Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression

Wohlgemuth, Murilo; Fries, Carlos Ernani; Sant’Anna, Ângelo Márcio Oliveira; Giglio, Ricardo; Fettermann, Diego de Castro

doi:10.1007/s10479-018-3105-7

Cited by 14 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Antomarioni et al (2019) applied a data mining technique to analyze components breakdown data based on failures probability measurements from industrial process. Wohlgemuth et al (2020) applied fault detection and pattern recognition scheme in an operating system. Liu et al (2021) presented a fault-assistant scheme for monitoring fault data collected and stored by sensing and computer technologies in industrial process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

Sant’Anna

2022

IJQRM

Self Cite

View full text Add to dashboard Cite

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

show abstract

Section: Introductionmentioning

confidence: 99%

“…(2019) applied a data mining technique to analyze components breakdown data based on failures probability measurements from industrial process. Wohlgemuth et al. (2020) applied fault detection and pattern recognition scheme in an operating system.…”

Section: Introductionmentioning

confidence: 99%

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

Sant’Anna

2022

IJQRM

Self Cite

View full text Add to dashboard Cite

show abstract

“…For logistics efficiency evaluation, Chinese scholars have mostly focused on macro-level evaluation in areas along the Belt and Road [5,6] and the Yangtze River Economic Belt [7,8]. International studies, on the other hand, have concentrated more on the micro level such as port logistics [9][10][11] and enterprise logistics [12][13]. In terms of the choice of evaluation methods, there are two main methods for calculating total factor productivity depending on whether a production function needs to be set: parametric and non-parametric methods.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Evolutionary Characteristics and Its Driving Mechanisms of China’s Logistics Industry Efficiency under Low Carbon Constraints

Huang¹,

Wei²,

Wang³

2022

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

As a pillar industry for national economic development and a key industry for carbon emission reduction in China, the logistics industry occupies a special place in the low carbon economy. In order to understand the spatial and temporal evolutionary characteristics and driving mechanisms of the efficiency of China's logistics industry, and to decipher the development path of the traditional logistics industry, this study selects the efficiency of China's provincial logistics industry as the research object. The study finds that the efficiency of China's logistics industry is at a low level of development and tends to fluctuate upwards, with the provinces with higher efficiency values concentrated in the eastern region; the level of economic development, the level of information technology, government logistics regulation and environmental regulations can significantly improve the efficiency of the logistics industry in the region, but energy intensity will have a negative impact on the efficiency of the logistics industry. In addition, the level of economic development of a region has a significant 'siphoning effect' on other regions, inhibiting the development of the logistics industry in other regions. Finally, based on the findings of the study, recommendations are made to promote the green development of logistics.

show abstract

“…In a few words, DOE brings the range of experiments from uncontrollable factors that are introduced randomly to carefully control these factors (Antony et al, 2014). Wohlgemuth et al (2020) integrated the statistical design and mathematical programming approaches to improve the productive efficiency of a set of decision-making units for logistic operators. Tervo et al (2003) argued that, when a parameter appears nonlinearly in the model, it can be considered that an optimally designed experiment depends on the current estimated value.…”

Section: Introductionmentioning

confidence: 99%

Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

et al. 2021

Self Cite

View full text Add to dashboard Cite

Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with nondominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance (IGD). To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms.

show abstract

Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression

Cited by 14 publications

References 29 publications

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

Spatial-Temporal Evolutionary Characteristics and Its Driving Mechanisms of China’s Logistics Industry Efficiency under Low Carbon Constraints

Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

Contact Info

Product

Resources

About