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A multi-warehouse partial backlogging inventory model for deteriorating items under inflation when a delay in payment is permissible

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  • Debasis Das
  • Arindam Roy
  • Samarjit Kar

Abstract

In this paper we develop a multi-item multi-warehouse inventory model for deteriorating items for m secondary warehouses (SWs) and one primary warehouse (PW) with displayed stock and price dependent demand under permissible delay in payment. Items are sold from PW which is located at the main market and due to large stock and insufficient space of existing PW, excess items are stored at m SWs of finite capacity. Due to different preserving facilities and storage environment, inventory holding cost is considered to be different in different warehouses. Here the demand of items is a deterministic function of corresponding selling price and the displayed inventory. Shortages are allowed and partially backlogged. The items of SWs are transported to the PW in continuous release pattern and associated transportation cost is proportional to the distance from PW to SWs. Here $$M_{i} (>T_{i}$$ M i ( > T i , cycle time) be the period of permissible delay in settling account for ith item, without the interest charges. But if the retailer settles the account after $$M_{i}$$ M i , he will have to pay with interest per cycle for the inventory not sold after the due date $$M_{i}$$ M i . A single objective inventory problem is solved numerically by developing Genetic algorithm and the maximum average profit and the corresponding optimum decision variables are evaluated. Finally the model is illustrated using a numerical example. A sensitivity analysis of the optimal solution with respect to the parameters of the system is carried out. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Debasis Das & Arindam Roy & Samarjit Kar, 2015. "A multi-warehouse partial backlogging inventory model for deteriorating items under inflation when a delay in payment is permissible," Annals of Operations Research, Springer, vol. 226(1), pages 133-162, March.
  • Handle: RePEc:spr:annopr:v:226:y:2015:i:1:p:133-162:10.1007/s10479-014-1691-6
    DOI: 10.1007/s10479-014-1691-6
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    References listed on IDEAS

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    Cited by:

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    2. Kartick Dey & Debajyoti Chatterjee & Subrata Saha & Ilkyeong Moon, 2019. "Dynamic versus static rebates: an investigation on price, displayed stock level, and rebate-induced demand using a hybrid bat algorithm," Annals of Operations Research, Springer, vol. 279(1), pages 187-219, August.
    3. Wen Zhang & Weizhe Yang, 2021. "Optimal pre-order strategy with delay in payments," Annals of Operations Research, Springer, vol. 305(1), pages 347-374, October.
    4. Jiang Wu & Jinn-Tsair Teng & Konstantina Skouri, 2018. "Optimal inventory policies for deteriorating items with trapezoidal-type demand patterns and maximum lifetimes under upstream and downstream trade credits," Annals of Operations Research, Springer, vol. 264(1), pages 459-476, May.
    5. Shayan Tavakoli & Ata Allah Taleizadeh, 2017. "An EOQ model for decaying item with full advanced payment and conditional discount," Annals of Operations Research, Springer, vol. 259(1), pages 415-436, December.
    6. Sunil Tiwari & Chandra K. Jaggi & Asoke Kumar Bhunia & Ali Akbar Shaikh & Mark Goh, 2017. "Two-warehouse inventory model for non-instantaneous deteriorating items with stock-dependent demand and inflation using particle swarm optimization," Annals of Operations Research, Springer, vol. 254(1), pages 401-423, July.
    7. Ata Allah Taleizadeh & Shayan Tavakoli & Luis Augusto San-José, 2018. "A lot sizing model with advance payment and planned backordering," Annals of Operations Research, Springer, vol. 271(2), pages 1001-1022, December.
    8. Ata Allah Taleizadeh & Sara Tavassoli & Arijit Bhattacharya, 2020. "Inventory ordering policies for mixed sale of products under inspection policy, multiple prepayment, partial trade credit, payments linked to order quantity and full backordering," Annals of Operations Research, Springer, vol. 287(1), pages 403-437, April.

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