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Статті в журналах з теми "WASTE ALLOCATION"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

CHARDON, X., C. RAISON, A. LE GALL, T. MORVAN, and P. FAVERDIN. "Fumigene: a model to study the impact of management rules and constraints on agricultural waste allocation at the farm level." Journal of Agricultural Science 146, no. 5 (September 9, 2008): 521–39. http://dx.doi.org/10.1017/s0021859608008034.

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SUMMARYIn France, many dairy farms plan the allocation of animal wastes to the fields of the farm at the beginning of every year. This decision is complex, because many factors must be taken into account at the field and farm scales, including increasingly constraining environmental regulations. To evaluate the environmental impact of waste allocation strategies, these strategies have to be translated into consistent decisions. The objective of the current study was to reproduce the decisions made by farmers, in a wide range of contexts. For this purpose, a linear programming model that could help in generating yearly waste allocations was developed. The model, called Fumigene, takes into account the farmer's preferences and environmental, agronomic and feasibility constraints. It was applied on two case farms and the simulated waste allocations were compared to those chosen by the farmers over periods of 3 and 4 years, respectively. The evaluation showed that the waste allocations generated by the model were consistent with the strategies of the farmers. Fumigene was then used in investigating the impact of taking into account the phosphorus (P) fertilization constraints instead of only the nitrogen constraints. In the case studied, balancing P fertilization over 5 years led to small changes in waste allocation. Balancing P fertilization every year caused bigger changes and led to export of a part of the wastes. In a general way, Fumigene can be coupled with environmental evaluation tools to compare the impacts of different waste allocation strategies.

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2

Tung, Yeou-Koung, and Wade E. Hathhorn. "Stochastic waste load allocation." Ecological Modelling 51, no. 1-2 (May 1990): 29–46. http://dx.doi.org/10.1016/0304-3800(90)90056-m.

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3

Tung, Yeou-Koung, and Wade E. Hathhorn. "Multiple-objective waste load allocation." Water Resources Management 3, no. 2 (1989): 129–40. http://dx.doi.org/10.1007/bf00872468.

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4

Rossman, Lewis A. "Risk equivalent seasonal waste load allocation." Water Resources Research 25, no. 10 (October 1989): 2083–90. http://dx.doi.org/10.1029/wr025i010p02083.

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5

Tung, Yeou-Koung. "Multiple-objective stochastic waste-load allocation." Water Resources Management 6, no. 2 (1992): 117–33. http://dx.doi.org/10.1007/bf00872207.

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6

Burn, Donald H., and Jeanne S. Yulianti. "Waste-Load Allocation Using Genetic Algorithms." Journal of Water Resources Planning and Management 127, no. 2 (April 2001): 121–29. http://dx.doi.org/10.1061/(asce)0733-9496(2001)127:2(121).

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7

KOZLIAKOVA, Irina, Irina KOZHEVNIKOVA, Olga EREMINA, and Nadezhda ANISIMOVA. "ENGINEERING GEOLOGICAL TYPIFICATION OF TERRITORIES FOR ALLOCATION OF MUNICIPAL SOLID WASTE MANAGEMENT FACILITIES." Periódico Tchê Química 17, no. 35 (July 20, 2020): 1124–36. http://dx.doi.org/10.52571/ptq.v17.n35.2020.92_kozliakova_pgs_1124_1136.pdf.

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The management of municipal solid waste (MSW) appears to be one of the major ecological problems at present. Selecting sites for the disposal of municipal solid wastes or waste utilization facilities is one of the most contentious aspects of waste management. The present study aimed to develop the methodology to assess the natural protection of the geological environment from contamination upon the implementation of large ecological projects due to the allocation of MSW utilization facilities. The case of the Central Federal District of Russia (CFD) is taken as an example. The suitability of territories for municipal solid waste disposal is assessed according to the presence of weakly permeable deposits in the geological cross-section and their occurrence mode. A “map of engineering geological zoning CFD by the conditions of allocation MSW disposal sites and utilization facilities” has been compiled to a scale 1:2500000. The map gives a general indication of the suitability of the planned sites for waste disposal within the regarded region and showed that, for the bulk of territory, the allocation of MSW disposal and management facilities requires undertaking additional measures for the geoenvironmental protection from contamination.

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8

Umegbolu, Emmanuel I., and Innocentia N. Ozoejike. "Management of solid healthcare wastes in some government healthcare facilities in Enugu state, Southeast Nigeria: a cross-sectional study." International Journal Of Community Medicine And Public Health 4, no. 11 (October 25, 2017): 4031. http://dx.doi.org/10.18203/2394-6040.ijcmph20174813.

