Selection strategy of medical waste collection and transportation modes based on simulation and entropy method

Purpose: The aim of the article is to analyze problems related to the logistics of medical waste in the Silesian Municipal Hospital. The research issues focused on the analysis of currently used solutions, the causes of existing problems and proposals for improvements aimed at improving the quality of medical waste management processes in the hospital. The research will focus on analyzing areas related to the collection, storage, transport, and disposal of medical waste. Design/methodology/approach: In the research presented in this article, an analysis of the literature on the processes of the waste management was used. The research focused on analyzing areas related to the collection, storage, transport, and disposal of medical waste. The analysis covered also procedures related to the segregation of medical waste and compliance with applicable regulations. The research focused also on assessing the effectiveness of existing logistics processes in waste management and identifying potential areas requiring improvement. IGrafx program was used for research and modeling. Findings: The research showed some problems related to human resources, waste storage, transportation of medical waste and disinfection of carts and storages. Therefore, carrying out this assessment and analysis of the medical waste logistics management system allowed to propose corrective actions and improvements in the areas that most require improvement and adversely affect the waste logistics process. Research limitations: The research limitation was an access to some data in the hospital. In the future similar research should be carried out in another hospitals in Silesia region. Practical implications: Carrying out this assessment and analysis of the medical waste logistics management system allowed to propose corrective actions and improvements in the areas that most require improvement and adversely affect the waste logistics process. The introduction of the proposed improvements will increase the efficiency of the process and contribute to minimizing the risk of current problems occurring in the future. Originality/value: The article shows the scale of the problem that can occur in many hospitals in Poland. Additionally, the article presents some recommendations for hospital staff, which aimed at minimizing the health risk to patients and staff, as well as improving compliance with standards and procedures set in the hospital.

During the COVID-19 pandemic, disposable masks, protective clothing, gloves, and nasopharyngeal swabs collected by nucleic acid testing formed a large amount of medical waste. Medical waste has strict temporary storage time requirements in hospitals, which need to be transported to medical waste disposal plants within the specified time. However, as most of disposal plants are far away from downtown, they also need to be responsible for the transportation and disposal of medical waste in many hospitals, and put forward higher requirement for transportation routes. Rapid and safe disposal of all types of medical waste generated by COVID-19 is crucial to the prevention and control of the epidemic. This paper designs the transportation route optimization model using Anylogic simulation software based on the regional distribution of 118 tertiary hospitals and 2 large medical waste disposal plants in Beijing, China. At the same time, transportation routes of 118 tertiary hospitals in the morning peak, evening peak, all-day, and ordinary periods were simulated based on the Beijing traffic index in 2017. On this basis, through the analysis of the simulation data, the selection of medical waste transport routes for 118 tertiary hospitals in the morning peak, evening peak, all day, and ordinary periods is further clarified, so as to ensure that medical waste can be transported to the medical waste disposal plant in the shortest time. The shortest path and fastest speed transport mode, medical waste transport data set, and the selection of transport mode of 118 tertiary hospitals formed by this research provide certain reference experience for the rapid and safe transport of medical waste during the epidemic period, and also provides corresponding data support for medical waste transportation management in the post-epidemic era and medical waste transportation decision-making when facing major public health problems.

