Research Article
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Year 2025, Volume: 10 Issue: 1, 18 - 33, 06.05.2025

Duygun Fatih Demirel , Afra Alev , Begüm Buse Erturan , Ecenur Bağrıyanık , Eylül Akkaya , Şeyda Zahide Gündoğdu

https://doi.org/10.26650/JTL.2025.1608346

Abstract

References

  • AYyıLdız, E., Şahin, M. C., Taşkın, A. (2023). A MuLti Depot MuLti Product SpLit DeLiverY VehicLe Routing ProbLem with Time Windows: A ReaL Cash in Transit ProbLem AppLication in lstanbuL, TurkeY. Journal of Transportation and Logistics, 7(2), 213-232. 10.26650/ JTL.2022.1113726 google scholar
  • Bacchetti, A., Bertazzi, L., & Zanardini, M. (2020). Optimizing the distribution pLanning process in suppLY chains with distribution strategY choice. Journal of the Operational Research Society, 72(7), 1525-1538. httpsdoi.org/10.1080/01605682.2020.1727785 google scholar
  • Baller, R., Fontaine, P., Minner, S., & Lai, Z. (2022). Optimizing Automotive lnbound Logistics: A Mixed-lnteger Linear Programming Approach. Transportation Research Part E: Logistics and Transportation Review, 163, 102734. https://doi.org/10.1016/j.tre.2022. 102734 google scholar
  • Bilgen, B., & Ozkarahan, İ. (2007). A Mixed-lnteger Linear Programming Model for Bulk Grain Blending and Shipping. International Journal of Production Economics, 107(2), 555-571. https://doi.org/10.1016/j.ijpe.2006.11.008 google scholar
  • BoujeLben, K. M., GicqueL, C., & Minoux, M. (2014). A Distribution Network Design ProbLem in the Automotive lndustrY: MlP FormuLation and Heuristics. Computers & Operations Research, 52, 16-28. https://doi.org/10.1016Zj.cor.2014.07.007 google scholar
  • Buriticâ, N. C., Escobar, J. W., & Gutierrez, R. (2018). SupplY Network Design bY Using CLustering and Mixed Integer Programming. International Journal of Industrial Engineering and Management, 9(2), 59-68. google scholar
  • Dündar, A. O., Tekin, M., Peker, K., Şahman, M. A., & KaraoğLan, İ. (2022). A mathematicaL modeL for muLti-period multi-stage muLti-mode multi-product capacitated wheat supply network design probLem and a case study. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(1), 265-281. https://doi.org/10.17341/gazimmfd.837124 google scholar
  • Ghahremani-Nahr, J., Najafi, S. E., & Nozari, H. (2019). A Combined Transportation Model for the Fruit and VegetabLe SuppLY Chain Network. Journal of Optimization in Industrial Engineering, 15(2), 131-145. https://doi.org/10.22094/joie.2022.1948231.1925 google scholar
  • Hajmirfattahtabrizi, M. H., & Song, H. (2019). Investigation of BottLenecks in SuppLY Chain SYstem for Minimizing TotaL Cost bY Integrating Manufacturing ModeLLing Based on MINLP Approach. Applied Sciences, 9(6), 1185. https://doi.org/10.3390/app9061185 google scholar
  • Jahani, H., Abbasi, B., Sheu, J.-B., & KLibi, W. (2024). SuppLY chain network design with financiaL considerations: A comprehensive review. European Journal of Operational Research, 312(3), 799-839. https://doi.org/10.1016/j.ejor.2023.02.033 google scholar
  • KhaLiLi-Fard, A., Sabouhi, F., & Bozorgi-Amiri, A. (2024). Data-driven robust optimization for a sustainabLe steeL suppLY chain network design: Toward the circuLar economY. Computers & IndustriaL Engineering, 195, 110408. https://doi.org/10.1016/j.cie.2024.110408 google scholar
  • Kheirabadi, M., Naderi, B., Arshadikhamseh, A., & Roshanaei, V. (2019). A mixed-integer program and a Lagrangian-based decomposition aLgorithm for the suppLY chain network design with quantitY discount and transportation modes. Expert Systems with Applica-tions, 137, 504-516. https://doi.org/10.1016/j.eswa.2019.07.004 google scholar