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Background: The significance of healthcare wastes (HCWs) consists in their hazardous component, which constitutes real danger to public health. In Nigeria, healthcare waste management (HCWM) has remained a problem yet to be properly recognized and so addressed. The study aimed to sensitise health workers and the public on the need for proper management of HCWs, considering the public health implications of not doing so.Methods: The waste management systems of ten healthcare facilities (HCFs) were assessed, using a modification of the WHO rapid assessment tool. In each HCF, segregated wastes were collected daily for ten days and quantified by weighing, using a spring balance.Results: Administratively, the HCWM system was poor in the ten HCFs (40.6%). 70% of them had satisfactory waste segregation, 81%, good waste treatment, and 26.7% adequate transportation methods for waste. None of the HCFs had budget allocation for HCWM, and 90% had inadequate storage facilities. Mean waste generation was 1.81 kg/day, 0.23 kg/patient/day, 0.16 kg/bed/day, and proportion of infectious wastes 16.8%. Correlation between the number of patients and proportion of infectious waste, was positive, strong and significant (r=0.80, p=0.01), and between bed occupancy rate and proportion of infectious waste, was positive too, but weak, and insignificant (r=0.34, p=0.34).Conclusions: In view of the identified weaknesses of the ten HCFs in HCWM, budget allocations for HCWM, improving waste storage facilities and transportation, with strengthening of waste segregation, collection, and treatment, would help to ensure adequate HCWM in the HCFs.

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9

Agudelo, Andrés, Antonio Valero, and César Torres. "Allocation of waste cost in thermoeconomic analysis." Energy 45, no. 1 (September 2012): 634–43. http://dx.doi.org/10.1016/j.energy.2012.07.034.

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10

El-Halwagi, M. M., A. A. Hamad, and G. W. Garrison. "Synthesis of waste interception and allocation networks." AIChE Journal 42, no. 11 (November 1996): 3087–101. http://dx.doi.org/10.1002/aic.690421109.

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11

Uysal, Cuneyt, and Ho-Young Kwak. "Role of Waste Cost in Thermoeconomic Analysis." Entropy 22, no. 3 (March 2, 2020): 289. http://dx.doi.org/10.3390/e22030289.

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Power plants or thermal systems wherein products such as electricity and steam are generated affect the natural environment, as well as human society, through the discharging of wastes. The wastes from such plants may include ashes, flue gases, and hot water streams. The waste cost is of primary importance in plant operation and industrial ecology. Therefore, an appropriate approach for including waste cost in a thermoeconomic analysis is essential. In this study, a method to take waste cost into account in thermoeconomics to determine the production cost of products via thermoeconomic analysis is proposed. The calculation of the waste cost flow rates at the dissipative units and their allocation to system components are important to obtain the production cost of a plant.

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12

Qiu, Ruzhi, Yingxia Li, Zhifeng Yang, Chunhui Li, Jingshan Yu, and Jianghong Shi. "A waste load allocation method based on unfairness factors and its application in the Zhangweinan Watershed, northern China." Water Science and Technology 62, no. 1 (July 1, 2010): 58–67. http://dx.doi.org/10.2166/wst.2010.278.

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A waste load allocation method was developed for industrial wastewater management based on unfairness factors, an industrial allocation factor and pollution reduction discounts. Three unfairness factors were defined to assess the relative efficiencies of energy consumption, pollution discharge and waste treatment costs for different industries. The overall effect of these factors was described by an industrial allocation factor. Based on the values of these factors, industries were classified into three types, after which waste load allocation proportions among different industries were determined using different pollution reduction discounts. This waste load allocation method was then applied in the Zhangweinan Watershed, which is one of the most seriously polluted watersheds in northern China. The results revealed that extractive, mechanical and food industries comprise the type I industries, which had the lowest pollution reduction discounts of 0, 0.25 and 0.5, respectively. The metallurgical industry and other industries were characterized as type II and discounts of 0.5 and 0.6 were given to their primary reductions. Textile, pharmaceutical, oil and pyrogenic, chemical and paper industries were classified as type III industries and had a waste load reduction of more than 80% of the pollution discharge in 2004.

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13

De Micheli, A. "Waste in Health Care." Journal of AMD 22, no. 4 (November 2019): 245. http://dx.doi.org/10.36171/jamd19.22.4.11.

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Waste in a health service is every activity, behavior, asset and service that, using resources, does not produce results in terms of health, well-being or quality of life. According to the OECD, in different countries, about 20% of health expenditure makes little or no contribution to improving people’s health. Avoiding waste is theoretically easy: health interventions delivered according to a principle of appropriateness are not wasting. However, there are multiple obstacles. Waste has, in fact, different causes: inappropriateness, use of ineffective health interventions, over- and under- use of health interventions, low quality standards, medical errors, lack of innovation, misplaced and unmotivated resource allocation, administrative complexity, incorrect management of equipments, assets and services, wasted time, non-compliance with quality standards, purchasing of technologies and assets at an excessive price, incomplete design or non-completion of works, fraud and corruption. In Italy, in 2017, waste in Health Service, even if calculated on indirect and inaccurate data, was estimated at 21.59 (±20%: 17.27 – 25.91) billion euros, 17-21% of public health spending. Strategies to reduce waste are essentially two: stop doing activities that do not generate value; use, if any, equal in effectiveness and safety, but less expensive, alternatives. The implementation goes through targeted research and the culture of continuous improvement rooted in the organization and owned and applied by decision makers and every health or administrative provider. KEY WORDS health resources; allocation of resources; cost-benefit analysis; health services misuse; inappropriate prescribing

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14

Shastitko, Andrey, Anna Meleshkina, and Anastasiya Shastitko. "Cost Allocation, Regulation and Incentives in Joint Production of Goods." Moscow University Economics Bulletin 2015, no. 1 (February 28, 2015): 18–40. http://dx.doi.org/10.38050/01300105201512.

