A method using weight restrictions in data envelopment analysis for ranking and validity issues in decision making

In this article we introduce a comprehensive yet efficient approach based on data envelopment analysis (DEA) with restricted multipliers for accountable and understandable multiple attribute decision making (MADM). Information system (IS) appraisals are motivated and used for illustrating the proposed methodology. Results show that the given DEA based approach can easily and significantly increase the information frame of the decision maker by identifying disparate rankings and by affirming the stability and validity of ranking outcomes. The given validity concept is contrary to the directions given in the main body of research and can also be used to question ranking outcomes of classic MADM methods.     

GPROC—an integrated system for the processing of numerical scientific data

Work done on an interactive graphical processing system is described. An overview is given of the uses to which a system could be put, with especial reference to aspects of solid state physics. Algorithms which may be usefully included in such a system, when used in a technical scientific environment, are described. The problems of implementing such a system on small microcomputer systems are outlined, and some possible areas of future development are discussed.     

Recruitment and selection processes through an effective GDSS

This study proposes a group decision support system (GDSS), with multiple criteria to assist in recruitment and selection (R&S) processes of human resources. A two-phase decision-making procedure is first suggested; various techniques involving multiple criteria and group participation are then defined corresponding to each step in the procedure. A wide scope of personnel characteristics is evaluated, and the concept of consensus is enhanced. The procedure recommended herein is expected to be more effective than traditional approaches. In addition, the procedure is implemented on a network-based PC system with web interfaces to support the R&S activities. In the final stage, key personnel at a human resources department of a chemical company in southern Taiwan authenticated the feasibility of the illustrated example.     

Investigating the buying process for the introduction of data processing in small and medium-sized firms

Analysing the buying process and the post-purchase experiences may be of interest to both the supplier and potential buyers. The buying behaviour during the introduction of data processing and computer-assisted information processing in 69 small and medium-sized firms were investigated using a standardized questionnaire. For all firms, the decision to buy involved a decision to obtain a system, with unity of hardware, system software, and application software — thus software support had the major influence on the decision. The assumption that in producer markets buyers decide more rationally and with greater market transparency only holds true for so called “intensive decision-makers”. This subset is the result of a cluster analysis that groups the firms according to their decision-making behaviour. A comparison of the “intensive decision-makers” with all others on their post-purchase development of computer-assisted information processing presents the following finding: Over an equal time span, the “intensive decision-makers” achieved a higher level of information processing (as measured by the number of DP-applications installed, the possibility of on-line operation of the firms' departments, and the creation of integrated solutions) than the rest of the sample.     

The use of PCA and signal processing techniques for processing time-based construction settlement data of road embankments

Instrumentation is beneficial in civil engineering for monitoring structures during their construction and operation. The data collected can be used to observe real-time response and develop data-driven models for predicting future behaviour. However, a limited number of sensors are usually used for on-site civil engineering construction due to cost restrictions and practicalities. This results in relatively small raw datasets, which often contain errors and anomalies. Interpreting and making judicious use of the available dataset for developing reliable predictive model represents a significant challenge. Therefore, it is essential to pre-process and clean the data for improving their quality. To date, little investigation has been performed in the application of such data cleaning methods to geotechnical engineering datasets collected from full-scale sites. The purpose of this study is to apply simple and effective data pre-processing techniques to site-data collected from a highway embankment constructed on a sequence of soil layers of different physical make-up and non-linear consolidation characteristics. Various cleaning methods were applied to magnetic extensometer data collected for monitoring settlement within foundation soils beneath the embankment. PCA was used to explore raw data, identify and remove outliers. Numerous filtering and smoothing methods were used to clean noise in the data and their results were further compared using RMSE and NMSE. The methods adopted for data pre-processing and cleaning proved very effective for capturing the raw settlement behaviour on site. The findings from this study would be useful to site engineers regarding complex decision-making relating to ground response due to embankment construction. This also has positive prospects for developing dynamic prediction models for embankment settlement.