Said Gattoufi

A Taxonomy for Data Envelopment Analysis

This paper presents a scheme for classifying the Data Envelopment Analysis (DEA) literature. The taxonomy allows one to distinguish articles on the basis of the data source used (D) if any, the type of envelopment (E) invoked, the approach to analysis (A) used, and the nature (N) of the paper. Each of the above attributes D, E, A and N (DEAN) are further subdivided to obtain a detailed description of each article comprising this rather wide-ranging field of knowledge. Sample articles are classified to illustrate the descriptive power and the parsimony of this taxonomy.

Epistemology of data envelopment analysis and comparison with other fields of OR/MS for relevance to applications

Periodically, professionals in a given field must reflect and assess where the field has been, where it is heading, and what if anything, should be done to change that fields course. This article discusses statistical trends within the Data Envelopment Analysis (DEA) literature. The number of articles published per year in refereed journals over the entire lifespan of the field, authorship and publishing outlets-of-choice statistics are used to indicate DEAs vitality, relevance, diffusion to other disciplines/professions and its worldwide acceptance. Lastly, based on published meta reviews, comparisons are made with other OR/MS sub-disciplines.

A maximum discrimination DEA method for ranking association rules in data mining

A data mining algorithm, such as Apriori, discovers a huge number of association rules (ARs) and therefore efficiently ranking all these rules is an important issue. This paper suggests a data envelopment analysis (DEA) method for ranking the discovered ARs using a maximum discrimination between the interestingness criteria defined for all ARs. It is shown that the proposed DEA model has a unique optimal solution which can be computed efficiently when the maximum discrimination between the criteria, the difference between DEA weights, is considered. The contribution of this study can be explained as follows: First, we show that using the conventional DEA model for ranking ARs may produce an invalid result because the weights corresponding to interestingness criteria would not discriminate between the criteria. This is investigated for a dataset consisting of 46 ARs with four criteria, namely support, confidence, itemset value and cross-selling. The paper also introduces the maximum discrimination between the weights of the criteria and obtains the optimal solution of the corresponding DEA model efficiently without the need of solving the related mathematical models. On the other hand, this model concludes less number of useful rule(s). A comparative analysis is then used to show the advantage of the proposed DEA method.

Smallest Position Value Approach

In a traveling salesman problem, if the set of nodes is divided into clusters for a single node from each cluster to be visited, then the problem is known as the generalized traveling salesman problem (GTSP). Such problem aims to find a tour with minimum cost passing through only a single node from each cluster. In attempt to show how a continuous optimization algorithm can be used to solve a discrete/combinatorial optimization problem, this chapter presents a standard continuous differential evolution algorithm along with a smallest position value (SPV) rule and a unique solution representation to solve the GTSP. The performance of the differential evolution algorithm is tested on a set of benchmark instances with symmetric distances ranging from 51 (11) to 442 (89) nodes (clusters) from the literature. Computational results are presented and compared to a random key genetic algorithm (RKGA) from the literature.

Understanding and predicting the quality determinants of e-government services: A two-staged regression-neural network model

Purpose – The purpose of this paper is to investigate the quality determinants influencing the adoption of e-government services in Oman and compare the performance of multiple regression and neural network models in identifying the significant factors influencing adoption in Oman. Design/methodology/approach – Primary data concerning service quality determinants and demographic variables were collected using a structured questionnaire survey. The variables selected in the design of the questionnaire were based on an extensive literature review. Factor analysis, multiple linear regression and neural network models were employed to analyze data. Findings – The study found that quality determinants: responsiveness, security, efficiency and reliability are statistically significant predictors of adoption. The neural network model performed better than the regression model in the prediction of e-government services’ adoption and was able to characterize the non-linear relationship of the aforementioned predic...