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This article addresses the joint cost allocation problem in context of firm’s price policy, market structure, enforcement of tax, environmental and antitrust legislation and the correlating problem of incentives’ design. The paper presents the theoretical foundations to distinguish such concepts as joint and by-product, their correlation with the category of waste. Moreover, the authors provide a comparative analysis of different methods of joint cost allocations and their areas of applicability. The practical part of this work involves the overview of actual practices of cost allocation in chemical industry. It confirms the need for an adjusted approach assessment of the efficiency and legitimacy of joint production in each particular case.

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15

Ghosh, Subimal, and P. P. Mujumdar. "Fuzzy waste load allocation model: a multiobjective approach." Journal of Hydroinformatics 12, no. 1 (September 1, 2009): 83–96. http://dx.doi.org/10.2166/hydro.2010.028.

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Fuzzy Waste Load Allocation Model (FWLAM), developed in an earlier study, derives the optimal fractional levels, for the base flow conditions, considering the goals of the Pollution Control Agency (PCA) and dischargers. The Modified Fuzzy Waste Load Allocation Model (MFWLAM) developed subsequently is a stochastic model and considers the moments (mean, variance and skewness) of water quality indicators, incorporating uncertainty due to randomness of input variables along with uncertainty due to imprecision. The risk of low water quality is reduced significantly by using this modified model, but inclusion of new constraints leads to a low value of acceptability level, λ, interpreted as the maximized minimum satisfaction in the system. To improve this value, a new model, which is a combination of FWLAM and MFWLAM, is presented, allowing for some violations in the constraints of MFWLAM. This combined model is a multiobjective optimization model having the objectives, maximization of acceptability level and minimization of violation of constraints. Fuzzy multiobjective programming, goal programming and fuzzy goal programming are used to find the solutions. For the optimization model, Probabilistic Global Search Lausanne (PGSL) is used as a nonlinear optimization tool. The methodology is applied to a case study of the Tunga–Bhadra river system in south India. The model results in a compromised solution of a higher value of acceptability level as compared to MFWLAM, with a satisfactory value of risk. Thus the goal of risk minimization is achieved with a comparatively better value of acceptability level.

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16

Mujumdar, Pradeep P., and V. R. Subbarao Vemula. "Fuzzy Waste Load Allocation Model: Simulation-Optimization Approach." Journal of Computing in Civil Engineering 18, no. 2 (April 2004): 120–31. http://dx.doi.org/10.1061/(asce)0887-3801(2004)18:2(120).

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17

Mohan, S., and K. Pavan Kumar. "Waste Load allocation Using Machine Scheduling: Model Formulation." Environmental Processes 3, no. 1 (January 14, 2016): 125–37. http://dx.doi.org/10.1007/s40710-016-0121-y.

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18

Mohan, S., and K. Pavan Kumar. "Waste Load Allocation Using Machine Scheduling: Model Application." Environmental Processes 3, no. 1 (February 16, 2016): 139–51. http://dx.doi.org/10.1007/s40710-016-0122-x.

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19

Mahjouri, Najmeh, and Mohammad Bizhani-Manzar. "Waste Load Allocation in Rivers using Fallback Bargaining." Water Resources Management 27, no. 7 (February 1, 2013): 2125–36. http://dx.doi.org/10.1007/s11269-013-0279-2.

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20

Agustriyanto, Rudy. "Simulation of Water Allocation Optimization Problem." EMITTER International Journal of Engineering Technology 6, no. 2 (December 29, 2018): 191–99. http://dx.doi.org/10.24003/emitter.v6i2.244.

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Pollution prevention is primarily stimulated by economics, legislation, liability concerns, and the enhanced environmental benefit of managing waste at source. Chemical process industries consume a huge amount of water. Consequently, wastewater streams from such industries which contain various contaminants may create environmental problem. The increasing cost of fresh water supply and wastewater treatment has encouraged process industries to minimize fresh water consumption and waste water generation. This paper presents a formulation of water allocation problem (WAP) in order to minimize fresh water consumption in multi contaminant mass exchanger network. The approach is based on mass balance equation within the system being studied. The problem were then solved by using Matlab Optimization Toolbox.

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21

Cho, J. H., K. H. Ahn, W. J. Chung, and E. M. Gwon. "Waste load allocation for water quality management of a heavily polluted river using linear programming." Water Science and Technology 48, no. 10 (November 1, 2003): 185–90. http://dx.doi.org/10.2166/wst.2003.0571.

