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

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.

KEYWORDS:

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

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval and competing interests

• - All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

• - This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue and has not self-plagiarism.

• - The authors have no affiliation with any organisation with a direct or indirect financial interest in the subject matter discussed in the manuscript.

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This work was partially supported by the FWF Austrian Science Fund (Peiman Ghasemi) [I 5908-G].

Notes on contributors

Peiman Ghasemi

Peiman Ghasemi is an esteemed researcher and academic affiliated with the Department of Business Decisions and Analytics at the University of Vienna. With a strong passion for research, Peiman focuses on various areas within the field of decision-making and analytics. His research interests encompass a wide range of topics, including soft computing, fuzzy sets and systems, meta-heuristic methods, data envelopment analysis, and applied operations research. Through his work, Peiman explores innovative approaches and methodologies in these domains, aiming to advance the understanding and application of these concepts. He has published over 100 papers in high-quality peer-reviewed international journals and conferences. Previously, Peiman served as a lecturer at the Department of Logistics, Tourism, and Service Management at the German University of Technology in Oman (GUtech) in Muscat, Oman. In this role, he actively contributed to the academic and research endeavors in the field. Peiman completed his PhD in Industrial Engineering from Azad Islamic University South Tehran Branch. His dissertation, titled "Mathematical Modeling for Location-Routing-Inventory Problem Based on Game Theory Considering Pre-Disaster Uncertainty," exemplifies his dedication to addressing real-world challenges through mathematical modeling and incorporating game theory principles. His commitment to academic excellence and his ability to develop innovative solutions contribute to the advancement of these disciplines and have a positive impact on real-world applications.

Fariba Goodarzian

Fariba Goodarzian is a Research Associate at the Heriot-Watt University in Edinburgh/UK. She passed a Postdoctoral Researcher at the University of Tehran, Department of Industrial Engineering, Tehran, Iran and in Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, D.C., United States and also in University of Sevilla/Spain. She earned her Ph.D. degree in Industrial Engineering from Yazd University, Yazd, Iran from 2017 to 2020, M.Sc. degree in Industrial Engineering from the University of Science and Technology of Mazandaran, Behshar from 2014 to 2016, and also she graduated B. Sc. degree in Industrial Engineering from Mazandaran University of Science and Technology, Babol from 2020 to 2014. Her publications are in Applied Soft Computing, Applied Mathematical Modeling, Engineering Applications and Artificial Intelligence, Journal of Computational Design and Engineering, Computers and Industrial Engineering, Applied Intelligence, The International Journal of Advanced Manufacturing Technology, Journal of Ambient Intelligence and Humanized Computing, Environmental Science and Pollution Research, Journal of Cleaner Production, Annals of Operations Research, Soft Computing, Journal of Environmental Management, Socio-Economic Planning Sciences, RAIRO-Operations Research, Sensors, International Journal of Systems Science: Operations & Logistics, International Journal of Logistics Management, etc. and international conferences. Dr. Fariba has several chapter books in Elsevier Publisher. Her main research interests are in the area of Supply Chain Management, Health Care Management, Operations Research, Network Design, Big Data Analytics, Uncertainty Programming, exact methods as well as proposing novel heuristic methods and metaheuristic algorithms.

Vladimir Simic

Vladimir Simic was born in Belgrade, Serbia in 1983. He received a Ph.D. degree in Transportation Engineering from the University of Belgrade, Faculty of Transport and Traffic Engineering, Serbia, in 2014. Since 2020, he is a Professor of the Transport and Traffic Engineering Department at the University of Belgrade, Serbia. He has conducted intensive research on operations research applications in diverse fields of specialization, with a particular focus on developing artificial intelligence systems, advanced hybrid multi-criteria decision-making tools, and real-life large-scale stochastic, fuzzy, interval, full- and semi-infinite programming optimization models. He published more than 130 research papers, including 71 papers in SCI-indexed International Journals (e.g., Experts Systems with Applications, IEEE Transactions of Fuzzy Systems, Engineering Applications of Artificial Intelligence, etc.). Dr. Simic is an Associate Editor of the Journal of Intelligent and Fuzzy Systems and an Editor of PeerJ Computer Science. He served as a guest editor for Engineering Applications of Artificial Intelligence (Elsevier), International Journal of Computational Intelligence Systems (Springer), Sustainability, Computer Modeling in Engineering and Sciences, PeerJ Computer Science, etc. Dr. Simic regularly serves as an ad-hoc reviewer of many top-tier journals. He won the “2016 Excellence in Review Award” from Resources, Conservation and Recycling in 2017.

Erfan Babaee Tirkolaee

Erfan Babaee Tirkolaee obtained a BSc. (2012) and MSc. (2014) in Industrial Engineering from Isfahan University of Technology in Isfahan, Iran. Then, he received a Ph.D. degree (2019) in Industrial Engineering from Mazandaran University of Science and Technology in Babol, Iran. Dr. Erfan Babaee Tirkolaee is currently an assistant professor in the Department of Industrial Engineering at Istinye University in Istanbul, Turkey. He is also an honorary scholar in the Department of Industrial Engineering and Management at Yuan Ze University, Taiwan. Meanwhile, he worked as a Quality Assurance consultant and Training manager in some automotive industries in Iran and could go through different relevant courses like ISO 9001: 2015 and IATF 16949-2016. He has been verified as a scientific elite by the Young Researchers and Elite Club in 2017 and Iran's National Elites Foundation in 2018. He has published more than 80 papers in high-quality journals, including IEEE Transactions on Fuzzy Systems, Expert Systems with Applications, Waste Management, Journal of Cleaner Production, Computers & Industrial Engineering, Annals of Operations Research, etc. His eleven papers have been selected as ESI Highly Cited Papers. He has been serving as a chair/organizing&committe member/keynote speaker in several prestigious international conferences and as a reviewer in many reputed WoS journals such that he has been recognized as a Top Peer Reviewer in 2 of the Essential Science Indicators research areas by Clarivate WoS. Dr. Erfan Babaee Tirkolaee is currently an Associate Editor of Expert Systems with Application (Elsevier), Academic Editor of PLOS One (PLOS), Editorial Board Member of Intelligent Automation & Soft Computing (Tech Science Press), and Editorial Advisory Board member of Management Decision (Emerald). Moreover, he has been serving on the guest editorial board in several journals such as Annals of Operations Research (Springer) and Environmental Science and Pollution Research (Springer). Recently, he has been featured among the “World’s Top 2% Researchers/Scientists in 2021” list identified by Elsevier BV, Stanford University. His main research interests includeWaste Management, Supply Chain Management, Solution Algorithms, Industrial Engineering, Operations Research, Fuzzy Programming, Robust Optimization.