Selecting a set of best stochastic inventory policies measured by opportunity cost

Mohammad H. Almomani; Mahmoud H. Alrefaei; Mohammad H. Almomani; Mahmoud H. Alrefaei

doi:10.3934/math.2023244

AIMS Mathematics

2023, Volume 8, Issue 2: 4892-4906. doi: 10.3934/math.2023244

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Research article

Selecting a set of best stochastic inventory policies measured by opportunity cost

Mohammad H. Almomani ^{1
,
,},
Mahmoud H. Alrefaei ²

1.
Department of Mathematics, Faculty of Science, The Hashemite University, P.O box 330127, Zarqa 13133, Jordan
2.
Department of Mathematics and Statistics, Jordan University of Science and Technology, Jordan

Received: 23 August 2022 Revised: 26 November 2022 Accepted: 02 December 2022 Published: 09 December 2022
MSC : 90C15, 90C47, 49K35, 49K45

In the present article, we consider the stochastic inventory model aiming at selecting a subset of best policies with minimum total cost. When the computational budget is limited, then the optimal computing budget allocation (OCBA) is used for allocating the available computational budget to the different policies in order to correctly select the best policies. To measure the quality of the selection, we use the expected opportunity cost (EOC) approach that measures the total difference means between the actual best policies and the selected policies. The proposed algorithm OCBA based on EOC is implemented on a stochastic inventory example. The results show that the EOC approaches zero as the total number of computational budgets increases and that using the EOC measure gives better results than using the probability of correct selection (PCS). Moreover, the numerical results indicate that the proposed algorithm is robust.
- stochastic inventory model,
- opportunity cost,
- computing budget allocation,
- simulation,
- optimization
Citation: Mohammad H. Almomani, Mahmoud H. Alrefaei. Selecting a set of best stochastic inventory policies measured by opportunity cost[J]. AIMS Mathematics, 2023, 8(2): 4892-4906. doi: 10.3934/math.2023244

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Abstract

In the present article, we consider the stochastic inventory model aiming at selecting a subset of best policies with minimum total cost. When the computational budget is limited, then the optimal computing budget allocation (OCBA) is used for allocating the available computational budget to the different policies in order to correctly select the best policies. To measure the quality of the selection, we use the expected opportunity cost (EOC) approach that measures the total difference means between the actual best policies and the selected policies. The proposed algorithm OCBA based on EOC is implemented on a stochastic inventory example. The results show that the EOC approaches zero as the total number of computational budgets increases and that using the EOC measure gives better results than using the probability of correct selection (PCS). Moreover, the numerical results indicate that the proposed algorithm is robust.

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