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A waste load allocation model using linear programming has been developed for economic water quality management. A modified Qual2e model was used for water quality calculations and transfer coefficients were derived from the calculated water quality. This allocation model was applied to the heavily polluted Gyungan River, located in South Korea. For water quality management of the river, two scenarios were proposed. Scenario 1 proposed to minimise the total waste load reduction in the river basin. Scenario 2 proposed to minimise waste load reduction considering regional equity. Waste loads, which have to be reduced at each sub-basin and WWTP, were determined to meet the water quality goal of the river. Application results of the allocation model indicate that advanced treatment is required for most of the existing WWTPs in the river basin and construction of new WWTPs and capacity expansion of existing plants are necessary. Distribution characteristics of pollution sources and pollutant loads in the river basin was analysed using Arc/View GIS.

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22

Huang, Jin, Van Butsic, Weijun He, Dagmawi Degefu, Zaiyi Liao, and Min An. "Historical Accountability for Equitable, Efficient, and Sustainable Allocation of the Right to Emit Wastewater in China." Entropy 20, no. 12 (December 10, 2018): 950. http://dx.doi.org/10.3390/e20120950.

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Establishing policies for controlling water pollution through discharge permits creates the basis for emission permit trading. Allocating wastewater discharge permits is a prerequisite to initiating the market. Past research has focused on designing schemes to allocate discharge permits efficiently, but these schemes have ignored differences among regions in terms of emission history. This is unfortunate, as fairness may dictate that areas that have been allowed to pollute in the past will receive fewer permits in the future. Furthermore, the spatial scales of previously proposed schemes are not practical. In this article, we proposed an information entropy improved proportional allocation method, which considers differences in GDP, population, water resources, and emission history at province spatial resolution as a new way to allocate waste water emission permits. The allocation of chemical oxygen demand (COD) among 30 provinces in China is used to illustrate the proposed discharge permit distribution mechanism. In addition, we compared the pollution distribution permits obtained from the proposed allocation scheme with allocation techniques that do not consider historical pollution and with the already established country plan. Our results showed that taking into account emission history as a factor when allocating wastewater discharge permits results in a fair distribution of economic benefits.

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23

ADACHI, Tsuyoshi, Gento MOGI, Akio ODANI, and Jiro YAMATOMI. "Optimum Allocation Model of Municipal Waste Recycling among Industries." Shigen-to-Sozai 115, no. 13 (1999): 951–57. http://dx.doi.org/10.2473/shigentosozai.115.951.

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24

NAGATA, Katsuya, Makoto NOHTOMI, Takaaki KOTODA, Haruaki Sekiai, and Morihiro OSADA. "Study on the Allocation in a Waste Incineration Plant." Proceedings of the Symposium on Environmental Engineering 2002.12 (2002): 221–23. http://dx.doi.org/10.1299/jsmeenv.2002.12.221.

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25

Fan, Y. R., G. H. Huang, K. Huang, L. Jin, and M. Q. Suo. "A Generalized Fuzzy Integer Programming Approach for Environmental Management under Uncertainty." Mathematical Problems in Engineering 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/486576.

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In this study, a generalized fuzzy integer programming (GFIP) method is developed for planning waste allocation and facility expansion under uncertainty. The developed method can (i) deal with uncertainties expressed as fuzzy sets with known membership functions regardless of the shapes (linear or nonlinear) of these membership functions, (ii) allow uncertainties to be directly communicated into the optimization process and the resulting solutions, and (iii) reflect dynamics in terms of waste-flow allocation and facility-capacity expansion. A stepwise interactive algorithm (SIA) is proposed to solve the GFIP problem and generate solutions expressed as fuzzy sets. The procedures of the SIA method include (i) discretizing the membership function grade of fuzzy parameters into a set ofα-cutlevels; (ii) converting the GFIP problem into an inexact mixed-integer linear programming (IMILP) problem under eachα-cut level; (iii) solving the IMILP problem through an interactive algorithm; and (iv) approximating the membership function for decision variables through statistical regression methods. The developed GFIP method is applied to a municipal solid waste (MSW) management problem to facilitate decision making on waste flow allocation and waste-treatment facilities expansion. The results, which are expressed as discrete or continuous fuzzy sets, can help identify desired alternatives for managing MSW under uncertainty.

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26

Bergeron, Francis C. "Analytical method of waste allocation in waste management systems: Concept, method and case study." Environmental Impact Assessment Review 62 (January 2017): 35–48. http://dx.doi.org/10.1016/j.eiar.2016.10.001.

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27

Karagiannidis, A., G. Perkoulidis, N. Moussiopoulos, and M. Chrysochoou. "Facility Location for Solid Waste Management through Compilation and Multicriterial Ranking of Optimal Decentralised Scenarios: A Case Study for the Region of Peloponesse in Southern Greece." Journal of Engineering Research [TJER] 1, no. 1 (December 1, 2004): 7. http://dx.doi.org/10.24200/tjer.vol1iss1pp7-18.

