A sustainable medical waste collection and transportation model for pandemics

Tirkolaee, Erfan Babaee; Aydın, Nizamettin

doi:10.1177/0734242x211000437

Cited by 86 publications

(50 citation statements)

References 59 publications

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“…Finally, to demonstrate the efficiency and effectiveness of their model, a case study was provided in Tehran/Iran. Babaee Tirkolaee and Aydın (2021) investigated the sustainable medical waste management problem during the COVID-19 epidemic. Hence, they designed test problems with various sizes and solved them using a CPLEX solver.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

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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 B Angappa Gunasekaren

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Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

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“…A case study is determined in Sari, Iran to show the performance of the proposed model. Tirkolaee and Aydın (2021) suggested a sustainable MWM for collection and transportation for pandemics. They minimized total cost and the total risk exposure imposed by the collection.…”

Section: Survey On Recent Mwcndmentioning

confidence: 99%

Viable medical waste chain network design by considering risk and robustness

Lotfi

Kargar

Gharehbaghi

et al. 2021

Environ Sci Pollut Res

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Medical waste management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up, the amount of MWMs increases day by day. We present medical waste chain network design (MWCND) that contains health center (HC), waste segregation (WS), waste purchase contractor (WPC), and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by conditional value at risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem makes to increase the cost function.

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“…Application of Machine Learning Algorithms in Transportation and Distribution. Vehicle routing problems (VRPs) are categorized as one of the most applicable issues in SCM [75][76][77][78][79][80]. Solving VRPs is one of the high and wellknown applications of ML in SCM.…”

Section: Authorsmentioning

confidence: 99%

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

Tirkolaee

Sadeghi

Mooseloo

et al. 2021

Mathematical Problems in Engineering

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104

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In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

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A sustainable medical waste collection and transportation model for pandemics

Cited by 86 publications

References 59 publications

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

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

Viable medical waste chain network design by considering risk and robustness

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

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