Recovering lead, plastic, and sulphuric acid from automobile used batteries by mathematical reverse logistics network modelling

Langarudi, Najme Roghani; Sadrnia, Abdolhossein; Sani, Amirreza Payandeh

doi:10.1504/pie.2019.098786

Cited by 6 publications

(4 citation statements)

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“…But there was no authentic mechanism defined for collection methods. Langarudi et al 22 conducted a study on reverse logistics frameworks for automotive LABs consisting of collection, remanufacturing, repair, recycling, and disposal to minimize the cost and CO 2 using mathematical modeling. However, this study also does not provide authentic methods of collection.…”

Section: Literature Surveymentioning

confidence: 99%

Blockchain-enabled architecture for lead acid battery circularity

Choudhary,

Sangwan,

Singh

2024

Sci Rep

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Widespread use of lead acid batteries (LABs) is resulting in the generation of million tons of battery waste, globally. LAB waste contains critical and hazardous materials, which have detrimental effects on the environment and human health. In recent times, recycling of the LABs has become efficient but the collection of batteries in developing countries is not efficient, which led to the non-professional treatment and recycling of these batteries in the informal sector. This paper proposes a blockchain-enabled architecture for LAB circularity, which ensures authentic, traceable and transparent system for collection and treatment of batteries. The stakeholders—battery manufacturers, distributors, retailers, users, and validators (governments, domain experts, third party experts, etc.)—are integrated in the circular loop through a blockchain network. A mobile application user interface is provided to all the stakeholders for the ease of adoption. The batteries manufactured and supplied in a geographical region as well as the recycled materials at the battery end-of-life are traced authentically. This architecture is expected to be useful for the battery manufacturers to improve their extended producer responsibility and support responsible consumption and production.

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Section: Literature Surveymentioning

confidence: 99%

Blockchain-enabled architecture for lead acid battery circularity

Choudhary,

Sangwan,

Singh

2024

Sci Rep

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“…Meanwhile, Zhang et al [77] determined the uncertainties with regard to planning for the transportation of ELVs in the ELV recycling process. Furthermore, Langarudi et al [78] explored the uncertainty in the reverse logistics network for automotive recycling operations. Dong et al [79] examined the uncertainties in the closed-loop supply chain before proposing a strategic location for ELV recycling.…”

Section: Logistics and Network Facilitiesmentioning

confidence: 99%

“…For this reason, many past studies proposed numerous strategies to manage the uncertainty related to logistics and network facilities. Firstly, several studies applied quantitative methods to cope with uncertainties in logistics and network facilities, which include mathematical modelling methods such as MILP [66,67,[70][71][72][73]75,76,78,79,84,90], linear programming (LP) [88], fuzzy linear programming (FLP) [69], bi-objective nonlinear integer programming (BONLIP) [74], bi-level programming [89], mixed-integer nonlinear program-ming (MINLP) [63], multi-objective mixed-integer linear programming (MOMILP) [65], polymorphic uncertain linear programming (PULP) [77], mixed-integer nonlinear programming (MINLP) [82], and multi-objective fuzzy linear programming (MOFLP) [87]. Other than that, several studies adopted the MCDM method to deal with the uncertainties, which include the combination of elimination and choice translating reality (ELECTRE I), Simos procedure, and stochastic multi-criteria acceptability analysis [64], intuitionistic fuzzy combinative distance-based assessment (CODAS) [81], and interval type-2 fuzzy additive ratio assessment (ARAS) [86].…”

Section: Managing Logistics and Network Facility Uncertaintymentioning

confidence: 99%

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A Review of Supply Chain Uncertainty Management in the End-of-Life Vehicle Industry

et al. 2022

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Various uncertainties exist in the End-of-Life Vehicle (ELV) industry, which further complicates the ELV business’s growth. In order to pursue greater progress in the ELV business establishment, a comprehensive analysis of previous ELV studies with regard to the supply chain uncertainty perspective is essential. The objective of this study is aimed at categorising the existing supply chain uncertainty sources according to their end-of-life (EoL) strategies, identify the management approaches adopted to analyse the prominent research trends, and conduct a regional analysis of ELV supply chain studies for the past years, from 2016 until 2022. The content analysis method was used to extract all the essential information from previous research, and an analysis was performed to obtain the latest research trends and identify the relationship between the gathered data. The findings show that the past research focuses on three main supply chain uncertainties, namely, uncertainty in logistics and network facilities (31.8%), production and operations (30.7%), and environmental (25.0%). Furthermore, the regional analysis shows that 83% of the studies were conducted in developing countries over the past years. Lastly, several research gaps were presented to provide researchers with potential directions and the way forward to explore ELV supply chain research from the uncertainty management context.

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