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The present paper addresses the problem of locating solid waste management facilities.Specifically, it studies and proposes optimal alternative solutions for the Greek Region of Peloponnese,by examining facilities for transferring, sorting, treating and landfilling of wastes. Quantitative and qualitative databases concerning the current solid waste management at the Region have been created and used by the model. A customized mixed-integer linear network model has been developed and solved for various evaluation criteria on a single-criterion basis by the use of a location-allocation modeling framework.The solutions resulting from the parametrical application of the multicriterial method ELECTRE III are then ranked for the entire criteria-spectrum. The best alternative scenario is presented for the Region in accordance with current legislation on waste management, which maximizes environmental benefits and promotes recycling, in the frame of sustainable waste management.

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28

Li, Zhichao, and Jilin Huang. "How to Effectively Improve Pesticide Waste Governance: A Perspective of Reverse Logistics." Sustainability 10, no. 10 (October 10, 2018): 3622. http://dx.doi.org/10.3390/su10103622.

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With the development of modern science and technology, agricultural production and quality have made major breakthroughs, followed by more and more pesticide waste. Pesticide waste refers to the packaging of pesticide residues. Pesticide waste will cause great damage to soil, water and human health, seriously affecting the ecological sustainability. Most of the existing literature is theoretical articles, and few empirical studies are provided on how to improve pesticide waste management. In this paper, a set of reverse logistics network models of pesticide wastes is constructed, and the framework of reverse logistics is used to realize the efficient recovery and treatment of pesticide wastes. The problem of how to collect pesticide wastes and how to optimize the location and flow allocation of facilities such as recycling center and treatment center was solved. The weights of the factors affecting the reverse logistics network model are determined by using the network analytic hierarchy process. Under the conditions of minimizing the cost and minimizing the negative externality, the mixed integer model programming method is used to make the optimal location decision and flow distribution. Finally, taking the JT area of China as an example, the results of the reverse logistics network model of pesticide wastes are demonstrated. The results show that the scheme is feasible.

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29

Peng, Zhen Guo, Xian Zheng Gong, Yu Liu, Chen Li, and Xiao Liu. "Life Cycle Inventory of Gangue as Alternative Raw Materials." Materials Science Forum 814 (March 2015): 533–38. http://dx.doi.org/10.4028/www.scientific.net/msf.814.533.

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With the development of science and technology and industry level, solid wastes are fully utilized. Among which gangue is the solid waste generated in the process of coal mining, its comprehensive utilization rate is more than 60%. Environmental pollution in the process of coal mining is one of the typical environmental problems confronted in China. In this paper, the life cycle inventory (LCI) of gangue was acquired by the method of life cycle assessment and further environmental impact assessment was achieved as well. The results showed that environmental impacts based on allocation of calorific value were greater than that of economic value, and it’s about 3.68 times the impacts assigned by economic value. Therefore allocation of economic value was better.

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30

Yang, Wei, Miao Ju Chen, Ying Liu, and Feng Yao Jin. "A System Dynamics Model for Water Quality Management - Waste Load Allocation." Advanced Materials Research 291-294 (July 2011): 1781–85. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.1781.

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A simulation-based system dynamics (SD) model was developed for allocating the waste load and supporting river quality management. Panjin section of Liaohe River in China was used as a study case to demonstrate the feasibility of the proposed methodology. The national surface-water environment function zoning of Liaoning Province was presented as a constraint in order to calculate the maximum allowable waste quantity for COD, which is expressed as interval format of both upper bound and lower bound. Then allocate the waste load to each point source responses within these quantities. The study results simulated from SD model, together with the intervals-format uncertainties of allowable COD level is valuable for supporting decision makers in optimizing water quality management.

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31

Joshi, Vijay, and Prasad Modak. "Heuristic Algorithms for Waste Load Allocation in a River Basin." Water Science and Technology 21, no. 8-9 (August 1, 1989): 1057–64. http://dx.doi.org/10.2166/wst.1989.0307.

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Waste load allocation for rivers has been a topic of growing interest. Dynamic programming based algorithms are particularly attractive in this context and are widely reported in the literature. Codes developed for dynamic programming are however complex, require substantial computer resources and importantly do not allow interactions of the user. Further, there is always resistance to utilizing mathematical programming based algorithms for practical applications. There has been therefore always a gap between theory and practice in systems analysis in water quality management. This paper presents various heuristic algorithms to bridge this gap with supporting comparisons with dynamic programming based algorithms. These heuristics make a good use of the insight gained in the system's behaviour through experience, a process akin to the one adopted by field personnel and therefore can readily be understood by a user familiar with the system. Also they allow user preferences in decision making via on-line interaction. Experience has shown that these heuristics are indeed well founded and compare very favourably with the sophisticated dynamic programming algorithms. Two examples have been included which demonstrate such a success of the heuristic algorithms.

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32

In, Ah Young, Da Been Jeong, and KwangSup Shin. "Design of Optimal Allocation and Disposal Plan of Medical Waste." Journal of the Korean Society of Supply Chain Management 20, no. 1 (May 31, 2020): 76–89. http://dx.doi.org/10.25052/kscm.2020.05.20.1.76.

