Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet, there is a challenge to select the best Web service which can offer more quality-of-service (QoS) values at the lowest price. Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet. In this paper, we modify the interval data envelopment analysis (DEA) models [Wang, Greatbanks and Yang (2005)] for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors. We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models. The experimental results show that the correlation between the proposed models and the interval DEA models is significant. Also, the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations. Finally, we demonstrate the usefulness of the proposed models for QoS-aware Web service composition. Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase. These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the marke

Evaluation and Improvement of the Efficiency of Logistics Companies with Data Envelopment Analysis Model

The performance of global trade depends on the logistics industry to move products, information, finances, technology and human resources along the supply chain. The current situation during the pandemic relies on the logistics industry particularly in the courier, parcel and express service providers to deliver daily essentials. Product customization, customer demand, technological sophistication, threat of new entrants, border closure, compliance to Covid-19 regulations and global economic crisis have taken the logistics industry by storm. For the sustainment and growth of these companies, strategic decision making shall take place. A huge determinant of these decisions is the financial efficiency of the companies. Therefore, this paper aims to determine the efficiency of the logistics companies in Malaysia by analyzing their financial performances using current ratio, debt to assets ratio, debt to equity ratio, earnings per share, return on assets and return on equity with data envelopment analysis model. The results of this study found that five companies, COMPLET, GDEX, MISC, SURIA and WPRTS are efficient. This study fills the research gap by determining the efficiency scores of these companies and suggesting potential improvements for inefficient companies to enhance and optimize their financial positions

Defuzzification of groups of fuzzy numbers using data envelopment analysis

Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships

A state-of-art survey on TQM applications using MCDM techniques

In today’s competitive economy, quality plays an essential role for the success business units and there are considerable efforts made to control and to improve quality characteristics in order to satisfy customers’ requirements. However, improving quality is normally involved with various criteria and we need to use Multi Criteria Decision Making (MCDM) to handle such cases. In this state-of the-art literature survey, 45 articles focused on solving quality problems by MCDM methods are investigated. These articles were published between 1994 and 2013.Seven areas were selected for categorization: (1) AHP, Fuzzy AHP, ANP and Fuzzy ANP, (2) DEMATEL and Fuzzy DEMATEL, (3) GRA, (4) Vikor and Fuzzy Vikor, (5) TOPSIS, Fuzzy TOPSIS and combination of TOPSIS and AHP, (6) Fuzzy and (7) Less frequent and hybrid procedures. According to our survey, Fuzzy based methods were the most popular technique with about 40% usage among procedures. Also AHP and ANP were almost 20% of functional methods. This survey ends with giving recommendation for future researches

Business analytics adoption and technological intensity: An efficiency analysis

Despite the overwhelming popularity of business analytics (BA) as an evidence-based decision support mechanism, the impact of its adoption on organizational performance has received scant attention from the research community. This study aims to unfold the adoption efficiencies of BA and its applications by proposing a data envelopment analysis (DEA) methodology to holistically assess the underlying factors with respect to the level of achievement regarding organizational performance, operational performance, and financial performance. Furthermore, the study unveils the firm-level and sectoral-level discrepancies in BA adoption efficiency in different industry settings. Relying on survey data obtained from 204 executives in various industries, this study provides empirical support for the cross-industry differences in BA adoption efficiencies. The results show that the firms in low-tech industries seem to achieve the highest efficiency from adopting BA regarding its influence on firm performance

Application of Multicriteria Decision-Making Methods in Railway Engineering: A Case Study of Train Control Information Systems (TCIS)

In order to improve its position in the transport market railway, as a complex system, it has to fulfill a number of objectives such as increased capacity and asset utilization, improved reliability and safety, higher customer service levels, better energy efficiency and fewer emissions, along with increased economic viability and profits. Some of these objectives call for the implementation of maximum values, while some of them require minimum values. Additionally, some can be expressed quantitatively, while some, for example, customer service, can be described qualitatively through a descriptive scale of points. The application of MCDM in railway engineering can play a significant role. Therefore, the major objective of this chapter is the review of the application of MCDM methods in railway engineering. As one of the means in achieving the objectives of railways and above all the utilization of capacity are Train Control Information Systems (TCIS). Based on that, the aim of this chapter is the evaluation of the efficiency of TCIS in the improvement of railway capacity utilization through defined technical-technological indicators. The non-radial Data Envelopment Analysis (DEA) model for the evaluation of TCIS efficiency in improvement of utilization of railway capacity using the selected indicators is proposed. The proposed non-radial DEA model for TCIS efficiency evaluation in using railway capacity could be applied to an overall network or for separate parts of railway lines

