A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms

Abstract

With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multi-commodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization Algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable contributions is paying attention to social distancing while receiving the injection and the possibility of the injection during working and non-working hours, and regarding the novelties in the solution methodology, a new heuristic method based on a meta-heuristic algorithm called Modified WOA with VNS (MVWOA) is developed. In terms of the performance metrics and the CPU time, the MOWOA is discovered with a superior performance than other given algorithms. Moreover, regarding the data, a case study related to the COVID-19 pandemic period in Tehran/Iran is provided to validate the proposed algorithm. The outcomes indicate that with the demand increase, the costs increase sharply while the vaccination desirability for students decreases with a slight slope.

Keywords: Artificial intelligence algorithm; Desirability for the vaccination; Distribution-location-allocation; Distributive justice; Simulation model; Stochastic optimization; Vaccine supply chain.

Fig. 1

Configuration of the considered COVID-19 vaccine supply chain network.

Fig. 2

Research framework.

Fig. 3

Interaction structure of COVID-19 prevalence in universities.

Fig. 4

VNS pseudo-code [43].

Fig. 5

The neighborhood structures.

Fig. 6

WOA pseudo-code [46].

Fig. 7

MVWOA pseudo-code.

Fig. 8

Mean S/N ratio plot for each level of the factors.

Fig. 9

Mean RPD plot for each level of the factors.

Fig. 10

CPU time trend.

Fig. 11

The means plot and LSD intervals for the suggested meta-heuristics.

Fig. 12

The derived non-dominant solutions' dispersion behavior by the proposed meta-heuristics in problem M1.

Fig. 13

The case study map in Tehran/Iran.

Fig. 14

Distribution function of the estimated vaccine dose value.

Fig. 15

Comparison of simulation results with real system.

Fig. 16

Simulation model's sensitivity analysis.

Fig. 17

Demand change behavior's effect on three objective functions.

Fig. 18

The trend of demand changes' effect on vaccine shortage amount by dose.