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33

Sathiyabama, Chandru Nagarajan, and Pramod Dasan. "Optimum Resource Allocation and Eliminating Waste Inside the Food Industry." Central European Business Review 2, no. 2 (June 30, 2013): 31–44. http://dx.doi.org/10.18267/j.cebr.44.

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34

Saberi, Leila, and Mohammad Hossein Niksokhan. "Optimal waste load allocation using graph model for conflict resolution." Water Science and Technology 75, no. 6 (January 25, 2017): 1512–22. http://dx.doi.org/10.2166/wst.2016.429.

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Анотація:

In this paper, a new methodology is proposed for waste load allocation in river systems using the decision support system (DSS) for the graph model for conflict resolution II (GMCRII), multi-criteria decision making (MCDM) analysis and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Minimization of total treatment and penalty costs and minimization of biological oxygen demand violation of standards at the check point are considered as the main objectives of this study. At first, the water quality along the river was simulated using the Streeter-Phelps (S-P) equation coupled with the MOPSO model. Thereby a trade-off curve between the objectives is obtained and a set of non-dominated solutions is selected. In the next step, the best alternative is chosen using MCDM techniques and the GMCRII DSS package and non-cooperative stability definitions. The applicability and efficiency of the methodology are examined in a real-world case study of the Sefidrud River in the northern part of Iran.

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35

Hutcheson, M. R. "Waste load allocation for conservative substances to protect aquatic organisms." Water Resources Research 28, no. 1 (January 1992): 215–20. http://dx.doi.org/10.1029/91wr02480.

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36

Choshen-Hillel, Shoham, Alex Shaw, and Eugene M. Caruso. "Waste management: How reducing partiality can promote efficient resource allocation." Journal of Personality and Social Psychology 109, no. 2 (2015): 210–31. http://dx.doi.org/10.1037/pspa0000028.

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Minor, S. Diane, and Timothy L. Jacobs. "Optimal Land Allocation for Solid‐ and Hazardous‐Waste Landfill Siting." Journal of Environmental Engineering 120, no. 5 (September 1994): 1095–108. http://dx.doi.org/10.1061/(asce)0733-9372(1994)120:5(1095).

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Saadatpour, M., and A. Afshar. "Waste load allocation modeling with fuzzy goals; simulation-optimization approach." Water Resources Management 21, no. 7 (August 17, 2006): 1207–24. http://dx.doi.org/10.1007/s11269-006-9077-4.

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39

Zhao, Jun, and Lixiang Huang. "Multi-Period Network Design Problem in Regional Hazardous Waste Management Systems." International Journal of Environmental Research and Public Health 16, no. 11 (June 8, 2019): 2042. http://dx.doi.org/10.3390/ijerph16112042.

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Анотація:

The management of hazardous wastes in regions is required to design a multi-echelon network with multiple facilities including recycling, treatment and disposal centers servicing the transportation, recycling, treatment and disposal procedures of hazardous wastes and waste residues. The multi-period network design problem within is to determine the location of waste facilities and allocation/transportation of wastes/residues in each period during the planning horizon, such that the total cost and total risk in the location and transportation procedures are minimized. With consideration of the life cycle capacity of disposal centers, we formulate the problem as a bi-objective mixed integer linear programming model in which a unified modeling strategy is designed to describe the closing of existing waste facilities and the opening of new waste facilities. By exploiting the characteristics of the proposed model, an augmented ε -constraint algorithm is developed to solve the model and find highly qualified representative non-dominated solutions. Finally, computational results of a realistic case demonstrate that our algorithm can identify obviously distinct and uniformly distributed representative non-dominated solutions within reasonable time, revealing the trade-off between the total cost and total risk objectives efficiently. Meanwhile, the multi-period network design optimization is superior to the single-period optimization in terms of the objective quality.

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40

Nikouei Mehr, Maryam, and Ronald G. McGarvey. "Planning Solid Waste Collection with Robust Optimization: Location-Allocation, Receptacle Type, and Service Frequency." Advances in Operations Research 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/2912483.

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Consider the problem faced by a purchaser of solid waste management services, who needs to identify waste collection points, the assignment of waste generation points to waste collection points, and the type and number of receptacles utilized at each collection point. Receptacles whose collection schedule is specified in advance are charged a fixed fee according to the number of times the receptacle is serviced (emptied) per week. For other receptacles, the purchaser pays a fee comprised of a fixed service charge, plus a variable cost that is assessed on a per-ton-removed basis. We develop a mathematical programming model to minimize the costs that the purchaser pays to the waste management provider, subject to a level of service that is sufficient to collect all of the purchaser’s required waste. Examining historical data from the University of Missouri, we observed significant variability in the amount of waste serviced for nonscheduled receptacles. Because this variability has a significant impact on cost, we modified our model using robust optimization techniques to address the observed uncertainty. Our model’s highly robust solution, while slightly more expensive than the nonrobust solution in the most-optimistic scenario, significantly outperforms the nonrobust solution for all other potential scenarios.

