A decomposition algorithm for robust lot sizing problem with remanufacturing option

Öykü Naz Attila; Agostinho Agra; Kerem Akartunali; Ashwin Arulselvan

doi:10.1007/978-3-319-62395-5_47

A decomposition algorithm for robust lot sizing problem with remanufacturing option

Öykü Naz Attila, Agostinho Agra, Kerem Akartunali, Ashwin Arulselvan

Management Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

6 Citations (Scopus)

75 Downloads (Pure)

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.

Original language	English
Title of host publication	Computational Science and its Applications - ICCSA 2017
Editors	Osvaldo Gervasi
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	684-695
Number of pages	12
Volume	10405
ISBN (Print)	9783319623948
DOIs	https://doi.org/10.1007/978-3-319-62395-5_47
Publication status	Published - 7 Jul 2017
Event	17th International Conference on Computational Science and its Applications - Trieste, Italy Duration: 3 Jul 2017 → 6 Jul 2017

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10405
ISSN (Print)	0302-9743

Conference

Conference	17th International Conference on Computational Science and its Applications
Abbreviated title	ICCSA 2017
Country/Territory	Italy
City	Trieste
Period	3/07/17 → 6/07/17

Keywords

robust lot sizing
remanufacturing
decomposition

Access to Document

10.1007/978-3-319-62395-5_47Licence: All rights reserved

Atilla-etal-ICCSA2017-A-decomposition-algorithm-for-robust-lot-sizing-problemAccepted author manuscript, 737 KB

1 Participation in conference
1 Invited talk

9th EURO/ALIO Conference on Applied Combinatorial Optimization
Kerem Akartunali (Participant)
25 Jun 2018 → 27 Jun 2018
Activity: Participating in or organising an event types › Participation in conference
Production Planning: From Decompositions to Robust Reformulations
Kerem Akartunali (Speaker)
9 Jan 2018
Activity: Talk or presentation types › Invited talk

Cite this

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title = "A decomposition algorithm for robust lot sizing problem with remanufacturing option",

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 {\"O}zbay (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.",

keywords = "robust lot sizing, remanufacturing, decomposition",

author = "Attila, {{\"O}yk{\"u} Naz} and Agostinho Agra and Kerem Akartunali and Ashwin Arulselvan",

note = "The final publication is available at Springer via https://doi.org/10.1007/978-3-319-62395-5_47; 17th International Conference on Computational Science and its Applications, ICCSA 2017 ; Conference date: 03-07-2017 Through 06-07-2017",

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language = "English",

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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 et al.

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 proceeding › Conference contribution book

TY - GEN

T1 - A decomposition algorithm for robust lot sizing problem with remanufacturing option

AU - Attila, Öykü Naz

AU - Agra, Agostinho

AU - Akartunali, Kerem

AU - Arulselvan, Ashwin

N1 - The final publication is available at Springer via https://doi.org/10.1007/978-3-319-62395-5_47

PY - 2017/7/7

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N2 - 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.

AB - 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.

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KW - decomposition

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A decomposition algorithm for robust lot sizing problem with remanufacturing option

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Activities

9th EURO/ALIO Conference on Applied Combinatorial Optimization

Production Planning: From Decompositions to Robust Reformulations

Cite this