A bi-objective robust optimization model for hazardous hospital waste collection and disposal network design problem

Under intense industry competition, decision makers must ensure that products demanded by consumers can be quickly produced with minimum production cost. However, because uncertainties are unavoidable and inevitably affect decision makers, numerous studies have discussed how to control uncertainties or minimize their effects. Multiple uncertainties that interact simultaneously may cause a combined effect in actual systems. Therefore, this study presents a robust optimization model with two uncertainties, extending the method of robust optimization with one uncertainty. To demonstrate the applicability of the proposed model with two uncertainties, this study uses the supplier selection problem with component purchase quantity allocation in supply chain management as an analysis case. This considers the reliability of production and transportation and develops a multi-objective robust optimization model with two uncertainties. In addition, a nondominated sorting genetic algorithm is proposed for solving the proposed multi-objective robust optimization model. The relationship between price of robustness and budget parameters is explored by considering the robust optimization model with production and transportation uncertainties proposed in this study. Finally, there is a comparative analysis between the results for price of robustness in the proposed two-uncertainty model and in the one-uncertainty model.

PurposeThis study aims to research the influence of non-probabilistic design variables on interval robust optimization of electric multiple units (EMU) brake module, therefore obtain the reasonable of design variables of the EMU brake module.Design/methodology/approachA robust optimization model of the EMU brake module based on interval analysis is established. This model also considers the dimension tolerance of design variables, and it uses symmetric tolerance to describe the uncertainty of design variables. The interval order relation and possibility degree of interval number are employed to deal with the uncertainty of objective function and constraint condition, respectively. On this basis, a multiobjective robust optimization model in view of interval analysis is established and applied to the robust optimization of the EMU brake module.FindingsCompared with the traditional method and the method proposed in the reference, the maximum stress fluctuation of the EMU brake module structure is smaller after using the method proposed in this paper, which indicates that the robustness of the maximum stress of the structure has been improved. In addition, the weight and strength of the structure meet the design requirements. It shows that this method and model introduced in this research have certain feasibility.Originality/valueThis study is the first attempt to apply the robust optimization model based on interval analysis to the optimization of EMU structure and obtain the optimal solution set that meets the design requirements. Therefore, this study provides an idea for nonprobabilistic robust optimization of the EMU structure.

Community health centers are the frontliners of healthcare services which in its activities produces medical waste and non-medical waste both in solid and liquid form. This waste needs to be managed in accordance with existing regulations where environmental management must be carried out systematically and sustainably. Regarding the management of Covid-19 medical waste in the health facilities of North Toraja Regency, this study aims to determine the management process, management constraints and compare the management of wastewater and hazardous and toxic solid medical waste generated from handling Covid-19 patients at hospitals and health centers in Toraja Regency. North. The research method used is descriptive research method with case study design. Data collection techniques are carried out by observations, interviews, and documentation of secondary data. The results show that the management of hazardous and toxic solid medical waste water and waste for Covid-19 patients in private hospitals leaves the processing to third parties and government hospitals destroy solid hazardous medical waste with incinerators, while at community health center the wastewater is directly discharged into water bodies and medical hazardous waste. handed over to a third party. The process of treating waste water and hazardous and toxic solid medical waste for Covid-19 patients at the health facilities of North Toraja Regency has not been in accordance with the guidelines issued by the Indonesian Ministry of Health. The main obstacle in processing solid medical waste water and hazardous waste for Covid-19 patients at the health facilities of North Toraja Regency is the lacking of operational costs and delays in disbursing operational costs. Government-owned hospitals were found to be slow to comply the Covid-19 Health Facilities Waste Management Guidelines issued by the Ministry of Health of the Republic of Indonesia.

Multi-objective distributionally robust approach for optimal location of renewable energy sources

Increasing the amount of medical waste can cause environmental and health problems if it is not managed properly. This is because medical waste is categorized as hazardous and toxic waste (B3), which can also create hazardous waste management emergencies situation. PT.X is a company that treats medical waste by using an incinerator. The purpose of this study is to plan and implement a medical waste management emergency response program at PT.X by determining the potential for hazards to occur in medical waste processing, implementing an emergency response system, and developing and implementing an emergency response program. The data collection method used is by direct observation and discussion with the company staff, as well as through the available data at the company. Risk assessment was conducted using the matrix of likelihood and consequence of potential risks, and the results showed 4 activities that have extreme risk. By planning and implementing a B3 waste emergency response program, PT. X has optimized its preparedness in dealing with emergency conditions so that in the event of an emergency it can be handled optimally.