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Rehman, Obaid-ur, Syed Jamil Hassan Kazmi, and Anwar Alam. "Mismanagement of Solid Waste & its Impact on the Environment of Peshawar City." International Journal of Learning and Development 2, no. 4 (August 9, 2012): 182. http://dx.doi.org/10.5296/ijld.v2i4.2213.

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The purpose of the study is to investigate reasons, types and workable for the disposal of wastes in Peshawar city with special emphasis on Municipal Corporation, Peshawar Development Authority (PDA) and local govt. The focus of the study is to ensure proper management of solid waste and cleanliness of the targeted areas. The data was collected through pre-designed questionnaire and interview schedule. The major sources of demographic data are census, sample surveys, demographic yearbook, statistical handbook, national and international publications. (Alam: 2006). Board, Karkhano and Hayat Abad Phase III were the targeted area of the study. A sample of size 300 was selected out of 37,839 population in three target areas i.e. Board 96 out of 12,164, Hayat Abad Phase III 121 out of 15,281, and Karkhano 83 out of 10,394 through proportion allocation method of sampling.The results of the study indicated that Local government has no proper planning, for the disposal of solid waste. The residents have no awareness for the proper disposal of the solid wastes. Population is directly proportion to the increase in solid wastes mismanagement, more over Afghan Refugees has accelerated the solid waste mismanagement in the area.

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Chung, Wu-Chun, Tsung-Lin Wu, Yi-Hsuan Lee, Kuo-Chan Huang, Hung-Chang Hsiao, and Kuan-Chou Lai. "Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds." Applied Sciences 11, no. 1 (December 25, 2020): 149. http://dx.doi.org/10.3390/app11010149.

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Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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Wang, Donglin, Xiangming Kong, Shan Zhao, and Yurui Fan. "FCVLP: A Fuzzy Random Conditional Value-at-Risk-Based Linear Programming Model for Municipal Solid Waste Management." Climate 7, no. 6 (June 6, 2019): 80. http://dx.doi.org/10.3390/cli7060080.

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A fuzzy random conditional value-at-risk-based linear programming (FCVLP) model was proposed in this study for dealing with municipal solid waste (MSW) management problems under uncertainty. FCVLP improves upon the existing fuzzy linear programming and fuzzy random conditional value-at-risk methods by allowing analysis of the risks of violating constraints that contain fuzzy parameters. A long-term MSW management problem was used to illustrate the applicability of FCVLP. The optimal feasibility solutions under various significance risk levels could be generated in order to analysis the trade-offs among the system cost, the feasibility degree of capacity constraints, and the risk level of waste-disposal-demand constraints. The results demonstrated that (1) a lower system cost may lead to a lower feasibility of waste-facility-capacity constraint and a higher risk of waste-disposal-demand constraint; (2) effects on system cost from vague information in incinerator capacity inputs would be greater than those in landfill capacity inputs; (3) the total allowable waste allocation would vary significantly because of the variations of risk levels and feasibility degrees. The proposed FCVLP method could be used to identify optimal waste allocation scenarios associated with a variety of complexities in MSW management systems.

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44

TABATA, Tomohiro, Naohiro GOTO, Koichi FUJIE, Hidehumi IMURA, and Tomotaka USUI. "Study on Waste Transportation and Appropriate Allocation of Recycling Facility by Spatial distribution of Waste Discharge." ENVIRONMENTAL SYSTEMS RESEARCH 30 (2002): 315–22. http://dx.doi.org/10.2208/proer.30.315.

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45

Zheng, Qing Jie, Bao Ming Han, and Hua Li. "Optimal Allocation of High-Speed Railway Mobile Equipment Based on the Passenger Flow." Applied Mechanics and Materials 505-506 (January 2014): 471–76. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.471.

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Based on the relationship between passenger flow and demand of electric multiple units (EMU), a new methodology to calculate the EMU demand and allocate the EMU is proposed, which can meet the demand of passenger as well as avoid the waste of transport capacity. An offline allocation plan of EMU is designed by analyzing the fluctuation in passenger flow, passenger average haul distance, passenger load factor and so on. Using the methodology, the EMU allocation problem is solved through program, which is used in Beijing-Shanghai High-speed Railway to obtain the EMU allocation plan.

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46

Kandeel, Fouad, Mohamed El-Shahawy, Gagandeep Singh, Donald C. Dafoe, Jeffrey S. Isenberg, and Arthur D. Riggs. "Towards a Rational Balanced Pancreatic and Islet Allocation Schema." Cell Transplantation 30 (January 1, 2021): 096368972110571. http://dx.doi.org/10.1177/09636897211057130.

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Allocation of donated organs for transplantation is a complex process that considers numerous factors such as donor, organ and candidate characteristics and practical issues such as geography. Whole pancreas and isolated islet transplantation are lifesaving for certain individuals with diabetes. Herein, we suggest a revised allocation schema that matches donor characteristics with candidate medical condition while allowing for geographic considerations. It is hoped that adoption of this schema will shorten allocation time, decrease organ waste and optimize the parity between organ donor characteristics and candidate state of health.