Aim. To identify occupational risks of bloodborne infections among healthcare workers in order to develop effective risk management strategies. Materials and methods. Investigation reports and incident records were analyzed for 3,256 occupational exposure events involving healthcare workers in Sverdlovsk Region from 2013 to 2023. Results. From 2013 to 2023, the incidence rate of blood exposure incidents among healthcare workers was 6.6 per 1,000 (95% CI: 6.1– 7.1). The risk varied depending on the type of medical department, the nature of medical procedures, job role, length of professional experience, day of the week, and time of day. The highest risk of occupational infection was observed among surgeons, traumatologists, obstetrician-gynecologists, anesthesiologists-resuscitators, nurses performing invasive procedures, and staff involved in medical waste collection and transportation. Incidents most commonly occurred during surgeries, injections, and waste handling. The most frequent injury type was hand needlestick injury (81.6%), with 72.3% caused by injection needles. On average, 44.1% of incidents posed a moderate risk of HIV or viral hepatitis transmission, and 9.2% carried a high risk. Post-exposure antiretroviral prophylaxis for HIV was provided to 91.0% of those who required it. Of the healthcare workers, those at the highest risk of contracting blood-borne infections were surgeons and traumatologists, obstetricians and gynecologists, anesthesiologists and resuscitators, nurses performing invasive procedures, and employees involved in the collection and transportation of medical waste. Emergencies most often occurred during surgical interventions, injections, and when working with medical waste. By the nature of the traumatic factor, hand pricks were predominant (81.6%), including 72.3% with injection needles. On average, 44.1% of emergency situations were associated with a moderate risk of HIV and viral hepatitis infection, and 9.2% with a high risk. Coverage of post-exposure antiretroviral therapy for HIV infection among all employees who needed it was 91.0%. Conclusion. The analysis of blood exposure incidents is a critical tool for monitoring and managing occupational risks of bloodborne infections among healthcare workers. A comprehensive, multifaceted strategy is required to reduce the risk of injuries and infections.

This study investigates future medical waste generation and greenhouse gas emissions resulting from medical waste collection and transportation activities in the city of Kocaeli, Turkey, between the years of 2020 and 2040. Population predictions were carried out by using Ilbank method, arithmetic method and United Nations Development (UNDP) method. Then, medical waste amounts were calculated based on the predicted populations. Among the three methods, arithmetic method was seemed to reflect the most reliable prediction. GHG emissions were calculated based on the fuel consumed by medical waste collection vehicles. The targeted GHGs were CH4 and CO2. Results of this work showed that the total medical waste generated per year was predicted to increase from 2,978 tons in 2020 to 5,203 tons in 2040. Finally, it was determined in this study that medical waste collection and transport would generate a total GHG emission of 11,229-ton CO2-e from 2020 to 2040. Furthermore, average global warming factor (GWF) was calculated as 0.00013 tCO2-e per ton of medical waste collected and transported. This study showed that medical waste collection and transportation generates significant amounts of GHG emissions. Results of this study will guide the local authorities when they plan to replace diesel fuel with alternative fuel types.

Battery Electric Vehicles (BEVs) are considered to have higher energy efficiency and advantages to better control CO2 emissions compared to Internal Combustion Engine Vehicles (ICEVs). However, in the context that a large amount of thermal power is still used in developing countries, the CO2 emission reduction effectiveness of BEVs can be weakened or even counterproductive. To reveal the impact of the electricity generation mix on carbon emissions from vehicles, this paper compares the life cycle carbon emissions of BEVs with ICEVs considering the regional disparity of electricity generation mix in China. According to Life Cycle Assessment (LCA) analysis and regional electricity carbon intensity, this study demonstrates that BEVs in the region with high penetration of thermal power produce more CO2 emissions, while BEVs in the region with higher penetration of renewable energy have better environmental performance in carbon emission reduction. For instance, in the region with over 50% penetration of renewable energy, a BEV can reduce more CO2 (18.32 t) compared to an ICEV. Therefore, the regions with high carbon emissions from vehicles need to increase the proportion of renewable generation as a priority rather than promoting BEVs.

