ABSTRACT
BACKGROUND AND OBJECTIVES: Good health and a safe environment are essential for sustainable development, including the appropriate management of healthcare wastes. The study intends to assess the generation rate and management methods of healthcare waste in the regional hospital center and a private clinic in Tangier, Morocco, with a focus on potential risks to health workers from infectious diseases. METHODS: The study collected data on healthcare waste generation over a period of two months by measuring and analyzing general and hazardous waste using an electronic scale. The data was presented as averages in kilograms per bed per day and as percentages. A questionnaire was provided to 100 healthcare workers. It included questions on their sociodemographic characteristics and their knowledge and attitudes regarding healthcare waste management. FINDINGS: The case study revealed that the healthcare waste production in the two institutions varied, with the private clinic producing 0.76 kilograms per day per bed and the regional hospital center producing 1.84 kilograms per day per bed. The survey also discovered that the hazardous fraction of waste generated in the regional hospital center was 40 percent, which was much higher than the World Health Organization's estimation. The daily amount of hazardous waste generated increased from 260.49 kilograms to 436.81 kilograms postCOVID-19. The survey found gaps in knowledge, attitudes, and daily challenges in waste management practices among the health workers in both facilities. CONCLUSION: The survey findings suggest that the healthcare waste management methods in Tangier are unsafe and may endanger the health workers and patients. The study found that the lack of monitoring and control contributed significantly to noncompliance with good practices. These findings can be used by the regional divisions of the Ministry of Health to develop specific protocols for managing sanitary emergencies and perform routine observation and training at all levels in the two facilities studied © This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
ABSTRACT
The process of collecting and transporting hazardous medical waste poses a potential threat to the environment and public safety. Furthermore, the waste management system faces higher transportation costs due to the increasing human activities related to rapid population growth. The absence of an efficient and safe logistics network for the timely collection and transportation of hazardous wastes may have negative effects on the environment and public health. Therefore, more sustainable transportation of hazardous waste services is a necessity This paper attempts to design a sustainable network for hazardous medical waste collection services during the COVID-19 pandemic. An electric medical waste collection vehicle routing problem is introduced to construct optimal routes and rosters for a fleet of electric vehicles as well as cover their choice of charging technologies, times and locations. This problem allows us to minimize the health risk of hazardous medical waste while providing cost-effective, zero-emission waste management logistics. Therefore, this problem covers environmental and economic objectives to achieve sustainable development. An effective heuristic that covers adaptive large neighbourhood search and a local search is designed to deal with the complex problem. A series of extensive computational experiments is carried out using real-life benchmark instances to assess the performance of the algorithm. A sensitivity analysis is also conducted to investigate the effect of multiple charger types on the cost and risk objectives. The experiment results indicate that mixed-use of different charger types can reduce the total energy cost and transport risk compared to the case of using only a single charger.
ABSTRACT
The medical waste generated during the epidemic is highly infectious. If it is not handled promptly and properly, it will have a bad effect. Under the constraints of limited time and funds, how to effectively and timely deal with the hazardous medical waste generated by epidemic patients is an issue of public concern. Reasonable network design of hazardous medical waste management system under emergency conditions is the key to solving this problem. However, so far, there are few related studies. This article considers factors such as multiple time periods, multiple types of medical waste, treatment technologies, and recycling. This research aims to explore issues such as the location of medical waste treatment facilities and whether they are operating in each time period, the distribution and transportation of various medical wastes, the selection of medical waste treatment technologies, and the processing capabilities of network nodes. With the goal of minimizing total economic cost and total risk, a multiobjective mixed integer programming model is constructed to obtain the optimal hazardous medical waste management system network. Take the management of hazardous medical waste during the COVID-19 in Wuhan as an example. The linear weighted summation method, augmented ε constraint method and augmented weighted Tchebycheff method are used to obtain high-quality non-dominated solutions. The numerical performance is compared and the validity and feasibility of the model are verified. The experimental results show that the augmented ε constraint method can obtain non-dominated solutions with more uniform distribution. A reasonable network design of the hazardous medical waste management system can well balance the total economic cost and the total risk. Decision makers can effectively control the total economic cost and total risk in the process of medical waste management by adjusting parameters such as weighting factors and adjusting the processing capacity and recovery rate of network nodes such as processing centers and recycling centers. The first part considers the network design of hazardous medical waste management system under emergency conditions, including the location of facilities, the transportation of hazardous medical waste, the selection of treatment technology and recycling. The network nodes involved mainly include: hospitals, temporary and existing hazardous medical waste treatment centers, recycling centers, and garbage disposal centers. The main problems to be solved in this paper are: The transportation and treatment of various types of hazardous medical waste generated by hospitals in a short period of time with a significant increase in quantity;In order to deal with a large amount of hazardous medical waste in time, the location of temporary treatment center, recycling center and garbage disposal center involved;Opening and operating the temporary processing center and recycling center in each time period;When the temporary processing center and recycling center are opened, the technical issues that need to be handled;The processing capacity of network nodes and the impact of factors such as recycling on the management of hazardous medical waste and other issues. Two goals were considered: One is to minimize the total cost, including the fixed open cost of each network node, operating cost, hazardous medical waste treatment cost and transportation cost, etc.;The second is to minimize the total risks, including the risks arising from the handling and transportation of hazardous medical waste. The two goals are in conflict with each other. This article uses a multiobjective optimization method to balance the two goals, and finally builds a multi-objective mixed integer programming model. In the second part, it is difficult to clearly distinguish the relative importance of multiple goals when considering the decisionmaker′ s decision-making. Therefore, a representative set of non-dominant solutions needs to be required for decision-makers to make decisions based on personal preference and actual management issues. It is different from the weighted summation method used in most literature. In this paper, both the augmented weighted Tchebycheff method and the augmented ε constraint method are used to solve the multi-objective mixed integer programming model constructed in this paper, and finally the Pareto optimal solution is given. In the third part, the management of hazardous medical waste generated during the period of COVID-19 in Wuhan, China was taken as an example to verify the feasibility and effectiveness of the model in practice, and compared the numerical performance of three multi-objective optimization methods. According to the model in this paper, it is found through comparison that the augmented ε constraint method is better than the other two methods in terms of calculation time and uniformity of solution distribution. The results of numerical examples show that decision-makers can effectively control the total economic cost and total risk of the hazardous medical waste management system by adjusting the weighting factors, network node processing capacity ratios and recovery rates. When dealing with the problem of hazardous medical waste generated by a highly contagious epidemic (such as COVID-19), risk factors can be considered first, followed by cost. Adjust the weight of the objective function according to the specific problem, and then solve the problem of hazardous medical waste management more rationally and efficiently. Finally, it is hoped that the model in this article can solve the actual hazardous medical waste management problems, and the uncertain issues involved in the hazardous medical waste treatment process will be further studied in the future. © 2022, Journal of Industrial Engineering/ Engineering Management. All Rights Reserved.