Department of Mathematics

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Author: search.filters.author.Sangwan, Kuldip SinghSubject: search.filters.subject.Sustainability

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    A comparative study of ε-constraint, lp-metric, and weighted sum multi-objective optimization methods in a circular economy
    (Elsevier, 2024) Kulshrestha, Rakhee; Sangwan, Kuldip Singh
    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.
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    Optimization of Specific Energy, Scrap, and Surface Roughness in 3D Printing Using Integrated ANN-GA Approach
    (Elsevier, 2023) Kulshrestha, Rakhee; Sangwan, Kuldip Singh
    3D printing technology is fast emerging as a solution to convert cyber models to physical models quickly for visualization and feedback in Industry 4.0 environment. Energy efficiency, surface roughness, and material wastage are important performance responses and the effects of 3D printing parameters on these conflicting responses need to be studies to further improve the technology. Multiobjective optimization is a tool to obtain the right balance among conflicting performance responses. This paper aims to find the optimal values of infill, layer height, printing speed, extruder temperature, and scale to optimize specific energy, scrap, and surface roughness, simultaneously. Experiments were performed based on a Taguchi L-27 orthogonal array using PLA filament. A predictive model has been developed using artificial neural network (ANN) integrated with a genetic algorithm (GA) for obtaining Pareto solutions. Technique for order preference by similarity to ideal solution (TOPSIS) is used to obtain the most preferred solution from the Pareto solutions and analytical hierarchal process (AHP) is used to determine weights of the three objectives. The proposed methodology is expected to help the practitioners to rank and customise the decisions proactively in conflicting scenarios before the product is 3D printed, thereby improving sustainability and/or meeting product quality requirements