COVID-19 medical waste transportation risk evaluation integrating type-2 fuzzy total interpretive structural modeling and Bayesian network

• We model policy options to accelerate passenger Battery EV diffusion in Australia. • Meeting recharger deployment targets as fleet grows is crucial to reach 100% uptake. • Subsidising BEV purchase brings forward price-parity tipping point. • Recharger and vehicle rebate policies costing AUD37.4 billion speed up transition. • Emissions reduction is best when policy introduced at earliest possible date. Transitioning to passenger battery electric vehicles (BEV) can mitigate climate change impacts of road transportation. We develop a novel BEV policy model, nesting it within a national-scale macroeconomic system dynamics model ( iSDG-Australia ) to simulate a suite of policy pathways. We model combinations of infrastructure support and subsidies, which bring forward the price-parity tipping point, thus rapidly accelerating BEVs’ share of new car sales. However, ongoing complementary charging infrastructure investment is critical to reach 100% new BEV car sales by 2050 in Australia. Even with a rapid transition, the modelled fleet would not achieve net-zero greenhouse gas emissions by 2050 due to vehicle longevity; and suddenly ceasing financial incentives could retard BEV sales by a decade. Based on our assumptions, results suggest emissions reductions are maximised by the fastest transition of the passenger vehicle fleet to BEVs, entailing government policy support from 2020 to 2050, for both adequate infrastructure deployment (AUD17.9b) and vehicle rebates (AUD19.5b), which achieves earlier BEV price-parity with fossil-fuelled vehicles.

The hazardous and toxic (B3) medical must be managing carefully. The B3 medical waste management is a series of operations, including the collection, storing, transporting, processing, recycling or disposal, and monitoring of B3 waste and the disposal of the products of such processing. The process is very important because several categories are dangerous and toxic. Moreover, Solid medical waste can contain infections, sharp objects, drugs, cytotoxic, chemical, radioactive, pressurized containers, and have a high heavy metal content. This paper is aim to recommend the B3 medical waste transportation system for primary health care service (PHCS) and institutions related. The good of management and effective transportation are needed because it cannot endanger the increasingly of the crowded population. The data were collected in 11 of PHCS using survey and questionnaire. The point survey was processed by Google Earth/Maps and analyzed using ArcGIS. The statistic method was used to analyze the questionnaires which collected from 35 of PHCS. Based on the results, the mean value is between 4 and 3.9. It is represented that 11 PHCS have worked using waste transportation properly. Some of the PHCS was using the third party for managing the waste medical. Hence, more 37% from 13 PHCS transporting the material to the last pool of waste every day. Whereas the other (46% from 16 PHCS) carried out once a month, and 8 PHCS (23%) done twice in a month. Only 1 PHCS (3%) transported once a week. Therefore, we recommended the 13 best route for medical waste transportation using network analysis.

Purpose: The aim of the article is to provide objective information about the classification of medical waste and its storage and transport based on the Silesian waste transport company – Ekomed. Generally, waste is very marginalized by society, therefore the article aims to present the further way of dealing with medical waste. Design/methodology/approach: The basic research methods were the analysis of literature on the safety of transport of medical waste, along with their categorization based on the BDO system, by example of Silesian waste transport company – EKOMED. Findings: Disposing of medical or chemical waste is a unique responsibility. Leaving such a sensitive procedure to the wrong companies or people who do not have adequate technical and personal resources may not only have dangerous consequences, but also be severely punished by applicable law. Therefore, it is worth trusting certified companies that not only have many years of experience, but also perfectly understand the responsibility of waste producers. Research limitations/implications: In the future, research will be continued in the field of medical waste transport in context of safety, especially during pandemic. Practical implications: This article provides an overview of quantitative research and diversity of medical waste from medical facilities in Poland. This review shows the importance and scale of the problem and allows for further continuation of research to manage the generated waste. The author has also noted that people generally forget about waste when they get into the bin. The main aim is to get people’s attention what is happening later with the medical waste. Originality/value: Based on empirical research, the article shows the scale of the problem that, through the new EU directives on the recycling and sustainable development, will be a forward- looking and important issue. Additionally, the article presents concepts for medical waste transport based on a Silesian company EKOMED. It’s twenty years of daily practice of transporting medical waste to the place of its disposal allowed the development of effective logistic solutions for its safe transport, which turned out to be of invaluable value during the SARS-COV-2 virus pandemic. Keywords: medical waste, transport, safety. Category of the paper: Research paper.

