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A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network

Author

Listed:
  • Seyed Mohsen Mousavi

    (University of Malaya)

  • Ardeshir Bahreininejad

    (University of Malaya
    Institut Teknologi Brunei)

  • S. Nurmaya Musa

    (University of Malaya)

  • Farazila Yusof

    (University of Malaya)

Abstract

In this study, the design of a two-echelon distribution supply chain network for the seasonal products with multiple vendors (manufacturers) and buyers (retailers), and a set of warehouses for each vendor are considered. The locations of the buyers are known and the capacity of the warehouses is restricted while the buyers purchase different products from the vendors under all unit discount policy. The main objective of this research is to find out the optimal locations of the potential vendors in addition to the quantity ordered (allocation) by the buyers so that the total inventory cost including ordering (transportation), holding and the purchasing costs is minimized. Besides, the distance from the buyers to the vendors is considered as the Euclidean distance. The total budget to buy the products is limited and the production capacity of each vendor is also restricted. To solve the problem, a modified particle swarm optimization (MPSO) algorithm is applied where the results are validated using a genetic algorithm (GA). Finally, some computational examples are generated to assess the algorithms’ performance where MPSO shows a better efficiency in comparison with the GA.

Suggested Citation

  • Seyed Mohsen Mousavi & Ardeshir Bahreininejad & S. Nurmaya Musa & Farazila Yusof, 2017. "A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 191-206, January.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0970-z
    DOI: 10.1007/s10845-014-0970-z
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    References listed on IDEAS

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    Cited by:

    1. Abdulaziz T. Almaktoom, 2017. "Stochastic Reliability Measurement and Design Optimization of an Inventory Management System," Complexity, Hindawi, vol. 2017, pages 1-9, August.
    2. Sasan Harifi & Madjid Khalilian & Javad Mohammadzadeh & Sadoullah Ebrahimnejad, 2021. "Optimization in solving inventory control problem using nature inspired Emperor Penguins Colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1361-1375, June.
    3. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    4. Amir Hossein Nobil & Amir Hosein Afshar Sedigh & Leopoldo Eduardo Cárdenas-Barrón, 2020. "A multiproduct single machine economic production quantity (EPQ) inventory model with discrete delivery order, joint production policy and budget constraints," Annals of Operations Research, Springer, vol. 286(1), pages 265-301, March.
    5. Farheen Naz & Anil Kumar & Abhijit Majumdar & Rohit Agrawal, 2022. "Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research," Operations Management Research, Springer, vol. 15(1), pages 378-398, June.
    6. A. Ghodratnama & H. R. Arbabi & A. Azaron, 2020. "A bi˗objective hub location-allocation model considering congestion," Operational Research, Springer, vol. 20(4), pages 2427-2466, December.
    7. Mohammed Alkahtani, 2022. "Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    8. Avelina Alejo-Reyes & Erik Cuevas & Alma Rodríguez & Abraham Mendoza & Elias Olivares-Benitez, 2020. "An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem," Mathematics, MDPI, vol. 8(9), pages 1-24, August.
    9. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    10. Zhen Wang & Qianwang Deng & Like Zhang & Xiaoyan Liu, 2023. "Integrated scheduling of production, inventory and imperfect maintenance based on mutual feedback of supplier and demander in distributed environment," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3445-3467, December.
    11. Hong, Jiangtao & Diabat, Ali & Panicker, Vinay V. & Rajagopalan, Sridharan, 2018. "A two-stage supply chain problem with fixed costs: An ant colony optimization approach," International Journal of Production Economics, Elsevier, vol. 204(C), pages 214-226.
    12. Anderson Rogério Faia Pinto & Marcelo Seido Nagano, 2020. "Genetic algorithms applied to integration and optimization of billing and picking processes," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 641-659, March.
    13. Sema Akin Bas & Beyza Ahlatcioglu Ozkok, 2020. "A fuzzy approach to multi-objective mixed integer linear programming model for multi-echelon closed-loop supply chain with multi-product multi-time-period," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(1), pages 25-46.
    14. Sina Nayeri & Mahdieh Tavakoli & Mehrab Tanhaeean & Fariborz Jolai, 2022. "A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms," Annals of Operations Research, Springer, vol. 315(2), pages 1895-1935, August.

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