoulos
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  3. Konstantina Skouri
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  4. Ioannis Konstantaras
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Corresponding author

Correspondence to Vasileios A. Tatsis .

Editor information

Editors and Affiliations

  1. , Leiden Institute of Advanced, Leiden University, Niels Bohrweg 1, Leiden, 2333CA, Netherlands

    Michael Emmerich

  2. , Leiden Institute of Advanced, Leiden University, Niels Bohrweg 1, Leiden, 2333CA, Netherlands

    Andre Deutz

  3. , Depto. de Computacion, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, 07360, Mexico

    Oliver Schuetze

  4. , Leiden Institute of Advanced, Leiden University, Niels Bohrweg 1, Leiden, 2333CA, Netherlands

    Thomas Bäck

  5. , Luxembourg Centre for, University of Luxembourg, Avenue des Hauts Fourneaux 7, Belval, 4362, Luxembourg

    Emilia Tantar

  6. , Computer Science and, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  7. , Bordeaux Mathematical Institute, Université Bordeaux I, cours de la Libération 351, Talence cedex, 33405, France

    Pierre Del Moral

  8. , UFR Sciences et Modélisation, Université Bordeaux Segalen, 3ter place de la Victoire, Bordeaux, 33076, France

    Pierrick Legrand

  9. Faculty of Sci., Tech. & Communication, Computer Science and, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Pascal Bouvry

  10. , Depto. de Computatción, CINVESTAV-IPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, 07360, Mexico

    Carlos A. Coello

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Tatsis, V.A., Parsopoulos, K.E., Skouri, K., Konstantaras, I. (2013). An Ant-Based Optimization Approach for Inventory Routing. In: Emmerich, M., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. Advances in Intelligent Systems and Computing, vol 227. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01128-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-01128-8_8

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-01127-1

  • Online ISBN: 978-3-319-01128-8

  • eBook Packages: EngineeringEngineering (R0)

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