A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application

Mauricio Orozco-Fontalvo; Víctor Cantillo; Pablo A. Miranda

doi:10.1007/978-3-030-31140-7_20

A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application

Mauricio Orozco-Fontalvo, Víctor Cantillo, Pablo A. Miranda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper addresses the real-world supply chain network design problem with a strategic multi-commodity and multi-period inventory-location problem with stochastic demands. The proposed methodology involves a complex non-linear, non-convex, mixed integer programming model, which allows for the optimization of warehouse location, demand zone’s assignment, and manufacturing settings while minimizing the fixed costs of a distribution center (DC), along with the transportation and inventory costs in a multi-commodity, multi-period scenario. In addition, a genetic algorithm is implemented to obtain near-optimal solutions at competitive times. We applied the model to a real-world industrial case of a Colombian rolled steel manufacturing company, where a new, optimized supply chain distribution network is required to serve customers at a national level. The proposed approach provides a practical solution to optimize their distribution network, achieving significant cost reductions for the company.

Original language	English
Title of host publication	Computational Logistics - 10th International Conference, ICCL 2019, Proceedings
Editors	Carlos Paternina-Arboleda, Stefan Voß
Publisher	Springer
Pages	317-331
Number of pages	15
ISBN (Print)	9783030311391
DOIs	https://doi.org/10.1007/978-3-030-31140-7_20
Publication status	Published - 1 Jan 2019
Event	10th International Conference on Computational Logistics, ICCL 2019 - Barranquilla, Colombia Duration: 30 Sept 2019 → 2 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11756 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Computational Logistics, ICCL 2019
Country/Territory	Colombia
City	Barranquilla
Period	30/09/19 → 2/10/19

Keywords

Cycle stock
Explicit enumeration
Facility location problems
Genetic algorithms
Inventory location problems
Safety stock
Stochastic modelling
Supply chain network design

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-31140-7_20

Cite this

Orozco-Fontalvo, M., Cantillo, V., & Miranda, P. A. (2019). A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application. In C. Paternina-Arboleda, & S. Voß (Eds.), Computational Logistics - 10th International Conference, ICCL 2019, Proceedings (pp. 317-331). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11756 LNCS). Springer. https://doi.org/10.1007/978-3-030-31140-7_20

Orozco-Fontalvo, Mauricio ; Cantillo, Víctor ; Miranda, Pablo A. / A stochastic, multi-commodity multi-period inventory-location problem : Modeling and solving an industrial application. Computational Logistics - 10th International Conference, ICCL 2019, Proceedings. editor / Carlos Paternina-Arboleda ; Stefan Voß. Springer, 2019. pp. 317-331 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application",

abstract = "This paper addresses the real-world supply chain network design problem with a strategic multi-commodity and multi-period inventory-location problem with stochastic demands. The proposed methodology involves a complex non-linear, non-convex, mixed integer programming model, which allows for the optimization of warehouse location, demand zone{\textquoteright}s assignment, and manufacturing settings while minimizing the fixed costs of a distribution center (DC), along with the transportation and inventory costs in a multi-commodity, multi-period scenario. In addition, a genetic algorithm is implemented to obtain near-optimal solutions at competitive times. We applied the model to a real-world industrial case of a Colombian rolled steel manufacturing company, where a new, optimized supply chain distribution network is required to serve customers at a national level. The proposed approach provides a practical solution to optimize their distribution network, achieving significant cost reductions for the company.",

keywords = "Cycle stock, Explicit enumeration, Facility location problems, Genetic algorithms, Inventory location problems, Safety stock, Stochastic modelling, Supply chain network design",

author = "Mauricio Orozco-Fontalvo and V{\'i}ctor Cantillo and Miranda, {Pablo A.}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 10th International Conference on Computational Logistics, ICCL 2019 ; Conference date: 30-09-2019 Through 02-10-2019",

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Orozco-Fontalvo, M, Cantillo, V & Miranda, PA 2019, A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application. in C Paternina-Arboleda & S Voß (eds), Computational Logistics - 10th International Conference, ICCL 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11756 LNCS, Springer, pp. 317-331, 10th International Conference on Computational Logistics, ICCL 2019, Barranquilla, Colombia, 30/09/19. https://doi.org/10.1007/978-3-030-31140-7_20

A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application. / Orozco-Fontalvo, Mauricio; Cantillo, Víctor; Miranda, Pablo A.
Computational Logistics - 10th International Conference, ICCL 2019, Proceedings. ed. / Carlos Paternina-Arboleda; Stefan Voß. Springer, 2019. p. 317-331 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11756 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper addresses the real-world supply chain network design problem with a strategic multi-commodity and multi-period inventory-location problem with stochastic demands. The proposed methodology involves a complex non-linear, non-convex, mixed integer programming model, which allows for the optimization of warehouse location, demand zone’s assignment, and manufacturing settings while minimizing the fixed costs of a distribution center (DC), along with the transportation and inventory costs in a multi-commodity, multi-period scenario. In addition, a genetic algorithm is implemented to obtain near-optimal solutions at competitive times. We applied the model to a real-world industrial case of a Colombian rolled steel manufacturing company, where a new, optimized supply chain distribution network is required to serve customers at a national level. The proposed approach provides a practical solution to optimize their distribution network, achieving significant cost reductions for the company.

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Orozco-Fontalvo M, Cantillo V, Miranda PA. A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application. In Paternina-Arboleda C, Voß S, editors, Computational Logistics - 10th International Conference, ICCL 2019, Proceedings. Springer. 2019. p. 317-331. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-31140-7_20

A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application

Abstract

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Keywords

ASJC Scopus subject areas

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