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Erschienen in:
2021 | OriginalPaper | Buchkapitel
A Multi-product Stochastic Programming Model for Supplier Selection in a Humanitarian Relief Chain
verfasst von : Juan Luis Mejía Miranda, Jania Astrid Martínez Saucedo
Erschienen in: Computer Science and Health Engineering in Health Services
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Abstract
This paper focuses on the supplier selection in a humanitarian relief chain. Considering the negative impact that natural disasters have in society and their environment, the procurement costs could be a challenging issue for humanitarian organizations (HO). Post-disaster procurement and pre-positioning inventory usually carry large costs due to the haste of the situation in the first or the extensive administration in the latter. This work analyzes supplier selection tools proposed in the literature and focuses primarily on the final decision stage where an organization optimizes the purchase of items from a supplier base.
We propose a scenario-based two-stage stochastic programming model for a statutory agency; it considers a limited budget and the procurement from a higher-level source like the federal government. We also consider framework agreements (FA’s) where the buyer commits to purchase some reserved capacity from suppliers during a given period of time (also called horizon) so that better prices could be negotiated and help the organization lower their procurement costs.
The proposed model delivers an optimal solution for a statutory agency and helps in the decision-making process as to what candidate suppliers are desirable to establish a framework agreement with. The solution shows important information as to what percentage of demand is covered from the selected suppliers.
Limited funding in HO's is a very important factor for decision-making. The model incorporates a budget constraint and delivers information about the amount of relief items to procure form a different source other than the supplier base.