  • KızıLkaYa, İ., Kevser, T., OfluoğLu, H., ÖLçücüer, F., DemireL, D. F. (2024). Determining a New Warehouse Location for an ELectricaL Home AppLiances CompanY. In N. M. Durakbasa & M. G. GençYıLmaz (Eds.), IndustriaL Engineering in the IndustrY 4.0 Era (pp. 733-746). Cham, SwitzerLand: Springer Nature. google scholar
  • Kravets, A., Bogachev, V., Egorova, I., & Bogachev, T. (2020). MuLtimodaL Freight Transportation Based on MuLticriteria Optimization bY Time Indicators. Transportation Research Procedia, 54, 243-252. https://doi.org/10.1016/j.trpro.2021.02.070 google scholar
  • Lee, J., Ko, C., & Moon, I. (2024). E-commerce suppLY chain network design using on-demand warehousing sYstem under uncertaintY. International Journal of Production Research, 62(5), 1901-1927. https://doi.org/10.1080/00207543.2022.2128462 google scholar
  • Li, J., Liu, Y., & Yang, G. (2024). Two-stage distributionaLLY robust optimization modeL for a pharmaceuticaL coLd suppLY chain network design probLem. InternationaL Transactions in OperationaL Research, 31, 3459-3493. https://doi.org/10.1111/itor.13267 google scholar
  • Luathep, P., Sumalee, A., Lam, W. H. K., Li, Z.-C., & Lo, H. K. (2011). Global optimization method for mixed transportation network design probLem: A mixed-integer Linear programming approach. Transportation Research Part B: Methodological, 45(5), 808-827. https:// doi.org/10.1016/j.trb.2011.02.002 google scholar
  • MogaLe, D. G., DoLgui, A., KandhwaY, R., Kumara, S. K., & Tiwari, M. K. (2017). A muLti-period inventorY transportation modeL for tacticaL pLanning of food grain suppLY chain. Computers & Industrial Engineering, 110, 379-394. https://doi.org/10.1016/j.cie.2017.06.008 google scholar
  • MogaLe, D. G., Kumar, M., Kumara, S. K., & Tiwari, M. K. (2018). Grain siLo Location-aLLocation probLem with dweLL time for optimization of food grain suppLY chain network. Transportation Research Part E: Logistics and Transportation Review, 111, 40-69. https://doi. org/10.1016/j.tre.2018.01.004 google scholar
  • Qiu, R., Zhang, B., Zhao, W., Tu, R.-F., He, M.-Q., Liao, Q., & Liang, Y.-T. (2024). An integrated MlNLP modeL for muLti-partY coordination in downstream oiL suppLY chain. Petroleum Science, 21(3), 2066-2079. https://doi.org/10.1016/j.petsci.2023.12.008 google scholar
  • SadjadY, H., & Davoudpour, H. (2012). Two-echeLon, muLti-commoditY suppLY chain network design with mode seLection, Lead-times and inventorY costs. Computers & Operations Research, 39(7), 1345-1354. https://doi.org/10.1016/j.cor.2011.08.003 google scholar
  • Yang, L., & Zhou, X. (2017). Optimizing on-time arrivaL probabiLitY and percentiLe traveL time for eLementarY path finding in time-dependent transportation networks: Linear mixed integer programming reformuLations. Transportation Research Part B: Methodological, 96, 68-91. https://doi.org/10.1016/j.trb.2016.11.012 google scholar
  • You, F., & Grossmann, l. E. (2010). lntegrated MuLti-EcheLon SuppLY Chain Design with lnventories Under UncertaintY: MlNLP ModeLs, ComputationaL Strategies. Process Systems Engineering, 56(2), 419-440. https://doi.org/10.1002/aic.12010 google scholar
  • Zhou, N., & Zhang, J. (2023). Optimization Research of Transportation Costs and EfficiencY of MuLtimodaL Transportation SYstem. google scholar
  • European Journal of Operational Research, 299(2), September 2021, 299(2). https://doi.org/10.1109/lCllCS59993.2023.10421588 google scholar

