Estimation of electronic waste using optimized multivariate grey models

Duman, Gazi Murat; Kongar, Elif; Gupta, Surendra M.

doi:10.1016/j.wasman.2019.06.023

Cited by 107 publications

(53 citation statements)

References 53 publications

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“…In line with the advanced manufacturing and supply chain technologies, the importance of predictive models dramatically grows due to their capability of analyzing large scale datasets. Predictive analyses introduce conducive models such as simulation and statistical tools to foresee the patterns of large datasets and what is likely happen [32]. Such competence can remarkably help faster and more efficient decision making in trade-in operations; therefore, it provides the OEM with an autonomy in reducing complexities and the resulting time and cost in operational layers.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

2020

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Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

2020

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“…However, recent studies [9,23,24] has pointed out that, the estimated values of the background value may be unequal to the actual values, explained in Section 2.2. Hence, based on the background values, the first-order grey differential equation can be formulated as…”

Section: Overview Of the Gmc (1 N) Modelmentioning

confidence: 99%

“…e GMC(1, n) model has gains popularity in practical applications because of its accurate estimations and simple structures. Many researchers such as Tien [14][15][16][17], Ma et al [31,32], and Duman et al [24], have elaborated the procedures for this model, which can currently be outlined below:…”

Section: Overview Of the Gmc (1 N) Modelmentioning

confidence: 99%

“…, n) into the structure of background values in GMC (1, n), namely, the background values for the system behavior sequence Z (1) 1 (rp + t) � λ 1 X (1) 1 (rp + t) + (1 − λ 1 )X (1) 1 (rp + t − 1) and the background values for the relevant sequences Z (1) i (t) � λ i X (1) i (t) + (1 − λ i )X (1) i (t − 1), i � 2,3,.. .. Subsequently, for the purpose of further enhancing precision, various heuristic intelligent techniques are employed to determine the background value coefficient, including Particle Swarm Optimization (PSO) [24][25][26], Genetic Algorithm (GA) [27,28], Ant Lion Optimizer (ALO) [29], and Ant Colony Algorithm (ACA) [30].…”

Section: Introductionmentioning

confidence: 99%

“…erefore, proposing an accurate forecasting grey model that has a simple structure and easy-understanding computations with a focus on the background values is a very valuable work. General consensus gotten from the literature was that accurate background values are regarded as a crucial requirement to ensure the reliability and practicability of the GMC(1, n) model [23,24], and the forecasting precision is heavily dependent on their correct computations. Supporting this argument, one of the major contributions in this paper stems from the mathematical derivation of the actual background values, finding its differences from the estimated one in the conventional GMC(1, n) model.…”

Section: Introductionmentioning

confidence: 99%

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A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications

Ding

2020

Complexity

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Accurate estimations can provide a solid basis for decision-making and policy-making that have experienced some kind of complication and uncertainty. Accordingly, a multivariable grey convolution model (GMC (1, n)) having correct solutions is put forward to deal with such complicated and uncertain issues, instead of the incorrect multivariable grey model (GM (1, n)). However, the conventional approach to computing background values of the GMC (1, n) model is inaccurate, and this model's forecasting accuracy cannot be expected. ereby, the drawback analysis of the GMC (1, n) model is conducted with mathematical reasoning, which can explain why this model is inaccurate in some applications. In order to eliminate the drawbacks, a new optimized GMC (1, n), shorted for OGMC (1, n), is proposed, whose background values are calculated based on Simpson' rule that is able to efficiently approximate the integration of a function. Furthermore, its extended version that uses the Gaussian rule to discretize the convolution integral, abbreviated as OGMC G (1, n), is proposed to further enhance the model's forecasting ability. In general, these two optimized models have such advantages as simplified structure, consistent forecasting performance, and satisfactory efficiency. ree empirical studies are carried out for verifying the above advantages of the optimized model, compared with the conventional GMC (1, n), GMC G (1, n), GM (1, n), and DGM (1, n) models. Results show that the new background values can effectively be calculated based on Simpson's rule, and the optimized models significantly outperform other competing models in most cases.

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Paper Electronics Utilizing Screen Printing and Vapor Phase Polymerization

Brooke

Edberg

Petsagkourakis

et al. 2023

Advanced Sustainable Systems

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The rise of paper electronics has been accelerated due to the public push for sustainability. Electronic waste can potentially be avoided if certain materials in electronic components can be substituted for greener alternatives such as paper. Within this report, it is demonstrated that conductive polymers poly(3,4‐ethylenedoxythiophene) (PEDOT), polypyrrole, and polythiophene, can be synthesized by screen printing combined with vapor phase polymerization on paper substrates and further incorporated into functional electronic components. High patterning resolution (100 µm) is achieved for all conductive polymers, with PEDOT showing impressive sheet resistance values. PEDOT is incorporated as conductive circuitry and as the active material in all‐printed electrochromic displays. The conductive polymer circuits allow for functional light emitting diodes, while the electrochromic displays are comparable to commercial displays utilizing PEDOT on plastic substrates.

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Estimation of electronic waste using optimized multivariate grey models

Cited by 107 publications

References 53 publications

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

A New Multivariable Grey Convolution Model Based on Simpson’s Rule and Its Applications

Paper Electronics Utilizing Screen Printing and Vapor Phase Polymerization

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