In order to overcome the drawback of the conventional signal timing optimization, a robust optimization algorithm for signal control parameters based on Tabu search-artificial bee colony algorithm is proposed under unsaturated flow condition. Based on the analysis of the characteristics of traffic signal control, a robust optimization model of signal control parameters is constructed by considering the minimum the average delay and the mean square error of average delay. As a consequence, the formation process of the initial solution to the bee colony is improved and the robust optimization model is solved by using the Tabu search-artificial bee colony algorithm. The proposed robust optimization model is validated by using an intersection in Zhangye City of China. The simulation results have shown that the robust optimization model and the algorithm are feasible and practicable. This robust model and algorithm can effectively deal with the volatility of traffic flow and reduce traffic delays.

A robust optimization model for green supplier selection and order allocation in a closed-loop supply chain considering cap-and-trade mechanism

A robust optimization framework for energy management of CCHP users with integrated demand response in electricity market

Introduction Hazardous healthcare waste produced at home contributes to approximately 0.5% of biomedical waste, and although potentially infectious, is often discarded with other domestic wastes. The study aimed to quantify and compare types and patterns of biomedical waste generated in homes and to assess the knowledge, attitude, and practices regarding biomedical waste among selected urban and rural households in the coastal area of Mangalore city in Karnataka. Methods A cross-sectional study was conducted in households consisting of rural and urban field practice areas of a tertiary care hospital in coastal Karnataka from November 2017 to February 2018. A pretested semi-structured questionnaire on knowledge, attitude, and practice regarding healthcare waste generated at home was assessed. Qualitative variables of the waste segregation practice, types of biomedical waste, the knowledge, perception, and attitude to practices regarding biomedical waste were expressed in percentages proportions and analyzed using SPSS v.20. Results Baby diapers were the most common hazardous waste. The number of baby diapers produced daily in rural and urban areas was 32 (42.7%) and 64 (85.3%), respectively. The urban households had better knowledge of possible hazardous healthcare waste at home, 28 (37.33%) compared with 7 (9.3%) in rural households. Seventy-one (94.7%) urban and 49 (65.3%) rural households had a favorable attitude toward the disposal of hazardous domestic biomedical waste. Conclusion The knowledge, attitudes, and practices regarding hazardous healthcare waste produced at the household level were better among urban households compared with rural households.

Many countries have no waste separation and mixed household waste accumulated mostly in landfills could pose serious environmental risk if landfills do not fulfill the requirements. The risk becomes much higher with increasing of hazardous components quantities in household waste (having hazardous properties and containing toxic chemicals). These include batteries, fluorescent lamps, chlorine-containing plastics, aromatic compounds and many others. Some hazardous waste are toxic themselves (waste oils) and some are not dangerous but contain toxic chemicals which are able to release into the environment under specific conditions.
\nThe purpose of this paper is the detailed study on hazardous components in household waste and the analysis of chemicals constituting these components and posing a threat to the environment and human.
\nThe profound literature review is used for the assessment of different hazardous household waste. Also, original investigations are carried out to identify toxic chemicals in some hazardous household waste: the chemical composition and data of manufacturers of batteries, cleanings, personal care products and fluorescent lamps are analyzed.
\nThe main hazardous components of household waste are analyzed: paints and varnishes, batteries, mercury-containing waste, waste of electrical and electronic equipment, cleaning products, medical waste, residues of pesticides and fertilizers, personal hygiene products. Hazardous substances contained in such components are considered. On the basis of detailed literature analysis and own research, it was investigated they are in a very large quantities. Among the most widespread substances are heavy metals, aromatic hydrocarbons, as well as many other carcinogenic and aggressive organic and inorganic compounds.
\nThe analysis shows household waste consisting of large number of hazardous components (fluorescent lamps, batteries, detergent residues, etc.) containing many toxic substances, including heavy metals, aromatic hydrocarbons and many other aggressive, carcinogenic organic and inorganic compounds. If hazardous components are mixed with household waste, then the hazard level of household waste and the operating costs sharply increase.

Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment.