A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company

Year 2025, Volume: 10 Issue: 1, 18 - 33, 06.05.2025

Duygun Fatih Demirel , Afra Alev , Begüm Buse Erturan , Ecenur Bağrıyanık , Eylül Akkaya , Şeyda Zahide Gündoğdu

https://doi.org/10.26650/JTL.2025.1608346

Abstract

Designing a distribution network for a company is critical as it determines how efficiently and cost-effectively prod ucts are transported. An optimized distribution network should minimize costs and delivery times while maximizing service levels. In this context, the location and number of facilities such as warehouses and factories, as well as the choice of transportation modes, play a significant role in the network’s performance. This study examines a distribution network design problem experienced by a packaging company. Currently, the company operates a single warehouse shipment management system for its operations in France. However, the company’s sales group in France believes that replacing the single-warehouse system with a two-warehouse system could optimize transportation costs while ensuring on-time deliveries. Therefore, the company analyzes the feasibility of such a transition. To achieve this, mixed-integer linear programming (MILP) models are developed, minimizing total distribution costs between the manufacturing plants and the warehouses while determining the distribution of deliveries through three transportation modes—trucks, trains, and ships—that ensures timely delivery of demands. The results indicate that the company should maintain its current single warehouse policy but can favor reduction in transportation costs by focusing on production lead times and delivery prices of transportation modes.

Keywords

Mixed Integer Linear Programming Distribution Network Design Problem Transportation Mode Selection Supply Chain Network Design Problem

