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Application of particle swarm optimisation for solving deteriorating inventory model with fluctuating demand and controllable deterioration rate

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  • Yu-Ren Chen
  • Chung-Yuan Dye

Abstract

In most of the inventory models in the literature, the deterioration rate of goods is viewed as an exogenous variable, which is not subject to control. In the real market, the retailer can reduce the deterioration rate of product by making effective capital investment in storehouse equipments. In this study, we formulate a deteriorating inventory model with time-varying demand by allowing preservation technology cost as a decision variable in conjunction with replacement policy. The objective is to find the optimal replenishment and preservation technology investment strategies while minimising the total cost over the planning horizon. For any given feasible replenishment scheme, we first prove that the optimal preservation technology investment strategy not only exists but is also unique. Then, a particle swarm optimisation is coded and used to solve the nonlinear programming problem by employing the properties derived from this article. Some numerical examples are used to illustrate the features of the proposed model.

Suggested Citation

  • Yu-Ren Chen & Chung-Yuan Dye, 2013. "Application of particle swarm optimisation for solving deteriorating inventory model with fluctuating demand and controllable deterioration rate," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(6), pages 1026-1039.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:6:p:1026-1039
    DOI: 10.1080/00207721.2011.652224
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    Citations

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    Cited by:

    1. Janssen, Larissa & Claus, Thorsten & Sauer, Jürgen, 2016. "Literature review of deteriorating inventory models by key topics from 2012 to 2015," International Journal of Production Economics, Elsevier, vol. 182(C), pages 86-112.
    2. G. Durga Bhavani & Ieva Meidute-Kavaliauskiene & Ghanshaym S. Mahapatra & Renata Činčikaitė, 2022. "A Sustainable Green Inventory System with Novel Eco-Friendly Demand Incorporating Partial Backlogging under Fuzziness," Sustainability, MDPI, vol. 14(15), pages 1-20, July.
    3. Liu, Aijun & Zhu, Qiuyun & Xu, Lei & Lu, Qiang & Fan, Youqing, 2021. "Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    4. Saha, Subrata & Chatterjee, Debajyoti & Sarkar, Biswajit, 2021. "The ramification of dynamic investment on the promotion and preservation technology for inventory management through a modified flower pollination algorithm," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    5. Hongjun Peng & Tao Pang & Fuliang Cao & Juan Zhao, 2018. "A Mutual Subsidy Mechanism for a Seasonal Product Supply Chain Channel Under Double Price Regulation," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(06), pages 1-26, December.
    6. Saha, Subrata & Sarkar, Biswajit & Sarkar, Mitali, 2023. "Application of improved meta-heuristic algorithms for green preservation technology management to optimize dynamical investments and replenishment strategies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 426-450.

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