An Investigation into Factors Affecting the Chilled Food Industry

With the advent of Industry 4.0, many new approaches towards process monitoring, benchmarking and traceability are becoming available, and these techniques have the potential to radically transform the agri-food sector. In particular, the chilled food supply chain (CFSC) contains a number of unique challenges by virtue of it being thought of as a temperature controlled supply chain. Therefore, once the key issues affecting the CFSC have been identified, algorithms can be proposed, which would allow realistic thresholds to be established for managing these problems on the micro, meso and macro scales. Hence, a study is required into factors affecting the CFSC within the scope of Industry 4.0. The study itself has been broken down into four main topics: identifying the key issues within the CFSC; implementing a philosophy of continuous improvement within the CFSC; identifying uncertainty within the CFSC; improving and measuring the performance of the supply chain. However, as a consequence of this study two further topics were added: a discussion of some of the issues surrounding information sharing between retailers and suppliers; some of the wider issues affecting food losses and wastage (FLW) on the micro, meso and macro scales. A hybrid algorithm is developed, which incorporates the analytic hierarchical process (AHP) for qualitative issues and data envelopment analysis (DEA) for quantitative issues. The hybrid algorithm itself is a development of the internal auditing algorithm proposed by Sueyoshi et al (2009), which in turn was developed following corporate scandals such as Tyco, Enron, and WorldCom, which have led to a decline in public trust. However, the advantage of the proposed solution is that all of the key issues within the CFSC identified can be managed from a single computer terminal, whilst the risk of food contamination such as the 2013 horsemeat scandal can be avoided via improved traceability

Integrating data envelopment analysis and balanced scorecard for improving organizations’ performance assessment

In today's business environment, organizations aim to improve their performance to compete efficiently in a highly competitive global market. Thus, the concept of performance measurement has received significant attention from both academics and practitioners. It has been recognized that performance measurement should take into consideration all aspects of the organization and reflect the organization’s multidimensional nature, including both financial and non-financial factors. Consequently, the Balanced Scorecard (BSC) has been developed to address such a need. Applying the BSC is changing the way top managers administer their organizations and would require them to devote adequate attention to both financial and non-financial aspects, both internally and externally. Although the BSC has been applied in various areas, there are some pitfalls associated with using it as a tool for evaluating organization performance. The criticisms include first, the fact that BSC lacks a formal implementation methodology; second, adopting a broad set of interrelated indicators may lead to information overload and cause complicated optimization problems; third, BSC does not possess the ability to specify a common scale of measurement; fourth, it does not have a standardized baseline or benchmark required to distinguish between different organization’ performance; and fifth, BSC does not include a mathematical model or a weighting scheme. Recent studies suggest that these limitations can be reduced by combining BSC with other techniques such as Data Envelopment Analysis (DEA), as these two techniques complement each other. The purpose of this thesis is to develop an improved performance assessment framework by combining BSC and DEA approaches to assess organizations’ performance and then applying this model to assess these organizations’ efficiency levels. The targeted population is all organizations traded on the London Stock Exchange and included in the FTSE All-Share Index, and secondary data are obtained from the financial statements published in the “DataStream” database. The final data set used for the current study consists of 307 organizations covering a period of five years, from 2012 to 2016. The study also adds to the extant literature by conducting cross-industry level analysis using the combined DEA-BSC model. Hence, it provides managers in different industries with insight to evaluate organizations’ efficiency level to improve their competitive plans and long-term objectives. The findings of the study suggest that for the seven different industries included in the analysis, the financial perspective of BSC has the greatest effect on the efficiency levels of thexvorganizations. Additionally, the findings provide an overview of the stability status of each industry by examining the efficiency scores for each industry over the five-year period. The findings provide a broader time horizon and take into account changes that happened in organisations’ performance outcomes over time. Furthermore, the results of the analysis categorize organizations in terms of the level of efficiency, identify the possible reasons for such inefficiencies in performance, and provide guidance on potential improvements