A Two-Warehouse Ordering Policy for Non-Instantaneous Deteriorating Items with two Phase Demand Rates, Two-Tiered Pricing and Shortages Under Trade Credit Policy

B. Babangida, Z. Muazu, M. L. Malumfashi and T. A. Yusuf
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B. Babangida: Department of Mathematics and Statistics, Umaru Musa Yar’adua University, PMB 2025, Katsina Nigeria
Z. Muazu: Department of Mathematics and Statistics, Umaru Musa Yar’adua University, PMB 2025, Katsina Nigeria
M. L. Malumfashi: Department of Mathematics and Statistics, Umaru Musa Yar’adua University, PMB 2025, Katsina Nigeria
T. A. Yusuf: Department of Mathematics and Statistics, Umaru Musa Yar’adua University, PMB 2025, Katsina Nigeria

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 11, 528-552

Abstract: The classical inventory models for non-instantaneous deteriorating items tacitly assumed that the selling price before and after deterioration sets in is the same. However, when items start decaying, the vendor might resolved to decrease the selling price in order to boost additional sales, reduces the cost of holding stock, attracts new clients and reduces lost due to deterioration. In this research, the vendor’s best refill approach for non-instantaneous decaying goods with two-phase demand rates, two-storage facilities, and shortages under an allowable payment delay has been determined. The unit selling price before deterioration sets in is greater than that after deterioration sets. Though a constant consumption rate is considered as soon as deterioration begun, the consumption rate before items starts decaying supposed to be time-dependent quadratic. Shortages are permitted depending on how long it will take before next replenishment. The model determines the best cycle time, optimal order quantity, and optimal time at which the inventory level in the owned ware-house reaches zero in order to increase the overall profit per unit of time. The solution’s existence and uniqueness are both checked by establishing the necessary and sufficient conditions. The model is validated by conducting some numerical experiments, after which sensitivity analysis is carried out which offer some managerial insights.

Date: 2024
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