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Optimizing Raw Parts Procurement and Inventory Under Excess Demand Values and Unknown Prices in Post Pandemic Situations via Fuzzy Programming


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DOI: https://doi.org/10.15866/iremos.v16i5.21552

Abstract


This study uses a fuzzy programming model to optimize part procurement and inventory management under excess demand values and unknown prices. It has been conducted in pandemic and post-pandemic situations for health-care-related companies and non-essential related services, where demand values tend to be surplus and unpredictable. The problem is modeled as mathematical optimization, and the objective function is the expectation value in the fuzzy sense of the total costs considered. The decision is centered on the number of parts for each supplier or those stored in the inventory for future use. Finally, numerical experiments are performed to demonstrate how the problem is solved. The results indicate that the optimal decisions have been derived, and therefore the proposed approach can be adopted by manufacturing/retail/service companies.
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Keywords


Excess Demand; Fuzzy Programming; Post-Pandemic; Procurement; Supply Chain Management

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References


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