Facilities Location in the Supply Chain Network Using an Iterated Local Search Algorithm

Ali Abdi Seyedkolaei; Seyed Hadi Nasseri

doi:10.26599/FIE.2023.9270002

Fuzzy Information and Engineering 2023, 15(1): 14-25 https://doi.org/10.26599/FIE.2023.9270002

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Open Access | Issue | Published: 07 March 2023

Facilities Location in the Supply Chain Network Using an Iterated Local Search Algorithm

Show Author's Information Hide Author's Information Ali Abdi Seyedkolaei^{¹^,²}(

), Seyed Hadi Nasseri^{¹^,²}

1Department of Applied Mathematics, University of Mazandaran, Babolsar 48175, Iran

2Research Center of Optimization and Logistics, University of Mazandaran, Babolsar 48175, Iran

Keywords:

supply chain, distribution centers, location, iterated local search algorithm

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Seyedkolaei AA, Nasseri SH. Facilities Location in the Supply Chain Network Using an Iterated Local Search Algorithm. Fuzzy Information and Engineering, 2023, 15(1): 14-25. https://doi.org/10.26599/FIE.2023.9270002

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Abstract

The supply chain design problem is one of the important problems in supply chain management. In this problem, the goal is to locate several facilities for distribution centers in different geographical areas to cover customer demands. In the model presented in this paper, customer demand is met through distribution centers, while these distribution centers supply products from manufacturing plants. In this paper, a mathematical model of integer linear programming is presented that, in addition to the optimal supply chain network design, considers the facility's location. This model aims to minimize the costs of sending products from the factory to distribution centers, from distribution centers to customers, and establishing distribution centers. To evaluate the proposed model, different network samples were created randomly on small, medium and large scales for experiments. In order to perform the experiments, a linear programming solver (LP Solver) and an iterated local search algorithm were used to compare the performance of each of these methods to find the optimal solution. These experiments show the superiority of the iterated local search algorithm compared to LP Solver in achieving the optimal solution in the shortest runtime for all instances.

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Facilities Location in the Supply Chain Network Using an Iterated Local Search Algorithm

Show Author's information Hide Author's Information Ali Abdi Seyedkolaei^{¹^,²}(

), Seyed Hadi Nasseri^{¹^,²}

1Department of Applied Mathematics, University of Mazandaran, Babolsar 48175, Iran

2Research Center of Optimization and Logistics, University of Mazandaran, Babolsar 48175, Iran

Abstract

Keywords: supply chain, distribution centers, location, iterated local search algorithm

References(19)

[1]

E. Olivares-Benitez, J. L. González-Velarde, and R. Z. Ríos-Mercado, A supply chain design problem with facility location and bi-objective transportation choices, TOP, vol. 20, no. 3, pp. 729–753, 2012.

DOI Google Scholar

[2]

M. Nabipour, S. H. Nasseri, and E. T. Saber, Comparison and evaluation of built environment factors for developing pedestrian urban travels, Fuzzy Information and Engineering, vol. 13, no. 4, pp. 505–521, 2021.

DOI Google Scholar

[3]

L. Zhen, Q. Sun, K. Wang, and X. Zhang, Facility location and scale optimisation in closed-loop supply chain, Int. J. Prod. Res., vol. 57, no. 24, pp. 7567–7585, 2019.

DOI Google Scholar

[4]

S. J. Sharahi, K. Khalili-Damghani, A. R. Abtahi, and A. Rashidi-Komijan, Type-II fuzzy multi-product, multi-level, multi-Period location–allocation, production–distribution problem in supply chains: Modelling and optimisation approach, Fuzzy Information and Engineering, vol. 10, no. 2, pp. 260–283, 2018.

DOI Google Scholar

[5]

B. Afify, S. Ray, A. Soeanu, A. Awasthi, M. Debbabi, and M. Allouche, Evolutionary learning algorithm for reliable facility location under disruption, Expert Syst. Appl., vol. 115, pp. 223–244, 2019.

DOI Google Scholar

[6]

A. Memari, R. A. Abdul Rahim, A. Hassan, and R. Ahmad, A tuned NSGA-II to optimize the total cost and service level for a just-in-time distribution network, Neural Comput. Appl., vol. 28, no. 11, pp. 3413–3427, 2017.

DOI Google Scholar

[7]

E. Ataei, R. Tavakkoli-Moghaddam, and Z. Azizi, Optimizing a public transportation system by a vector-evaluated particle swarm optimization algorithm - A case study in an urban bus organization, J. Trans. Eng., vol. 9, no. 3, pp. 413–429, 2018.

Google Scholar

[8]

H. Golpîra, E. Najafi, M. Zandieh, and S. Sadi-Nezhad, Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk, Comput. Ind. Eng., vol. 107, pp. 301–312, 2017.

DOI Google Scholar

[9]

J. A. Orjuela-Castro, L. A. Sanabria-Coronado, and A. M. Peralta-Lozano, Coupling facility location models in the supply chain of perishable fruits, Res. Trans. Bus. Manage., vol. 24, pp. 73–80, 2017.

DOI Google Scholar

[10]

S. Poudel, M. Marufuzzaman, A. Quddus, S. Chowdhury, L. Bian, and B. Smith, Designing a reliable and congested multi-modal facility location problem for biofuel supply chain network, Energies, vol. 11, no. 7, p. 1682, 2018.

DOI Google Scholar

[11]

L. Zhen, Q. Sun, K. Wang, and X. Zhang, Facility location and scale optimisation in closed-loop supply chain, Int. J. Prod. Res., vol. 57, no. 24, pp. 7567–7585, 2019.

DOI Google Scholar

[12]

C. Bilir, S. O. Ekici, and F. Ulengin, An integrated multi-objective supply chain network and competitive facility location model, Comput. Ind. Eng., vol. 108, pp. 136–148, 2017.

DOI Google Scholar

[13]

S. H. Amin and F. Baki, A facility location model for global closed-loop supply chain network design, Appl. Math. Modell., vol. 41, pp. 316–330, 2017.

DOI Google Scholar

[14]

O. Kaya and D. Ozkok, A blood bank network design problem with integrated facility location, inventory and routing decisions, Netw. Spat. Econ., vol. 20, no. 3, pp. 757–783, 2020.

DOI Google Scholar

[15]

DOI Google Scholar

[16]

S. Srinivasan and S. H. Khan, Multi-stage manufacturing/re-manufacturing facility location and allocation model under uncertain demand and return, Int. J. Adv. Manuf. Technol., vol. 94, no. 5, pp. 2847–2860, 2018.

DOI Google Scholar

[17]

A. Moradi, A. Abdi Seyedkolaei, and S. A. H. Seno, Controller placement in software defined network using iterated local search, J. AI Data Min., vol. 8, no. 1, pp. 55–65, 2020.

DOI Google Scholar

[18]

A. Abdi Seyedkolaei, S. A. H. Seno, and A. Moradi, Dynamic controller placement in software-defined networks for reducing costs and improving survivability, Trans. Emerg. Telecommun. Technol., vol. 32, no. 1, p. e4152, 2021.

DOI Google Scholar

[19]

J. Hamon, C. Dhaenens, G. Even, and J. Jacques, Feature selection in high dimensional regression problems for genomic, presented at the 10^th Int. Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Nice, France, 2013.

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Received: 02 October 2022

Revised: 24 November 2022

Accepted: 29 December 2022

Published: 07 March 2023

Issue date: March 2023

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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).