A decomposition algorithm for robust lot sizing problem with remanufacturing option

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)

Abstract

In this paper, we propose a decomposition procedure for constructing robust optimal production plans for reverse inventory systems. Our method is motivated by the need of overcoming the excessive computational time requirements, as well as the inaccuracies caused by imprecise representations of problem parameters. The method is based on a min-max formulation that avoids the excessive conservatism of the dualization technique employed by Wei et al. (2011). We perform a computational study using our decomposition framework on several classes of computer generated test instances and we report our experience. Bienstock and Özbay (2008) computed optimal base stock levels for the traditional lot sizing problem when the production cost is linear and we extend this work here by considering return inventories and setup costs for production. We use the approach of Bertsimas and Sim (2004) to model the uncertainties in the input.
LanguageEnglish
Title of host publicationComputational Science and its Applications - ICCSA 2017
EditorsOsvaldo Gervasi
Place of PublicationCham, Switzerland
PublisherSpringer
Pages684-695
Number of pages12
Volume10405
ISBN (Print)9783319623948
DOIs
Publication statusPublished - 7 Jul 2017
Event17th International Conference on Computational Science and its Applications - Trieste, Italy
Duration: 3 Jul 20176 Jul 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10405
ISSN (Print)0302-9743

Conference

Conference17th International Conference on Computational Science and its Applications
Abbreviated titleICCSA 2017
CountryItaly
CityTrieste
Period3/07/176/07/17

Fingerprint

Remanufacturing
Lot sizing
Decomposition
Conservatism
Production cost
Inventory cost
Base stock
Inventory systems
Setup cost
Uncertainty

Keywords

  • robust lot sizing
  • remanufacturing
  • decomposition

Cite this

Attila, Ö. N., Agra, A., Akartunali, K., & Arulselvan, A. (2017). A decomposition algorithm for robust lot sizing problem with remanufacturing option. In O. Gervasi (Ed.), Computational Science and its Applications - ICCSA 2017 (Vol. 10405, pp. 684-695). (Lecture Notes in Computer Science; Vol. 10405). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-62395-5_47
Attila, Öykü Naz ; Agra, Agostinho ; Akartunali, Kerem ; Arulselvan, Ashwin. / A decomposition algorithm for robust lot sizing problem with remanufacturing option. Computational Science and its Applications - ICCSA 2017. editor / Osvaldo Gervasi. Vol. 10405 Cham, Switzerland : Springer, 2017. pp. 684-695 (Lecture Notes in Computer Science).
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Attila, ÖN, Agra, A, Akartunali, K & Arulselvan, A 2017, A decomposition algorithm for robust lot sizing problem with remanufacturing option. in O Gervasi (ed.), Computational Science and its Applications - ICCSA 2017. vol. 10405, Lecture Notes in Computer Science, vol. 10405, Springer, Cham, Switzerland, pp. 684-695, 17th International Conference on Computational Science and its Applications, Trieste, Italy, 3/07/17. https://doi.org/10.1007/978-3-319-62395-5_47

A decomposition algorithm for robust lot sizing problem with remanufacturing option. / Attila, Öykü Naz; Agra, Agostinho; Akartunali, Kerem; Arulselvan, Ashwin.

Computational Science and its Applications - ICCSA 2017. ed. / Osvaldo Gervasi. Vol. 10405 Cham, Switzerland : Springer, 2017. p. 684-695 (Lecture Notes in Computer Science; Vol. 10405).

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Attila ÖN, Agra A, Akartunali K, Arulselvan A. A decomposition algorithm for robust lot sizing problem with remanufacturing option. In Gervasi O, editor, Computational Science and its Applications - ICCSA 2017. Vol. 10405. Cham, Switzerland: Springer. 2017. p. 684-695. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-62395-5_47