During the pandemic, the amount of infectious medical waste has increased dramatically. Currently, the medical waste recycling process generally suffers from defects such as long distances, high costs, and a lack of emergency response mechanisms. This paper addresses the problem of medical waste collection and route optimization for regions with multiple vehicle types and stages. It comprehensively considers factors such as transportation costs, distance, vehicle allocation, and contamination risks during the collection and distribution of medical waste. The goal is to minimize transportation costs and risks, with constraints including uniqueness, connectivity between nodes, and vehicle load capacity. A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. Given the NP-hard nature of the problem, a location allocation method based on minimum envelope clustering analysis is employed, and an improved NSGA-II algorithm incorporating a fast non-dominated sorting mechanism is designed to obtain Pareto optimal solutions. Comparing with the results of traditional genetic algorithms through simulation, the results show that using the improved NSGA-II to solve practical problems: 1. When the production of medical waste is flat (1 disposal center, 4 backup transfer points, 58 producing points), the total cost is reduced by 13.94%, the total mileage is reduced by 7.17%, the full load rate is increased by 6.14%, and the convergence time is 26 seconds. 2. When the production of medical waste increased significantly (1 disposal center, multiple backup transfer points, 58 producing points), the total cost, total mileage, and transportation risk were reduced by 9.50%, 10.35%, and 2.03%, respectively, and the full load rate increased by 5.98%. The final results also indicate that compared to the results obtained by traditional genetic algorithms, the improved NSGA-II algorithm performs better in solving the optimization problem of infectious medical waste transportation routes.

During the pandemic, the amount of infectious medical waste has increased dramatically. Currently, the medical waste recycling process generally suffers from defects such as long distances, high costs, and a lack of emergency response mechanisms. This paper addresses the problem of medical waste collection and route optimization for regions with multiple vehicle types and stages. It comprehensively considers factors such as transportation costs, distance, vehicle allocation, and contamination risks during the collection and distribution of medical waste. The goal is to minimize transportation costs and risks, with constraints including uniqueness, connectivity between nodes, and vehicle load capacity. A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. Given the NP-hard nature of the problem, a location allocation method based on minimum envelope clustering analysis is employed, and an improved NSGA-II algorithm incorporating a fast non-dominated sorting mechanism is designed to obtain Pareto optimal solutions. Comparing with the results of traditional genetic algorithms through simulation, the results show that using the improved NSGA-II to solve practical problems: 1. When the production of medical waste is flat (1 disposal center, 4 backup transfer points, 58 producing points), the total cost is reduced by 13.94%, the total mileage is reduced by 7.17%, the full load rate is increased by 6.14%, and the convergence time is 26 seconds. 2. When the production of medical waste increased significantly (1 disposal center, multiple backup transfer points, 58 producing points), the total cost, total mileage, and transportation risk were reduced by 9.50%, 10.35%, and 2.03%, respectively, and the full load rate increased by 5.98%. The final results also indicate that compared to the results obtained by traditional genetic algorithms, the improved NSGA-II algorithm performs better in solving the optimization problem of infectious medical waste transportation routes.

BackgroundMedical waste should be collected, classified, and transported to the treatment plant within 48 h. If it is not disposed of in time, it will cause cross-infection, increasing the risk of disease transmission and environmental pollution. How to reasonably plan transportation routes to ensure that the medical waste can be transported to the treatment plant in time is very important.ObjectiveThere are usually two modes of transportation, the fastest speed and shortest path, how to reasonably plan the transportation scheme so that medical waste can be transported to the treatment plant for disposal in the specified time is the main purpose of this article.MethodsThe multi-agent modeling method is adopted. AnyLogic simulation software is used to model the transportation routes of 118 Grade III hospitals and 2 treatment plants in Beijing under the two transportation modes of fastest speed and shortest path.ResultsBased on the traffic index in Beijing, the speed range of 20 km/h-32 km/h is set up and divided into 4 parts and 24 levels with 0.5 km/h as the unit, and the 24 levels of medical waste transportation data set is formed. The key speed nodes of 21 km/h, 24 km/h and 29.5 km/h are identified.ConclusionsThe medical waste transportation model and transport data set formed in this paper have enriched the theory and data basis of medical waste transportation management. The key speed nodes of transportation model selection have important practical significance for the transportation management decision of medical waste in big cities.

