Designing a new sustainable Test Kit supply chain network utilizing Internet of Things.

The advent of COVID-19 put much economic pressure on countries worldwide, especially low-income countries. Providing test kits for Covid-19 posed a huge challenge at the beginning of the pandemic. Especially the low-income and less developed countries that did not have the technology to produce this kit and had to import it into the country, which itself cost a lot to buy and distribute these kits. This paper proposes a sustainable COVID-19 test kits supply chain network (STKSCN) for the first time to fill this gap. Distribution and transportation of test kits, location of distribution centers, and management of used test kits are considered in this network. A mixed integer linear programming Multi-Objective (MO), multi-period, multi-resource mathematical model is extended for the proposed supply chain. Another contribution is designing a platform based on the Internet of Things (IoT) to increase the speed, accuracy and security of the network. In this way, patients set their appointment online by registering their personal details and clinical symptoms. An augmented ɛ-constraint2 (AUGMECON2) is proposed for solving small and medium size of problem. Also, two meta-heuristic algorithms, namely NSGA-II and PESA-II are presented to solve the small, medium and large size of the problem. Taguchi method is utilized to control the parameters, and for comparison between meta-heuristic, five performance metrics are suggested. In addition, a case study in Iran is presented to validate the proposed model. Finally, the results show that PESA-II is more efficient and has better performance than the others based on assessment metrics and computational time.

An integrated sustainable medical supply chain network during COVID-19.

Nowadays, in the pharmaceutical industry, a growing concern with sustainability has become a strict consideration during the COVID-19 pandemic. There is a lack of good mathematical models in the field. In this research, a production-distribution-inventory-allocation-location problem in the sustainable medical supply chain network is designed to fill this gap. Also, the distribution of medicines related to COVID-19 patients and the periods of production and delivery of medicine according to the perishability of some medicines are considered. In the model, a multi-objective, multi-level, multi-product, and multi-period problem for a sustainable medical supply chain network is designed. Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model. Response surface method is used to tune the parameters since meta-heuristic algorithms are sensitive to input parameters. Six assessment metrics were used to assess the quality of the obtained Pareto frontier by the meta-heuristic algorithms on the considered problems. A real case study is used and empirical results indicate the superiority of the hybrid fish swarm algorithm with variable neighborhood search.

A sustainable-resilience healthcare network for handling COVID-19 pandemic.

In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching-learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between - 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health. Supplementary Information: The online version contains supplementary material available at 10.1007/s10479-021-04238-2.