Optimization and inventory management under stochastic demand using metaheuristic algorithm

This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective gre...

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Bibliographic Details
Published in:PloS one Vol. 19; no. 1; p. e0286433
Main Authors: Tan, Nguyen Duy, Kim, Hwan-Seong, Long, Le Ngoc Bao, Nguyen, Duy Anh, You, Sam-Sang
Format: Journal Article
Language:English
Published: United States Public Library of Science 05.01.2024
Public Library of Science (PLoS)
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ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective grey wolf optimization (MOGWO) method, reducing storage space while maximizing profit. Numerical outcomes are used to confirm the efficacy of the optimal solutions. The numerical analysis and tests for multi-objective inventory optimization are performed in the four practical scenarios. The inventory model’s sensitivity analysis is performed to verify the optimal solutions further. Especially the proposed approach allows businesses to optimize profits while regulating the storage space required to operate in inventory management. The supply chain performance can be significantly enhanced using inventory management strategies and inventory management practices. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility.
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ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0286433