Multi-purpose reverse logistics network design for medical waste management in a megacity: Istanbul, Turkey

Abstract

In the study, a multi-purpose reverse logistics network has been designed to create effectual management of medical waste (MW) generated in 39 districts of Istanbul, a heavily populated city, during the COVID-19 pandemic as well as that to be generated in the next decade. With the model, the medical waste management system in Istanbul is analyzed during the pandemic and for the next 10 years. The model attempts to integrate economic, environmental, and social objectives within the sustainable development goals. It aims to maximize the number of personnel and government earnings for the estimated MW of a megacity while minimizing the total fixed cost and the cost of carbon emissions and transportation. The results indicated that the existing facilities are sufficient for the treatment and disposal of MW generated even under pandemic conditions. However, the capacity of the sterilization facility could be insufficient to treat the estimated amount of MW in the next decade. Opening a sterilization facility near the sanitary landfill in Komurcuoda with a total management cost of 62,450,332 €/year would be an optimum solution for Istanbul MW. In comparison to the single-purpose model results, the multi-purpose model resulted in approximately 42,000 € more in total cost. Sensitivity analyses show that the amount of MW has the most significant effect on the total cost. This simple model created an effective MW management proposal for Istanbul, which can be a model for megacities. Supplementary Information The online version contains supplementary material available at 10.1007/s10669-022-09873-z.

The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW)

“…An RSC network formulated by Balci et al (2022) for medical waste during the pandemic in the metropolitan region of Istanbul, Turkey. In addition to finding the optimum level of cost for the network, the model helps the government with human resource and financial decision-making.…”

Resilience and lessons learned from COVID-19 emergency response

“…In describing the ModelOps technology, Lefevre et al review data analytics and emergency response for bushfire management in particular, but further unpack and describe how artificial intelligence models like ModelOps can be demystified from a ‘black box’ status and toward a more transparent set of operations and features that are approachable and useful for emergency responders. Inspired by COVID challenges, Balci et al ( 2022 ) assesses multi-purpose logistic networks for medical waste in megacities like Istanbul, Türkiye. Using their model, Balci et al are able to evaluate medical waste management throughput under a range of crisis or stochastic heavy use conditions—ensuring the safe operation of critical sanitation infrastructure.…”