Improving Resource Management of Large Logistics Facilities

Miodrag, Zoran; Tesch, Christan; Clausen, Uwe

doi:10.1016/j.sbspro.2012.09.720

Cited by 3 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bunse et al (2011) highlight the importance of investigations on energy usage profiles in reference to processes and machines (and associated equipment) in order to compare individual performance numbers, whereas Seow and Rahimifard (2011) criticise the lack of accurate data as well as scientific publications in this field (Mueller et al 2013b). Available data of intralogistic processes were majorly generated by energy audits and energy balance sheets (Bunse et al 2011) so that there is a lack of comprehensive real environment data (Miodrag et al 2012). The focus of research in the field of energy monitoring is on manufacturing processes which rarely considers intralogistic activities such as handling (Neugebauer et al 2011).…”

Section: Energy Monitoring In Sciencementioning

confidence: 99%

“…Due to companies' focus on global cooperation, vertical disintegration and the concentration on individual core competencies, the quantity of goods to be transported increased constantly by 1% -2% throughout the last decade (European Commission 2014), already reaching the maximum performance ability of many distribution centres (Chen, Paulraj 2004: 1;Miodrag et al 2012;Clausen et al 2013). According to Figure 1, more than 72% of all goods that are transported on land within Europe are handled by road transportation so that these significantly contribute to the congestion of logistic hubs.…”

Section: Introductionmentioning

confidence: 99%

“…Investigating the above mentioned phenomena, scientists highlight the importance for highly efficient tools and technologies in order to ease the bottle-necks of logistic distribution centres and transfer hubs in order to enhance the transportation and handling of goods. Amongst others, efficient factory planning (Mueller et al 2013b), performance and capacity utilization maximation by resource management (Miodrag et al 2012), technical and technological improvement (Bamberg et al 2012) or process monitoring (Wenzel, Bandow 2011) are considered as highly potential areas to improve material handling.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Status of Energy Monitoring in Science and Industry by the Example of Material Handling Processes

Fekete

Martin

Kuhn

et al. 2014

Business, Management and Education

View full text Add to dashboard Cite

Global mega trends attract increased resource preservation as well as system efficiency and arouse growing scientific, industrial and public attention, whereas process and technologic developments still lack realisation due to inaccurate knowledge of real process energy demand, associated possible savings and a low inducement for investment. This investigation develops a generally applicable process for energy data collection focussing on material handling systems based on a predefined target model for energy monitoring in order to generate a valid reference model. The introduction of Standardised Energy Consuming Activities (SECA) model enables the development of energy based process functions as reference for its implementation to energetic investigations in various industrial applications. Analysing current and target state of energy monitoring in scientific and industrial investigations for logistics shows the developmental deficit of standardisation and realisation in energy monitoring.

show abstract

Section: Energy Monitoring In Sciencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Status of Energy Monitoring in Science and Industry by the Example of Material Handling Processes

Fekete

Martin

Kuhn

et al. 2014

Business, Management and Education

View full text Add to dashboard Cite

show abstract

“…These defects result in the original data can not fully used in evaluation and predicting of the logistics activities, and the data are wasteful [1,2]. It is big problem need to be solved that how to fully inspire the parties involved in the logistics information system, analyze the various risk factors, correctly evaluate the influence of various factors, establish the quality evaluation model of logistics system management and achieve the maximum benefit [3].…”

Section: Introductionmentioning

confidence: 99%

Research on Logistic Management Quality Evaluation Based on Support Vector Machine

Deng¹,

Yu²,

Ning³

2016

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

View full text Add to dashboard Cite

Abstract. For making feasible logistics management come true, fully using data resources, and establishing in line with the development of a healthy enterprise logistics management mode, this paper puts forward a new analysis model for evaluating the quality level of logistics management based on support vector machine theory. By analyzing the influence factors in the logistics business process, the proposed model predicts management efficiency of logistics operations implementation, and provide theoretical support for logistics management system optimization. The support vector machine is of accuracy and efficiency, which is effective for the quality evaluation of logistics management.

show abstract

“…Many of them were centering on how to construct the reverse logistics system and operate the system efficiently. The performance evaluation of the RL management system was few discussed in the literatures at hand [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A New Logistic Management Quality Evaluation Method based on Support Vector Machine

Xu¹,

Fu²

2015

Advances in Computer Science Research

View full text Add to dashboard Cite

In order to achieve more effective logistics management, make full use of data resources, and build in line with the development of a healthy enterprise logistics management mode. A new analysis model for evaluating the quality level of logistics management based on support vector machine theory is put forward. Through analyzing the influence factors in the logistics business process, the proposed model forecasts management efficiency of logistics operations implementation, and provide theoretical support for logistics management system optimization. Compared with the BP neural network model, the support vector machine has a more accuracy and efficiency, which is feasible for the quality evaluation of logistics management.

show abstract

Improving Resource Management of Large Logistics Facilities

Cited by 3 publications

References 6 publications

The Status of Energy Monitoring in Science and Industry by the Example of Material Handling Processes

The Status of Energy Monitoring in Science and Industry by the Example of Material Handling Processes

Research on Logistic Management Quality Evaluation Based on Support Vector Machine

A New Logistic Management Quality Evaluation Method based on Support Vector Machine

Contact Info

Product

Resources

About