A DEA-based simulation-optimisation approach to design a resilience plasma supply chain network: a case study of the COVID-19 outbreak

Peiman Ghasemi, Fariba Goodarzian, Vladimir Simic, Erfan Babaee Tirkolaee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This study develops a novel multi-objective mathematical model for a Plasma Supply Chain Network (PSCN) in order to maximise the coverage of blood donors during periods and minimise the blood transportation costs between different nodes, relocation cost of temporary mobile facilities, inventory holding cost of the blood, and the costs of newly established blood centres. Therefore, the major contribution of this work is the simultaneous consideration of resiliency and efficiency in the proposed PCN during the COVID-19 outbreak. To address the uncertain parameters, Stochastic Chance-Constrained Programming (SCCP) method is applied to the model. Additionally, to solve the PSCN model, the ϵ-constraint method is employed for small- and medium-sized problems and then a multi-objective invasive weed optimisation (MOIWO) algorithm is implemented for large-sized problems. To validate the suggested methodology, a variety of problem instances is designed and solved using the solution techniques, considering two assessment metrics of Hyper Volume (HV) and Min Ideal Distance (MID). Moreover, a real case study and sensitivity analyses on significant parameters are conducted to configure the optimal network. Eventually, the obtained results are examined and useful decision aids are suggested.
Original languageEnglish
Article number2224105
JournalInternational Journal of Systems Science: Operations and Logistics
Volume10
Issue number1
Early online date10 Jul 2023
DOIs
Publication statusPublished - 31 Dec 2023

Keywords

  • COVID-19 pandemic
  • Plasma supply chain network
  • efficiency
  • resilience
  • simulation-optimisation model
  • stochastic chance-constrained programming

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Management Science and Operations Research
  • Information Systems and Management

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