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

• Published in: Transportation research. Part E, Logistics and transportation review Vol. 156; p. 102517
• Main Authors: Manupati, Vijaya Kumar, Schoenherr, Tobias, Wagner, Stephan M., Soni, Bhanushree, Panigrahi, Suraj, Ramkumar, M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.12.2021
• Subjects: Convalescent plasma; COVID-19; CPLEX optimization; Location-allocation problem; Mixed integer linear programming; NSGA-II; NSGA-III; Plasma supply chain; COVID-19; CPLEX optimization; NSGA-II; Plasma supply chain; NSGA-III; Convalescent plasma; Mixed integer linear programming; Location-allocation problem
• ISSN: 1366-5545; 1878-5794
• DOI: 10.1016/j.tre.2021.102517

•We investigate a location-allocation problem for convalescent plasma banks.•Optimal locations are determined, then collection facilities are allocated.•A MILP model considering transportation time and cost is solved via CPLEX.•Results are validated by a comparison study using NSGA-II and NSGA-III.•The model is implemented within the context of India and results are presented. 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.