A fuzzy sustainable model for COVID-19 medical waste supply chain network

The COVID-19 has placed pandemic modeling at the forefront of the whole world's public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic. © 2023, Crown.

Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions

The emergency department (ED) is the most important section in every hospital. The ED behaviour is adequately complex, because the ED has several uncertain parameters such as the waiting time of patients or arrival time of patients. To deal with ED complexities, this paper presents a simulation-based optimisation-based meta-model (S-BO-BM-M) to minimise total waiting time of the arriving patients in an emergency department under COVID-19 conditions. A full-factorial design used meta-modelling approach to identify scenarios of systems to estimate an integer nonlinear programming model for the patient waiting time minimisation under COVID-19 conditions. Findings showed that the S-BO-BM-M obtains the new key resources configuration. Simulation-based optimisation meta-modelling approach in this paper is an invaluable contribution to the ED and medical managers for the redesign and evaluates of current situation ED system to reduce waiting time of patients and improve resource distribution in the ED under COVID-19 conditions to improve efficiency. Copyright © 2023 Inderscience Enterprises Ltd.

A bi-level mathematical model for logistic management considering the evolutionary game with environmental feedbacks

Purpose: This paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The developed model has two players including interdictor (COVID-19) and fortifier (government). Accordingly, the aim of the first player (COVID-19) is to maximize system costs and causing further damage to the system. The goal of the second player (government) is to minimize the costs of location, routing and allocation due to budget limitations. Design/methodology/approach: The approach of evolutionary games with environmental feedbacks was used to develop the proposed model. Moreover, the game continues until the desired demand is satisfied. The Lagrangian relaxation method was applied to solve the proposed model. Findings: Empirical results illustrate that with increasing demand, the values of the objective functions of the interdictor and fortifier models have increased. Also, with the raising fixed cost of the established depot, the values of the objective functions of the interdictor and fortifier models have raised. In this regard, the number of established depots in the second scenario (COVID-19 wave) is more than the first scenario (normal COVID-19 conditions). Research limitations/implications: The results of the current research can be useful for hospitals, governments, Disaster Relief Organization, Red Crescent, the Ministry of Health, etc. One of the limitations of the research is the lack of access to accurate information about transportation costs. Moreover, in this study, only the information of drivers and experts about transportation costs has been considered. In order to implement the presented solution approach for the real case study, high RAM and CPU hardware facilities and software facilities are required, which are the limitations of the proposed paper. Originality/value: The main contributions of the current research are considering evolutionary games with environmental feedbacks during the COVID-19 pandemic outbreak and location, routing and allocation of the medical centers to the distribution depots during the COVID-19 outbreak. A real case study is illustrated, where the Lagrangian relaxation method is employed to solve the problem. © 2021, Emerald Publishing Limited.