Introduction:Healthcare facilities generate medical waste, also known as healthcare waste or health facility waste, during the healthcare delivery process, which is improperly managed as a result of underfunded healthcare systems, poor training, and lack of awareness of policies and legislation on handling medical waste as well as do not have national guidelines in place to adhere to the correct disposal of such wastage in developing countries like Ethiopia.Objectives:To estimate the healthcare waste generation rate among selected public hospitals in eastern Ethiopia.Methods:A cross-sectional study design was conducted in eight public in eastern Ethiopia from 1st June 2023 to 30th July 2023. The “Bed-Patient-healthcare wastes” model was used to estimate the healthcare waste generation rate based on WHO and empirical evidence. HCW generation rate = No of (Inpatients (Inpts) + Outpatients (Outpts))/day*0.53 kg/day. Meanwhile, general (GW), infectious (IW), pharmaceutical (PW), sharps (SW), and pathological (PtW) are computed as follows: GW = No. of (Inpt + Oupt)/day*0.53 kg/day and IW, PW, SW, and PtW = No. of Inpts/day*0.53 kg/day. In contrast, for hazardous and non-hazardous healthcare wastes, No of HCWs/day*0.8 and no. of HCWs/day*0.2 were utilized, and Key informant interviews and field observational were also applied. Descriptive analysis such as percentage, mean, and standard deviation were presented.Result:According to the current assessment, 105.1-142.8 kg/day healthcare waste was generated from outpatient and 167.3-244.2 kg/day from inpatients of public hospitals. From these, the Mean ± SD of daily healthcare waste generation rate estimated by non-hazardous and hazardous waste were 164.6 ± 80.9 kg/patients/day and 41.2 ± 20.2 kg/patients/day, respectively. In daily generation, the mean and standard deviations (Mean ± SD) of general wastes, infections waste, pharmaceutical wastes, sharp wastes, and pathological waste/patient were 62% (521.9), 23% (194.8), 4% (35.4), 2% (17.7), and 9% (70.9), respectively.Conclusion:The current assessment concluded that a large volume of healthcare waste is generated from selected public hospitals, which were not effectively segregated, separated at the source, and haven’t conventional or standard treatment. As a result, the study advised that regional and global collaboration in hospital waste and wastewater management be encouraged.

A multi-objective robust grey wolf optimization model for the study of concrete mix using copper slag and glass powder

Unpredictable disruptive events significantly increase the difficulty of the management of automobile supply chains. In this paper, we propose an automobile production planning problem with component chips substitution in a finite planning horizon. The shortage of one chip can be compensated by another chip of the same type with a higher‐end feature at an additional cost. Therefore, the automobile manufacturer can divert the on‐hand inventory of chips to product lines that are more profitable in the event of shortages caused by supply chain disruptions. To cope with this, we propose a max‐min robust optimization model that captures the uncertain supplies of chips. We show that the robust model has a mixed‐integer programming equivalence that can be solved by a commercial IP solver directly. We compare the max‐min robust model with the corresponding deterministic and two‐stage stochastic models for the same problem through extensive numerical experiments. The computational results show that the max‐min robust model outperforms the other two models in terms of the average and worst‐case profits.

Providing reservoirs with accurate forecasts is crucial for effective real-time flood control. This research focuses on the key role of forecasts in real-time flood management for reservoirs. A new approach was developed in this study, integrating a forecast-driven methodology to handle uncertainty in reservoir flood control operations. This involves a novel hybrid of two post-processing techniques: the Cloud model and error-based copula functions, together termed as the stochastic errors-based Cloud (SE-Cloud). Additionally, a multi-objective robust optimization model (MRO) was proposed, encompassing risk, resilience, and vulnerability, to address flood control challenges using ensemble forecasts. For comparative purposes, a two-objective stochastic optimization model (TSO) was also created, aiming to reduce both the highest expected reservoir level and peak discharge. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. The main findings of this study are: (1) The SE-Cloud model was proved to be more efficient in predicting peak flow events and in representing uncertainties in forecasts, with an improvement in hypervolume values ranging from 13.14% to 39.65% over the Cloud model. (2) The MRO strategy resulted in a higher inflow release compared to the TSO, leading to a 0.05m reduction in the anticipated highest water level and a 4.29% increase in peak discharge. (3) With the resilience value downstream remaining constant, it was suggested that increasing upstream vulnerability by using the MRO strategy would not lead to a decrease in resilience. The findings highlight the potential of AI-based ensemble forecasts in augmenting flood control robustness.