Convalescent plasma bank facility location-allocation problem for COVID-19

Vijaya Kumar Manupati; Tobias Schoenherr; Stephan M Wagner; Bhanushree Soni; Suraj Panigrahi; M Ramkumar

doi:10.1016/j.tre.2021.102517

Convalescent plasma bank facility location-allocation problem for COVID-19

Transp Res E Logist Transp Rev. 2021 Dec:156:102517. doi: 10.1016/j.tre.2021.102517. Epub 2021 Oct 28.

Authors

Vijaya Kumar Manupati¹, Tobias Schoenherr², Stephan M Wagner³, Bhanushree Soni⁴, Suraj Panigrahi¹, M Ramkumar⁵

Affiliations

¹ Department of Mechanical Engineering, National Institute of Technology Warangal, Warangal, Telangana 506004, India.
² Department of Supply Chain Management, Broad College of Business, Michigan State University, 632 Bogue St., East Lansing, MI, USA.
³ Chair of Logistics Management, Department of Management, Technology, and Economics, Swiss Federal Institute of Technology Zurich, Weinbergstrasse 56/58, 8092 Zurich, Switzerland.
⁴ Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Gorimedu, Puducherry 605006, India.
⁵ Operations and Quantitative Methods Group, Indian Institute of Management Raipur, Atal Nagar, Kurru (Abhanpur), Raipur 493 661, India.

Abstract

With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers.

Keywords: COVID-19; CPLEX optimization; Convalescent plasma; Location-allocation problem; Mixed integer linear programming; NSGA-II; NSGA-III; Plasma supply chain.