References

  • AYyıLdız, E., Şahin, M. C., Taşkın, A. (2023). A MuLti Depot MuLti Product SpLit DeLiverY VehicLe Routing ProbLem with Time Windows: A ReaL Cash in Transit ProbLem AppLication in lstanbuL, TurkeY. Journal of Transportation and Logistics, 7(2), 213-232. 10.26650/ JTL.2022.1113726 google scholar
  • Bacchetti, A., Bertazzi, L., & Zanardini, M. (2020). Optimizing the distribution pLanning process in suppLY chains with distribution strategY choice. Journal of the Operational Research Society, 72(7), 1525-1538. httpsdoi.org/10.1080/01605682.2020.1727785 google scholar
  • Baller, R., Fontaine, P., Minner, S., & Lai, Z. (2022). Optimizing Automotive lnbound Logistics: A Mixed-lnteger Linear Programming Approach. Transportation Research Part E: Logistics and Transportation Review, 163, 102734. https://doi.org/10.1016/j.tre.2022. 102734 google scholar
  • Bilgen, B., & Ozkarahan, İ. (2007). A Mixed-lnteger Linear Programming Model for Bulk Grain Blending and Shipping. International Journal of Production Economics, 107(2), 555-571. https://doi.org/10.1016/j.ijpe.2006.11.008 google scholar
  • BoujeLben, K. M., GicqueL, C., & Minoux, M. (2014). A Distribution Network Design ProbLem in the Automotive lndustrY: MlP FormuLation and Heuristics. Computers & Operations Research, 52, 16-28. https://doi.org/10.1016Zj.cor.2014.07.007 google scholar
  • Buriticâ, N. C., Escobar, J. W., & Gutierrez, R. (2018). SupplY Network Design bY Using CLustering and Mixed Integer Programming. International Journal of Industrial Engineering and Management, 9(2), 59-68. google scholar
  • Dündar, A. O., Tekin, M., Peker, K., Şahman, M. A., & KaraoğLan, İ. (2022). A mathematicaL modeL for muLti-period multi-stage muLti-mode multi-product capacitated wheat supply network design probLem and a case study. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(1), 265-281. https://doi.org/10.17341/gazimmfd.837124 google scholar
  • Ghahremani-Nahr, J., Najafi, S. E., & Nozari, H. (2019). A Combined Transportation Model for the Fruit and VegetabLe SuppLY Chain Network. Journal of Optimization in Industrial Engineering, 15(2), 131-145. https://doi.org/10.22094/joie.2022.1948231.1925 google scholar
  • Hajmirfattahtabrizi, M. H., & Song, H. (2019). Investigation of BottLenecks in SuppLY Chain SYstem for Minimizing TotaL Cost bY Integrating Manufacturing ModeLLing Based on MINLP Approach. Applied Sciences, 9(6), 1185. https://doi.org/10.3390/app9061185 google scholar
  • Jahani, H., Abbasi, B., Sheu, J.-B., & KLibi, W. (2024). SuppLY chain network design with financiaL considerations: A comprehensive review. European Journal of Operational Research, 312(3), 799-839. https://doi.org/10.1016/j.ejor.2023.02.033 google scholar
  • KhaLiLi-Fard, A., Sabouhi, F., & Bozorgi-Amiri, A. (2024). Data-driven robust optimization for a sustainabLe steeL suppLY chain network design: Toward the circuLar economY. Computers & IndustriaL Engineering, 195, 110408. https://doi.org/10.1016/j.cie.2024.110408 google scholar
  • Kheirabadi, M., Naderi, B., Arshadikhamseh, A., & Roshanaei, V. (2019). A mixed-integer program and a Lagrangian-based decomposition aLgorithm for the suppLY chain network design with quantitY discount and transportation modes. Expert Systems with Applica-tions, 137, 504-516. https://doi.org/10.1016/j.eswa.2019.07.004 google scholar
  • KızıLkaYa, İ., Kevser, T., OfluoğLu, H., ÖLçücüer, F., DemireL, D. F. (2024). Determining a New Warehouse Location for an ELectricaL Home AppLiances CompanY. In N. M. Durakbasa & M. G. GençYıLmaz (Eds.), IndustriaL Engineering in the IndustrY 4.0 Era (pp. 733-746). Cham, SwitzerLand: Springer Nature. google scholar
  • Kravets, A., Bogachev, V., Egorova, I., & Bogachev, T. (2020). MuLtimodaL Freight Transportation Based on MuLticriteria Optimization bY Time Indicators. Transportation Research Procedia, 54, 243-252. https://doi.org/10.1016/j.trpro.2021.02.070 google scholar
  • Lee, J., Ko, C., & Moon, I. (2024). E-commerce suppLY chain network design using on-demand warehousing sYstem under uncertaintY. International Journal of Production Research, 62(5), 1901-1927. https://doi.org/10.1080/00207543.2022.2128462 google scholar
  • Li, J., Liu, Y., & Yang, G. (2024). Two-stage distributionaLLY robust optimization modeL for a pharmaceuticaL coLd suppLY chain network design probLem. InternationaL Transactions in OperationaL Research, 31, 3459-3493. https://doi.org/10.1111/itor.13267 google scholar
  • Luathep, P., Sumalee, A., Lam, W. H. K., Li, Z.-C., & Lo, H. K. (2011). Global optimization method for mixed transportation network design probLem: A mixed-integer Linear programming approach. Transportation Research Part B: Methodological, 45(5), 808-827. https:// doi.org/10.1016/j.trb.2011.02.002 google scholar
  • MogaLe, D. G., DoLgui, A., KandhwaY, R., Kumara, S. K., & Tiwari, M. K. (2017). A muLti-period inventorY transportation modeL for tacticaL pLanning of food grain suppLY chain. Computers & Industrial Engineering, 110, 379-394. https://doi.org/10.1016/j.cie.2017.06.008 google scholar
  • MogaLe, D. G., Kumar, M., Kumara, S. K., & Tiwari, M. K. (2018). Grain siLo Location-aLLocation probLem with dweLL time for optimization of food grain suppLY chain network. Transportation Research Part E: Logistics and Transportation Review, 111, 40-69. https://doi. org/10.1016/j.tre.2018.01.004 google scholar
  • Qiu, R., Zhang, B., Zhao, W., Tu, R.-F., He, M.-Q., Liao, Q., & Liang, Y.-T. (2024). An integrated MlNLP modeL for muLti-partY coordination in downstream oiL suppLY chain. Petroleum Science, 21(3), 2066-2079. https://doi.org/10.1016/j.petsci.2023.12.008 google scholar
  • SadjadY, H., & Davoudpour, H. (2012). Two-echeLon, muLti-commoditY suppLY chain network design with mode seLection, Lead-times and inventorY costs. Computers & Operations Research, 39(7), 1345-1354. https://doi.org/10.1016/j.cor.2011.08.003 google scholar
  • Yang, L., & Zhou, X. (2017). Optimizing on-time arrivaL probabiLitY and percentiLe traveL time for eLementarY path finding in time-dependent transportation networks: Linear mixed integer programming reformuLations. Transportation Research Part B: Methodological, 96, 68-91. https://doi.org/10.1016/j.trb.2016.11.012 google scholar
  • You, F., & Grossmann, l. E. (2010). lntegrated MuLti-EcheLon SuppLY Chain Design with lnventories Under UncertaintY: MlNLP ModeLs, ComputationaL Strategies. Process Systems Engineering, 56(2), 419-440. https://doi.org/10.1002/aic.12010 google scholar
  • Zhou, N., & Zhang, J. (2023). Optimization Research of Transportation Costs and EfficiencY of MuLtimodaL Transportation SYstem. google scholar
  • European Journal of Operational Research, 299(2), September 2021, 299(2). https://doi.org/10.1109/lCllCS59993.2023.10421588 google scholar
There are 25 citations in total.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section Research Article
Authors

