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Waste production and related environmental problems have caused urban services management many problems in collecting, transporting and disposal of waste. Since collecting and transporting waste are important parts of waste management budget, it is necessary to apply an appropriate method to reduce costs of collection. The aim of this study is to design a new model for urban waste collection vehicle routing problem with time windows and energy generating from waste.

A multiobjective model is presented with the objectives of minimizing cost of waste collection, reducing emissions from environmental pollutants and increasing income of recycles trade and energy recovery. Concerning the complexity of the model and its inability to solve large-scale problems, hyperactive genetic algorithms and multiple objective particle swarm optimization are applied.

The proposed model not only affects costs and income but also reduces the emissions of environmental pollutants. To solve the formulated model, multitarget approaches are applied on the processing site of Kermanshah city as a case study. The solutions of these algorithms and the exact method of partial constraints are compared and the outcomes are verified by numerical analysis by solving various examples in small, medium and large scales. The proposed model helps to collect urban wastes in the shortest possible time in addition to reducing the total cost, revenues from the sale of recycled materials and energy generation.

Waste collection is related to VRP issue. Considering the environmental requirements in waste management, the concept of green supply chain is approached. This study provides a better understanding of urban waste management by examining various articles and combining economic and environmental dimensions. Waste management with a green approach and energy production from disposable waste involves many common stakeholders.

This study suggests that proper waste management can generate revenue from municipal waste. Moreover, it reduces cost of collecting and emission of environmental pollutants. These findings could motivate companies to waste management with a sustainable approach.

The proposed model boosts the current understanding of the waste management and energy generation of waste. The paper adds additional values by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

Waste production and related environmental problems have caused urban services management many problems in collecting, transporting and disposal of waste. The purpose of this study is to design a new model for municipal waste collection vehicle routing problems with time windows and energy generating from waste. To this purpose, a bi-objective model is presented with the objectives of increasing the income of waste recycles and energy generation from waste and reducing emissions from environmental pollutants.

A bi-objective model is presented with the objectives of increasing income of recycles trade and energy generation and reducing emissions from environmental pollutants. Concerning the complexity of the model and its inability to solve large-scale problems, non-dominated sorting genetic algorithms and multi-objective particle swarm optimization algorithms are applied.

In this research, an integrated approach to urban waste collection modeling that coordinates the various activities of waste management in the city of Kermanshah and energy generation from waste are provided. Besides, this study calculates the criteria that show the environmental effects of municipal waste. The proposed model helps to collect municipal wastes in the shortest possible time in addition to reducing the total cost, revenues from the sale of recycled materials and energy production.

The proposed model boosts the current understanding of the waste management and energy generation of waste. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

Production of waste has been increased exponentially due to world industrialization and urban and machine life expansion. On the other hand, the outbreak of the COVID-19 coronavirus quickly became a global crisis. This crisis has added a large amount of waste to urban waste. The purpose of this study is to create cooperation between municipal waste collector contractors.

Thus, a mathematical model is proposed under uncertain conditions, which includes the volume of municipal waste and infectious waste including personal protective equipment and used equipment for patients. To reduce total costs, the results are evaluated with four cooperative game theory methods such as Shapley value, t value, core center and least core. Ultimately, the saved cost by cooperation in each coalition is allocated fairly among the contractors. Finally, a comparison was made between the solution methods based on the value of the objective function and the solution time.

The results indicate that the proposed cooperative method increases cost savings and reduces the fine of residual waste. Therefore, it can be mentioned that this kind of cooperation would finally result in more incentives for contractors to form larger coalitions. Genetic algorithms were used to solve the large-scale model.

The proposed model boosts the current understanding of waste management in the COVID-19 pandemic. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

In this study, the authors consider the key decisions in the design of the green closed-loop supply chain (CSLC) network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, in this paper is the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.

In this study, the author consider the key decisions in the design of the green CLSC network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.

The results indicate that the results obtained from the colonial competition algorithm have higher quality than the genetic algorithm. This quality of results includes relative percentage deviation and computational time of the algorithm and it is shown that the computational time of the colonial competition algorithm is significantly lower than the computational time of the genetic algorithm. Furthermore, the limit test and sensitivity analysis results show that the proposed model has sufficient accuracy.

Solid modeling of the green supply chain of the closed loop using the solid optimized method by Bertsimas and Sim. Development of models that considered environmental impacts to the closed loop supply chain. Considering the impact of the technology type in the manufacture of products and the recycling of waste that will reduce emissions of environmental pollutants. Another innovation of the model is the multi-cycle modeling of the closed loop of supply chain by considering the uncertainty and the fixed and variable cost of transport.

To study the consumption pattern, attitude and knowledge of the general population about dietary supplements (DS) in the United Arab Emirates (UAE).

A community-based cross-sectional study was conducted to collect data on knowledge, attitude and consumption pattern related to the use of DS. Participants aged = 18 years were asked to complete a self-administered online questionnaire that included demographic characteristics, health and lifestyle information, consumption patterns, attitudes and knowledge levels regarding the use of DS.

A total of 207 individuals participated in the study, and 117 (56.5%) participants reported using DS products as influenced by the pandemic of coronavirus disease 2019 (COVID-19), of which 63 (53.8%) participants had been using DS for more than one month but less than one year. The majority of the participants was females (64.7%), non-UAE nationals (60.9%) and employed (51.7%). Multivitamins (77.8%) were the most commonly used DS. Use of DS was more prevalent among older participants (n = 78 (61.9%), p = 0.006), non-UAE nationals (n = 79 (62.7%), p = 0.025) and employed (n = 69 (64.5%), p = 0.023). Improving general health (76.1%) and immune booster (47%) were the most frequently identified reasons for using DS, which is relatable to the COVID-19 pandemic. The majority of study participants (72.12%) reported knowing relatively little about the use of DS. About 154 participants (74.4%) did not know that DS products do not treat diseases.

Further studies with a larger sample size need to be conducted to examine the association between gender or chronic disease and the consumption and type of DS products used to fill the gap in the literature and overcome the limitations identified in this study.

This study highlights the need for community education programs and strategies that can raise awareness of the health benefits and risks of using DS. Further studies with a larger sample size need to be conducted to examine the association between gender or chronic disease and the consumption and type of DS products used to fill the gap in the literature and overcome the limitations identified in this study.