AL-SHIFA HEALTHCARE INFORMATION SYSTEM IN OMAN: A DEBATABLE IMPLEMENTATION SUCCESS

This paper presents a case study of the project management of Al‐Shifa healthcare information system (HIS) in Oman. In addition to the reports about the project made available to the researchers, the focus group methodology was used for collecting the relevant information from the main projects stakeholders. The paper describes first the evolution of the implementation of an HIS tailored by the Ministry of Health (MOH) in Oman to fit the needs of the healthcare units under its supervision. The case analysis that constitutes the second element in the paper is made based on the Project Management approach developed by the Project Management Institute (PMI). As a matter of fact, the authors found that there was no formal project management approach adopted by the MOH for the development and implementation of the hereto‐mentioned HIS project. Moreover, the project has experienced a chronic creep in terms of features, cost and time‐schedule. The recommendations of the authors for the rescue of the project from its current dilemma, consist of technological, administrative and human resource development actions.

Minor and major consolidations in inverse DEA

Defining a major or a minor consolidation when merging two entities.Proposing a novel method to identify a merger is a major or a minor consolidation.Developing a new inverse Data Envelopment Analysis for merging.Applying the proposed model in real data from banks.Providing insight that the same method can be used in any industries. Many production systems have acquisition and merge operations to increase productivity. This paper proposes a novel method to anticipate whether a merger in a market is generating a major or a minor consolidation, using Inverse data envelopment analysis (InvDEA) model. A merger between two or more decision making units (DMUs) producing a single merged DMU that affects the efficiency frontier, defined by the pre-consolidation market conditions, is called a major consolidation. The corresponding alternative case is called a minor consolidation. A necessary and sufficient condition to distinguish the two types of consolidations is proven and two numerical illustrations in banking and supply chain management are discussed. The crucial importance of anticipating the magnitude of a consolidation in a market is outlined.

Choosing the best twenty20 cricket batsmen using ordered weighted averaging

This paper provides a way to evaluate and choose the best Twenty20 (T20) cricket batsmen using ordered weighted averaging (OWA) operator. The paper suggests an OWA evaluation method for a cricket batsman using multiple attributes related to the player’s performance. A dataset consisting of ten important attributes for 24 T20 Indian Premier League 4 (IPL 4) cricket batsmen is used to choose the best players. Also, a comparison result shows the advantage of using the proposed OWA method. The proposed method in this paper can also be used for evaluation of T20 bowlers in turn in the selection of a T20 cricket team.

The productivity of Omani banks: a data envelopment analysis approach

The aims of this research are to analyse the performances of Omans banking sector and do a comparative analysis for its different units. The analysis is based on the assessment of the relative efficiency of the commercial banks operating in Oman. Data Envelopment Analysis (DEA) was used to evaluate the relative efficiency of the commercial banks in Oman. Panel data for the period of 2000-2005 were used to conduct the analysis. The intermediation theory serves as the theoretical background. The DEA provides the Technical Efficiency (TE) score for each bank per year. The TE scores are then decomposed into Pure Technical Efficiency (PTE) and Scale Efficiency (SE). In line with previous similar studies, the results suggest that the banking system in Oman, though performing proportionally to the size of its units, shows signs of pure technical inefficiency. Furthermore, the relatively high average TE score (90.4%) indicates the oligopolistic nature of Omans banking sector. The findings also indicate that the local commercial banks outperform their foreign counterparts, confirming the home-country advantage suggested in the corporate cross-border activity literature. Finally, large banks are found to out perform their medium and small counterparts.

Contextual Decision Making for Cancer Diagnosis

Pathologist needs to routinely make management decisions about patients who are at risk for a disease such as cancer. Although, making a decision for cancer diagnosis is a dificult task since it context dependent. The term context contains a large number of elements that limits strongly any possibility to automatize this. The decision-making the process being highly contextual, the decision support system must benefit from its interaction with the expert to learn new practices by acquiring missing knowledge incrementally and learning new practices, it is called in deferent research human/doctor in the loop, and thus enriching its experience base.