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47

Seung Taek Oh, Bidyut Baran Saha, Takahiko Miyazaki, and Shigeru Koyama. "F203 Cycle time allocation effect on the performance of adsorption cooling systems powered by SOFC waste heat." Proceedings of the National Symposium on Power and Energy Systems 2013.18 (2013): 443–46. http://dx.doi.org/10.1299/jsmepes.2013.18.443.

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48

Tao, Hongyan, Kejing Shu, and Chengwei Lv. "Eco-econo-efficiency Based Quota Allocation towards Coal De-capacity Reform Implementation: Case Study from China." E3S Web of Conferences 409 (2023): 03011. http://dx.doi.org/10.1051/e3sconf/202340903011.

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Reducing coal capacity is an essential part of China’s energy transition, with the formulation of a coal de-capacity quota allocation plan the key to achieving its coal overcapacity reductions. This paper presents a coal decapacity quota allocation mechanism based on a comprehensive consideration of the ecological, economic, and efficiency concerns at the enterprise level to deal with China’s coal overcapacity problems. A multi-objective optimization quota allocation model based on equilibrium strategy is established for the decision makers, in which the ecological concerns are measured by three waste emissions, the economic concern assessed based on the coal de-capacity costs, and the efficiency measured using a DEA method. By introducing a membership function method, the model is transformed into its equivalent single form and solved using LINGO software. To prove the practicality of the model, a case analysis was conducted. The results indicate that due to the different ecological and economic level in each coal mine, the allocation ratio of each coal mine for coal de-capacity is quite different. Compared with the government plan to reduce overcapacity in the mining area, the ecological benefits of the enterprise’s strategy increases by 4.69 million tonnes, and the economic costs has reduced 42.79 million CNY. A sensitivity analysis under different parameters for the decision maker preferences implemented, and the production efficiency changes in the mining areas analyzed and compared. It was found that although coal capacity reduction quota allocations can be affected by the decision makers preferences, the comprehensive ecological, economic, and efficiency objectives at small scale, low-efficiency coal mines should take on additional coal capacity reduction tasks. After the optimal allocations, the efficiencies in all mining areas reached an optimal value.

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Zaher, Mahmoud A., and Nabil M. Eldakhly. "An Integrated Framework for Dynamic Resource Allocation in Multi-project Environment." American Journal of Business and Operations Research 10, no. 1 (2023): 08–15. http://dx.doi.org/10.54216/ajbor.100101.

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This paper proposes an integrated machine learning (ML) framework for dynamic resource allocation in a multi-project environment. The framework utilizes machine learning algorithms to predict future resource demands and identify potential resource shortages. The proposed framework considers various factors such as project priorities, resource availability, and project deadlines to optimize resource allocation decisions. The framework is designed to continuously learn from past resource allocation decisions and improve future resource allocation strategies. The effectiveness of the proposed framework is evaluated through a case study in a real-world multi-project environment. The results show that the framework can significantly improve resource utilization and project completion times while reducing resource waste and cost. Overall, the proposed framework provides a practical solution for dynamic resource allocation in complex multi-project environments.

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Shah, Bhavin, and Vivek Khanzode. "Storage allocation framework for designing lean buffers in forward-reserve model: a test case." International Journal of Retail & Distribution Management 45, no. 1 (January 9, 2017): 90–118. http://dx.doi.org/10.1108/ijrdm-07-2016-0112.

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Purpose The retail revolution swing from traditional distribution to e-tailing services and unprecedented increase in internet adoption insist practitioners to diversely plan warehousing strategies. More than practically required storage space has been identified as wastes, and also it does not improve performance. An organized framework integrating storage design policies, operational performance and customer value improvement for retail-distribution management is lacking. Therefore, the purpose of this paper is to develop broad guidelines to design the “just-right” amount of forward area, i.e., “lean buffer” answering the following questions: “What should be lean buffer size? How effective the forward area is? As per demand variations, which storage waste (SKU) should be allocated with how much storage space? What is the amount of storage waste (SW)? How smooth the material flow is in between reserve-forward area?” for storage allocation in cosmetics distribution centers. Design/methodology/approach After forecasting static storage allocation between two planning horizons, if a particular SKU is less or non-moving, then it will cause SW, as the occupied location can be utilized by other competing SKUs, and also it impedes material flow for an instance. A dynamically efficient and self-adaptive, knapsack instance based heuristics is developed in order to make effective storage utilization. Findings The existing state-of-the-art under study is supported with a distribution center case, and the study investigates the need of a model adopting lean management approach in storage allocation policies along with test results in LINGO. The sensitivity analysis describes the impact of varying demand and buffer size on performance. The results are compared with uniform and exponential distributed demands, and findings reveal that the proposed heuristics improves efficiency and reduce SWs in forward-reserve area. Originality/value The presented model demonstrates a novel thinking of lean adoption in designing storage allocation strategy and its performance measures while reducing wastes and improving customer value. Future research issues are highlighted, which may be of great help to the researchers who would like to explore the emerging field of lean adoption for sustainable retail and distribution operations.

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