A multi-objective optimization model for municipal waste management system in Tehran city, Iran

Ahani, Mona; Arjmandi, Reza; Hoveidi, H.; Jamal, Ghodousi; Lavasani, Mohammad

doi:10.1007/s13762-019-02335-1

Cited by 16 publications

(11 citation statements)

References 34 publications

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“…A multiobjective nonlinear optimization model was formulated to determine the optimal flows of MSW to different waste treatment facilities. Ahani et al 26 focused on the application of a multiobjective optimization algorithm and a fuzzy expert system to address the issue of waste management in Tehran city, Iran. A multiobjective model was formulated to simultaneously optimize cost and risk.…”

Section: Introductionmentioning

confidence: 99%

“…The objective was to design a cost effective supply chain network while simultaneously addressing the sustainability issues such as public health impacts and land use. Ahani et al 26 focused on the application of a multiobjective optimization algorithm and a fuzzy expert system to address the issue of waste management in Tehran city, Iran. Objective function was to simultaneously optimize the economic and environmental costs.…”

Section: Introductionmentioning

confidence: 99%

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A multiobjective optimization framework for sustainable design of municipal solid waste processing pathways to energy and materials

et al. 2019

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A large quantity of municipal solid waste (MSW) is generated worldwide, and its effective management is a major problem in urban areas or particularly the areas where waste is dumped. Numerous technological alternatives are available for waste treatment, each with different costs and environmental footprints. For sustainable waste management, both economic and environmental aspects should be considered. Therefore, this study addresses the systematic design of processing routes for the sustainable management and utilization of MSW under economic and environmental criteria. To address this, a generic superstructure-based multiobjective optimization framework is developed. MSW superstructure composes of the potential processing routes for the waste management where MSW can be utilized for energy generation and other valuable waste-based products. On the basis of superstructure, we develop a multiobjective mixed-integer nonlinear programming (MINLP) model that simultaneously maximizes the net profit and minimizes the greenhouse gas (GHG) emissions. The developed optimization model is coded and solved in GAMS to determine the optimal most promising routes for the sustainable processing of MSW. A case of the Emirate of Abu Dhabi is employed to test the applicability of the developed framework. The optimization results emphasize that pathway 2 shows a decent trade-off between economic and environmental objectives, whereas the two most environmentally sustainable processing pathways have the potential to reduce GHG emissions by 58% and 80%, compared with business as usual (BAU) scenario, and also possess potential economic benefits. The insights gained from this analysis guide the municipality planners to devise a promising and sustainable waste management strategy. K E Y W O R D Seconomic analysis, environmental analysis, multiobjective optimization, municipal solid waste, sustainable management

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multiobjective optimization framework for sustainable design of municipal solid waste processing pathways to energy and materials

et al. 2019

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“…Ahani, M. et al [25] proposed a multi-stage solution to solve sustainable biomass supply chain network design problem. This method uses the geographic information system to obtain the candidate location of the facility, and uses the analytic hierarchy process to improve the location.…”

Section: Multi-objective Solution Methodsmentioning

confidence: 99%

Optimization of Location-Routing for the Waste Household Appliances Recycling Logistics under the Uncertain Condition

et al. 2022

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Waste household appliances and electronic products usually contain harmful substances which need scientific and reasonable collection, classification, processing, recovery and disposal to achieve sustainable and effective recycling and utilization. In recent years, due to the poor management of waste household appliances recycling logistics system, safety accidents occur frequently, which seriously harm the health and life safety of the society. This paper studies the risk management of recycling waste household appliances under uncertain conditions and establishes a risk measurement model under fuzzy population density. Considering the multi-stage and classification diversity of waste household appliances recycling logistics, the multi-objective location routing model and location -routing model are established respectively. Based on the model complexity analysis, the solution method of multi-objective model is designed. Finally, the validity of the model and algorithm is verified by examples and tests.

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“…The technology has been widely used since its inception, especially in recent years, the development is particularly rapid, common in life such as face recognition, expression recognition, license plate recognition, and other various fields are used in machine vision technology, and this technology is also improving with the development of society. With the mandatory implementation of solid waste sorting, intelligent solid waste sorting technology that can help people solve the problem of how-to sort has also come into being [4]. The application of machine vision technology can complete the intelligent classification of recyclable solid waste and achieve the accurate identification and classification of solid waste, which is bound to become a future trend.…”

Section: Introductionmentioning

confidence: 99%

Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology

Chen

2021

Advances in Mathematical Physics

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In this paper, the technique of image recognition algorithm is used to conduct an in-depth study and analysis of the intelligent classification and recycling system of solid waste and to optimize the design of its system. The network structure and detection principle of the YOLO target detection algorithm based on convolutional neural nets are analysed, images of construction solid waste are collected as a dataset, and the image dataset is expanded using data enhancement techniques, and the target objects in the dataset are labelled and used to train their own YOLO detection models. To facilitate testing the images and to design a YOLO algorithm-based construction solid waste target detection system. Using the detection system for construction solid waste recognition, the YOLO model can accurately detect the location, class, and confidential information of the target object in the image. Image recognition is a technique to recognize images by capturing real-life images through devices and performing feature extraction, and this technique has been widely used since its inception. The deep learning-based classification algorithm for recyclable solid waste studied in this paper can classify solid waste efficiently and accurately, solving the problem that people do not know how to classify solid waste in daily life. The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. However, the actual situation is intricate and often the result is not obtained as envisioned, and the use of migration learning can be a good way to improve the overfitting phenomenon. In this paper, the combination of lazy optimizer and lookahead can improve the generalization ability and fitting speed as well as greatly improve the accuracy and stability. The experimental results are tested, and it is found that the solid waste classification accuracy can be as high as 95% when the VGG19 model is selected and the optimizer is combined.

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A multi-objective optimization model for municipal waste management system in Tehran city, Iran

Cited by 16 publications

References 34 publications

A multiobjective optimization framework for sustainable design of municipal solid waste processing pathways to energy and materials

A multiobjective optimization framework for sustainable design of municipal solid waste processing pathways to energy and materials

Optimization of Location-Routing for the Waste Household Appliances Recycling Logistics under the Uncertain Condition

Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology

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