Medical waste management is crucial in densely populated urban areas of developing nations. The disposal of biohazardous medical waste requires strict monitoring due to potential environmental and public health risks. In developing countries, several constraints present as challenges to medical waste disposal, including inaccessible biohazardous disposal plants, limited in-facility biohazardous waste storage, government regulation, and cost. Surabaya, Indonesia’s second-largest city, experiences these challenges. Currently, the Surabaya Health Department (SHD) relies on third-party waste processing vendors to handle infectious waste from 63 health facilities due to hazardous waste disposal only being permitted at the provincial level. In addition, waste collection occurs monthly for most health facilities, with a regulated 14-day storage period to prevent accumulation which contradicts the minimum 25-kg threshold that third-party vendors implement. This study utilizes Surabaya’s context to develop an effective medical waste disposal and transportation strategy and logistics using the Periodic Vehicle Routing Problem (PVRP). Results indicate that the 14-day storage requirement benefits SHD and vendors, improving operational efficiency and mitigating risks. Compliance with storage regulations reduces travel distances compared to scenarios without storage requirements. This study’s methodology applies to developing countries exhibiting similar constraints and acts as a guideline to develop similar medical waste disposal strategies.

There has been a dramatic increase in medical waste due to rapid urbanisation and population growth, especially the coronavirus disease 2019 outbreak. Inadequate medical waste collection and transportation will negatively impact sustainable development. Consequently, the government is confronted with a substantial challenge, and it is an urgent issue that must be resolved. This paper provides a comprehensive examination of the medical waste vehicle routing problem through a search on the Web of Science and it commences with a descriptive analysis, followed by a summary of objective functions from two perspectives: single and bi/multi-objective functions. Following that, it presents and highlights practical strategies for the medical waste vehicle routing problem, which may aid in preventing the occurrence of major disasters in the future. Subsequently, the algorithms employed are summarised. Finally, a discussion of the findings and future research directions is presented. The result revealed that sustainable multi-objective functions, the adoption of various collection and transportation strategies, and the utilisation of approximate and hybrid algorithms need to be explored further. This paper aims to provide a roadmap for decision-makers in the domain of medical waste collection and transportation.

A cost-effective solution with less human involvement must be developed for medical waste (MW) transportation. A learning-based coordinated unmanned aerial vehicle–unmanned ground vehicle (UAV–UGV) (CUU) framework, currently unavoidable use, with a transfer learning algorithm is suggested. A transfer learning algorithm is implemented for collision-free optimal path planning. In the framework, mobile ground robots collect medical waste from waste disposal centers through the pick-and-place technique. Then, networked drones lift the collected medical waste and fly through a predefined optimal trajectory. The framework considers the dynamic behavior of the environment and explores the actions for picking, placing, and dropping medical waste. A deep reinforcement learning mechanism has been incorporated for each successful or unsuccessful action by the framework to provide the rewards. With optimal policies, the coordinated UAV and UGV change their actions in dynamic conditions. An optimal cost of transportation of medical waste by the proposed framework is created by considering the weight of MW packets as the payload capacity of a CUU framework, the cost of steering the UAV and UGV, and the time required to transport the MW. The effectiveness of the CUU framework for MW transportation has been tested using MATLAB. The MW transportation data have been encrypted using an encryption key for security and authenticity.