Duygun Fatih Demirel 0000-0001-8284-428X

Afra Alev 0009-0001-4978-1376

Begüm Buse Erturan 0009-0000-7678-2510

Ecenur Bağrıyanık 0009-0002-8587-6329

Eylül Akkaya 0009-0003-9197-0351

Şeyda Zahide Gündoğdu 0009-0006-0035-648X

Submission Date December 27, 2024
Acceptance Date February 12, 2025
Publication Date May 6, 2025
Published in Issue Year 2025 Volume: 10 Issue: 1

Cite

APA Demirel, D. F., Alev, A., Erturan, B. B., … Bağrıyanık, E. (2025). A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company. Journal of Transportation and Logistics, 10(1), 18-33. https://doi.org/10.26650/JTL.2025.1608346
AMA Demirel DF, Alev A, Erturan BB, Bağrıyanık E, Akkaya E, Gündoğdu ŞZ. A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company. JTL. May 2025;10(1):18-33. doi:10.26650/JTL.2025.1608346
Chicago Demirel, Duygun Fatih, Afra Alev, Begüm Buse Erturan, Ecenur Bağrıyanık, Eylül Akkaya, and Şeyda Zahide Gündoğdu. “A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company”. Journal of Transportation and Logistics 10, no. 1 (May 2025): 18-33. https://doi.org/10.26650/JTL.2025.1608346.
EndNote Demirel DF, Alev A, Erturan BB, Bağrıyanık E, Akkaya E, Gündoğdu ŞZ (May 1, 2025) A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company. Journal of Transportation and Logistics 10 1 18–33.
IEEE D. F. Demirel, A. Alev, B. B. Erturan, E. Bağrıyanık, E. Akkaya, and Ş. Z. Gündoğdu, “A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company”, JTL, vol. 10, no. 1, pp. 18–33, 2025, doi: 10.26650/JTL.2025.1608346.
ISNAD Demirel, Duygun Fatih et al. “A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company”. Journal of Transportation and Logistics 10/1 (May2025), 18-33. https://doi.org/10.26650/JTL.2025.1608346.
JAMA Demirel DF, Alev A, Erturan BB, Bağrıyanık E, Akkaya E, Gündoğdu ŞZ. A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company. JTL. 2025;10:18–33.
MLA Demirel, Duygun Fatih et al. “A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company”. Journal of Transportation and Logistics, vol. 10, no. 1, 2025, pp. 18-33, doi:10.26650/JTL.2025.1608346.
Vancouver Demirel DF, Alev A, Erturan BB, Bağrıyanık E, Akkaya E, Gündoğdu ŞZ. A Mixed-Integer Programming Model for Optimizing the Distribution Network of a Packaging Company. JTL. 2025;10(1):18-33.


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