A multi-period vaccines supply chain network design with capacity expansion and different replenishment cycles under uncertain demand

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

The World Health Organization (WHO) proposes guidelines for vaccine distribution networks that incorporate economic order quantity and risk pooling principles, specifically through implementing different replenishment cycles between different levels of the distribution network. The substantial impact of different replenishment cycles on inventory levels is demonstrated in the existing literature. However, current optimization network design research has paid limited attention to this aspect. This article aims to address this research gap by investigating a two-stage stochastic programming for a multi-period perishable vaccine network design problem, taking into account the different replenishment cycles characteristic and capacity expansion under uncertain demand conditions. Given the inherent difficulty of solving the proposed problem, a matheuristic approach based on Variable Neighborhood Search (VNS) is developed. To illustrate the practical application and analyze the impact of uncertain demand, we apply the proposed model and its solution method using the Indonesia dataset in the COVID-19 situation. The computational results indicate that lower replenishment cycles leads to higher inventory levels, total costs, and capacity requirements for the top-level distribution center. Optimum replenishment cycles and further managerial implications and future potential research are also presented.

Original languageEnglish
Article number106660
Number of pages19
JournalComputers and Operations Research
Volume167
DOIs
Publication statusPublished - Jul 2024

Keywords

  • Combinatorial optimization
  • Two-stage stochastic programming
  • Vaccine supply chain network
  • Variable Neighborhood Search

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation
  • Management Science and Operations Research

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