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A comparative study of ε-constraint, lp-metric, and weighted sum multi-objective optimization methods in a circular economy

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dc.contributor.author Kulshrestha, Rakhee
dc.contributor.author Sangwan, Kuldip Singh
dc.date.accessioned 2025-09-22T04:48:27Z
dc.date.available 2025-09-22T04:48:27Z
dc.date.issued 2024
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S2212827124000623
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19491
dc.description.abstract Approximately 74.7 Mt (Million Metric Tonnes) of e-waste is expected to be produced in 2030, and laptop e-waste is one of the major constituents of this. The goal of this paper is to develop and optimize a mixed-integer linear programming (MILP) mathematical model for a laptop manufacturer in India, based on a framework that integrates secondary reuse concept associated with traditional circular economy waste avoidance strategies. The multi-objective solution techniques of ε-constraint, LP-metric, and weighted sum methods are used to optimize the circular economy model. The proposed model aids as a policy tool to decide the optimum number of inspection/collection centers, sales/distribution centers, disassembly centers, refurbishing centers, recycling centers, and their optimum locations and allocations. This study results suggest that reuse, secondary customer centers, refurbishing, and recycling of the laptops is not only economically beneficial to the organization but also environment friendly and helps to create more jobs in the rural economy. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Mathematics en_US
dc.subject Circular economy en_US
dc.subject Sustainability en_US
dc.subject Multi-objective optimization en_US
dc.subject MILP en_US
dc.subject WEEE en_US
dc.title A comparative study of ε-constraint, lp-metric, and weighted sum multi-objective optimization methods in a circular economy en_US
dc.type Article en_US


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