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A genetic algorithm for a single product network design model with lead time and safety stock considerations

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  • Sourirajan, Karthik
  • Ozsen, Leyla
  • Uzsoy, Reha

Abstract

We consider a two-stage supply chain with a production facility that replenishes a single product at retailers. The objective is to locate distribution centers in the network such that the sum of facility location, pipeline inventory, and safety stock costs is minimized. We explicitly model the relationship between the flows in the network, lead times, and safety stock levels. We use genetic algorithms to solve the model and compare their performance to that of a Lagrangian heuristic developed in earlier work. A novel chromosome representation that combines binary vectors with random keys provides solutions of similar quality to those from the Lagrangian heuristic. The model is then extended to incorporate arbitrary demand variance at the retailers. This modification destroys the structure upon which the Lagrangian heuristic is based, but is easily incorporated into the genetic algorithm. The genetic algorithm yields significantly better solutions than a greedy heuristic for this modification and has reasonable computational requirements.

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  • Sourirajan, Karthik & Ozsen, Leyla & Uzsoy, Reha, 2009. "A genetic algorithm for a single product network design model with lead time and safety stock considerations," European Journal of Operational Research, Elsevier, vol. 197(2), pages 599-608, September.
    • Handle: RePEc:eee:ejores:v:197:y:2009:i:2:p:599-608
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    2. Ali Diabat & Jean-Philippe Richard & Craig Codrington, 2013. "A Lagrangian relaxation approach to simultaneous strategic and tactical planning in supply chain design," Annals of Operations Research, Springer, vol. 203(1), pages 55-80, March.
    3. Federico Perea & Mozart B. C. Menezes & Juan A. Mesa & Fernando Rubio-Del-Rey, 2020. "Transportation infrastructure network design in the presence of modal competition: computational complexity classification and a genetic algorithm," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 442-474, July.
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    6. Tancrez, Jean-Sébastien & Lange, Jean-Charles & Semal, Pierre, 2012. "A location-inventory model for large three-level supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 485-502.
    7. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    8. Jabbarzadeh, Armin & Haughton, Michael & Pourmehdi, Fahime, 2019. "A robust optimization model for efficient and green supply chain planning with postponement strategy," International Journal of Production Economics, Elsevier, vol. 214(C), pages 266-283.
    9. Darmawan, Agus & Wong, Hartanto & Thorstenson, Anders, 2021. "Supply chain network design with coordinated inventory control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    10. Schuster Puga, Matías & Minner, Stefan & Tancrez, Jean-Sébastien, 2019. "Two-stage supply chain design with safety stock placement decisions," International Journal of Production Economics, Elsevier, vol. 209(C), pages 183-193.
    11. Mehrdad Shahabi & Shirin Akbarinasaji & Avinash Unnikrishnan & Rachel James, 2013. "Integrated Inventory Control and Facility Location Decisions in a Multi-Echelon Supply Chain Network with Hubs," Networks and Spatial Economics, Springer, vol. 13(4), pages 497-514, December.
    12. Fathi, Mahdi & Khakifirooz, Marzieh & Diabat, Ali & Chen, Huangen, 2021. "An integrated queuing-stochastic optimization hybrid Genetic Algorithm for a location-inventory supply chain network," International Journal of Production Economics, Elsevier, vol. 237(C).
    13. Das, Kanchan, 2011. "Integrating effective flexibility measures into a strategic supply chain planning model," European Journal of Operational Research, Elsevier, vol. 211(1), pages 170-183, May.
    14. Jia Shu & Qiang Ma & Sijie Li, 2010. "Integrated location and two-echelon inventory network design under uncertainty," Annals of Operations Research, Springer, vol. 181(1), pages 233-247, December.
    15. Yazdanparast, R. & Jolai, F. & Pishvaee, M.S. & Keramati, A., 2022. "A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions," Renewable Energy, Elsevier, vol. 184(C), pages 799-819.
    16. M. H. Alavidoost & Mosahar Tarimoradi & M. H. Fazel Zarandi, 2018. "Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 809-826, April.
    17. Kumar Maiti, Manas, 2011. "A fuzzy genetic algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon," European Journal of Operational Research, Elsevier, vol. 213(1), pages 96-106, August.
    18. Sasan Khalifehzadeh & Mehdi Seifbarghy & Bahman Naderi, 2017. "Solving a fuzzy multi objective model of a production–distribution system using meta-heuristic based approaches," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 95-109, January.
    19. Yazdani, Majid & Aouam, Tarik, 2023. "Shipment planning and safety stock placement in maritime supply chains with stochastic demand and transportation times," International Journal of Production Economics, Elsevier, vol. 263(C).
    20. L. A. C. Roque & D. B. M. M. Fontes & F. A. C. C. Fontes, 2014. "A hybrid biased random key genetic algorithm approach for the unit commitment problem," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 140-166, July.
    21. Schuster Puga, Matías & Tancrez, Jean-Sébastien, 2017. "A heuristic algorithm for solving large location–inventory problems with demand uncertainty," European Journal of Operational Research, Elsevier, vol. 259(2), pages 413-423.
    22. Shahabi, Mehrdad & Unnikrishnan, Avinash & Jafari-Shirazi, Ehsan & Boyles, Stephen D., 2014. "A three level location-inventory problem with correlated demand," Transportation Research Part B: Methodological, Elsevier, vol. 69(C), pages 1-18.
    23. Kallina, Dennis & Siegfried, Patrick, 2021. "Optimization of Supply Chain Network using Genetic Algorithms based on Bill of materials," MPRA Paper 111397, University Library of Munich, Germany.

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