Constrained Stochastic Inventory Control Models for Multi-Item with Variable Demand
Asian Journal of Probability and Statistics,
Page 1-15
DOI:
10.9734/ajpas/2022/v20i2415
Abstract
Constrained multi-item inventory system with variable demands are considered. The demand rates of five selected multi-item - Cowbell, Milo, SMA, Cerelac and Golden Morn were modeled as Weibull, Normal and Lognormal probability distributions respectively with the aid of chi-squared multinomial goodness-of-fit test. The respective probability distributions with estimated location parameters: 491.55, 536.92, 10.5, 2.1926 and 5.3103 were used as the basis of probabilistic inventory models to obtain dynamic EOQ for each item under each constraint subject to Kuhn, Karush and Tucker (KKT) conditions as against the use of simple averages in deterministic inventory models. The optimal values of these constraints: available warehouse space (124sq.ft), specified level of inventory (94 units), limited capital (76,671.52 naira) and number of orders (1/month) were obtained using the optimal EOQ values to establish optimal inventory level and constraints level/capacity for each item in order to avoid shortage or excess stock.
Keywords:
- Constrained inventory model
- multi-item
- variable demand
- optimal inventory
- probability distribution
How to Cite
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Available:htpp://dx.doi.org/10.1063/1.4937024
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