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Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration

Author

Listed:
  • Magfura Pervin

    (Vidyasagar University)

  • Sankar Kumar Roy

    (Vidyasagar University)

  • Gerhard-Wilhelm Weber

    (Middle East Technical University)

Abstract

In this paper, a deterministic inventory control model with deterioration is developed. Here, the deterioration rate follows stochastic deterioration, especially Weibull distribution deterioration. A time-dependent demand approach is introduced to show the applicability of our proposed model and to be up-to-date with respect to time. The main purpose of the paper is to investigate the optimal retailer’s replenishment decisions for deteriorating items including time-dependent demand for demonstrating more practical circumstances within economic-order quantity frameworks. Keeping in mind the criterion of modern era, we consider that the holding cost is totally dependent on time, and shortages are allowed for this model. Subject to the formulated model, we minimize the total inventory cost. The mathematical model is explored by numerical examples to validate the proposed model. A sensitivity analysis of the optimal solution with regard to important parameters is also carried out to elaborate the quality, e.g., stability, of our result and to possibly modify our model. The paper ends with a conclusion and an outlook to future studies.

Suggested Citation

  • Magfura Pervin & Sankar Kumar Roy & Gerhard-Wilhelm Weber, 2018. "Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration," Annals of Operations Research, Springer, vol. 260(1), pages 437-460, January.
    • Handle: RePEc:spr:annopr:v:260:y:2018:i:1:d:10.1007_s10479-016-2355-5
    DOI: 10.1007/s10479-016-2355-5
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    References listed on IDEAS

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    6. Soumen Kumar Das & Magfura Pervin & Sankar Kumar Roy & Gerhard Wilhelm Weber, 2023. "Multi-objective solid transportation-location problem with variable carbon emission in inventory management: a hybrid approach," Annals of Operations Research, Springer, vol. 324(1), pages 283-309, May.
    7. Asim Paul & Magfura Pervin & Sankar Kumar Roy & Nelson Maculan & Gerhard-Wilhelm Weber, 2022. "A green inventory model with the effect of carbon taxation," Annals of Operations Research, Springer, vol. 309(1), pages 233-248, February.
    8. Kanokporn Kungwalsong & Abraham Mendoza & Vasanth Kamath & Subramanian Pazhani & Jose Antonio Marmolejo-Saucedo, 2022. "An application of interactive fuzzy optimization model for redesigning supply chain for resilience," Annals of Operations Research, Springer, vol. 315(2), pages 1803-1839, August.
    9. Maihami, Reza & Govindan, Kannan & Fattahi, Mohammad, 2019. "The inventory and pricing decisions in a three-echelon supply chain of deteriorating items under probabilistic environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 118-138.
    10. De, Arijit & Choudhary, Alok & Turkay, Metin & Tiwari, Manoj K., 2021. "Bunkering policies for a fuel bunker management problem for liner shipping networks," European Journal of Operational Research, Elsevier, vol. 289(3), pages 927-939.
    11. I. Kudelska & G. Pawłowski, 2020. "Influence of assortment allocation management in the warehouse on the human workload," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(2), pages 779-795, June.
    12. Vishal Kashav & Chandra Prakash Garg & Rupesh Kumar, 2023. "Ranking the strategies to overcome the barriers of the maritime supply chain (MSC) of containerized freight under fuzzy environment," Annals of Operations Research, Springer, vol. 324(1), pages 1223-1268, May.
    13. Md. Abdul Hakim & Ibrahim M. Hezam & Adel Fahad Alrasheedi & Jeonghwan Gwak, 2022. "Pricing Policy in an Inventory Model with Green Level Dependent Demand for a Deteriorating Item," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    14. Sadia Samar Ali & Haripriya Barman & Rajbir Kaur & Hana Tomaskova & Sankar Kumar Roy, 2021. "Multi-Product Multi Echelon Measurements of Perishable Supply Chain: Fuzzy Non-Linear Programming Approach," Mathematics, MDPI, vol. 9(17), pages 1-27, August.
    15. Fei, Liguo & Wang, Yanqing, 2022. "Demand prediction of emergency materials using case-based reasoning extended by the Dempster-Shafer theory," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    16. Bo Yan & Xiaoxu Chen & Qin Yuan & Xiaotai Zhou, 2020. "Sustainability in fresh agricultural product supply chain based on radio frequency identification under an emergency," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(4), pages 1343-1361, December.
    17. Luis A. San-José & Joaquín Sicilia & Manuel González-de-la-Rosa & Jaime Febles-Acosta, 2022. "Profit maximization in an inventory system with time-varying demand, partial backordering and discrete inventory cycle," Annals of Operations Research, Springer, vol. 316(2), pages 763-783, September.
    18. Al-Amin Khan, Md. & Shaikh, Ali Akbar & Konstantaras, Ioannis & Bhunia, Asoke Kumar & Cárdenas-Barrón, Leopoldo Eduardo, 2020. "Inventory models for perishable items with advanced payment, linearly time-dependent holding cost and demand dependent on advertisement and selling price," International Journal of Production Economics, Elsevier, vol. 230(C).
    19. Soheyl Khalilpourazari & Shima Soltanzadeh & Gerhard-Wilhelm Weber & Sankar Kumar Roy, 2020. "Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study," Annals of Operations Research, Springer, vol. 289(1), pages 123-152, June.

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