Papers by Jamal Ouenniche
Ranking of Bankruptcy Prediction Models Under Multiple Criteria
Advances in DEA Theory and Applications
Dea in Performance Evaluation of Crude Oil Prediction Models
Advances in DEA Theory and Applications
An Account of Dea-Based Contributions in the Banking Sector
Advances in DEA Theory and Applications
Dea Score Confidence Intervals with Past-Present and Past-Present-Future-Based Resampling
Advances in DEA Theory and Applications
A Value Tree for Multi-Criteria Evaluation of Sustainable Aviation Fuels
DEA-based Nash bargaining approach to merger target selection
European Journal of Operational Research
International Journal of Production Economics, 2021
Sustainable aviation fuels (SAF) provide a viable option to decarbonise global aviation. Unlike c... more Sustainable aviation fuels (SAF) provide a viable option to decarbonise global aviation. Unlike conventional jetfuel, SAFs can be produced in several production pathways making their selection a complex multi-criteria decision making (MCDM) problem with conflicting objectives. In this paper, we propose a multicriteria based framework for evaluating SAF production pathways, which is a sequential decision-making process with feedback adjustment mechanisms. Given the early stage of SAF technologies' development and the scarcity of data on such technologies, in this research, we involved a variety of aviation industry stakeholders to assist with data and preference gathering. Our MCDM framework is designed to be generic to provide flexibilities to potential users in choosing the appropriate implementation decisions for the relevant stakeholders. The strength of the proposed framework lays in its flexibility to accommodate various stakeholders' subjective judgements, choice of ranking method, and robustness of results. We used our MCDM framework within a stakeholders' participatory approach to rank order 11 SAF production pathways against 24 criteria grouped under social, environmental, economic, and technical impact categories. Our analysis revealed that the environmental and the economic impact categories are the most important ones followed by the technical and the social criteria; the gasification/Fischer-Tropsch (F-T) based production processes are preferred over fermentation and oil-based ones; and waste gases are the preferred feedstock along with wood-residue. These findings provide decision-makers with guidelines on the selection of SAF production pathways. ☆ We are grateful for constructive comments and suggestions from three anonymous reviewers and the editor of this journal. We would like to thank Dr. Michelle Carter at Knowledge Transfer Network (KTN) for her ongoing support, and all project partners for their valuable comments. We also appreciate for those who have participated in our online survey and attended our "Flightplan for Sustainable Aviation" Edinburgh workshop in 2019. The authors wish to acknowledge the financial support provided by the Engineering and Physical Sciences Research Council (EP/N009924/1).
Annals of Operations Research, 2019
Nowadays, business analytics has become a common buzzword in a range of industries, as companies ... more Nowadays, business analytics has become a common buzzword in a range of industries, as companies are increasingly aware of the importance of high quality predictions to guide their pro-active planning exercises. The financial industry is amongst those industries where predictive analytics techniques are widely used to predict both continuous and discrete variables. Conceptually, the prediction of discrete variables comes down to addressing sorting problems, classification problems, or clustering problems. The focus of this paper is on classification problems as they are the most relevant in risk-class prediction in the financial industry. The contribution of this paper lies in proposing a new classifier that performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new VIKOR-based classifier and out-of-sample predictions are devised with a CBR-based classifier trained on the risk class predictions provided by the proposed VIKOR-based class...
Annals of Operations Research, 2018
Data envelopment analysis (DEA) has witnessed increasing popularity in banking studies since 1985... more Data envelopment analysis (DEA) has witnessed increasing popularity in banking studies since 1985. In this paper, we propose a new DEA-based analysis framework with a regression-based feedback mechanism, where regression analysis provides DEA with feedback that informs about the relevance of the inputs and the outputs chosen by the analyst. Unlike previous studies, the DEA models used within the proposed framework could use both inputs and outputs, only inputs, or only outputs. So far, the UK banking sector remains relatively under researched despite its crucial importance to the UK economy. We use the proposed framework to address several research questions related to both the efficiency of the UK commercial banking sector and DEA analyses with and without regression-based feedback. Empirical results suggest that, on average, the commercial banks operating in the UK-whether domestic or foreign-are yet to achieve acceptable levels of overall technical efficiency, pure technical efficiency, and scale efficiency. On the other hand, DEA analyses with and without a linear regression-based feedback mechanism seem to provide consistent findings; however, in general DEA analyses without feedback tend to over-or underestimate efficiency scores depending on the orientation of the analyses. Furthermore, in general, a linear regression-based feedback mechanism proves effective at improving discrimination in DEA analyses unless the initial choice of inputs and outputs is well informed. Keywords Data envelopment analysis • Efficiency • UK commercial banks • DEA models without explicit inputs • DEA models without explicit outputs B Jamal Ouenniche
Expert Systems with Applications, 2018
On feature selection, as one of the critical steps to develop a distress prediction model (DPM), ... more On feature selection, as one of the critical steps to develop a distress prediction model (DPM), a variety of expert systems and machine learning approaches have analytically supported developers. Data envelopment analysis (DEA) has provided this support by estimating the novel feature of managerial efficiency, which has frequently been used in recent two-stage DPMs. As key contributions, this study extends the application of expert system in credit scoring and distress prediction through applying diverse DEA models to compute corporate market efficiency in addition to the prevailing managerial efficiency, and to estimate the decomposed measure of mix efficiency and investigate its contribution compared to Pure Technical Efficiency and Scale Efficiency in the performance of DPMs. Further, this paper provides a comprehensive comparison between two-stage DPMs through estimating a variety of DEA efficiency measures in the first stage and employing static and dynamic classifiers in the second stage. Based on experimental results, guidelines are provided to help practitioners develop two-stage DPMs; to be more specific, guidelines are provided to assist with the choice of the proper DEA models to use in the first stage, and the choice of the best corporate efficiency measures and classifiers to use in the second stage.
The Journal of Developing Areas, 2016
In practice, the diversity of management philosophies, quality programs, and quality tools has re... more In practice, the diversity of management philosophies, quality programs, and quality tools has resulted in many quality consulting firms to be established most often offering the same product under a different packaging. This continual repackaging of quality programs for marketing purposes has led to confusion and resulted in organizations often abandoning a specific quality program in favor of another, although programs could be complementary in nature. In an attempt to assist organizations in making informed decisions with respect to the choice of continuous quality improvement programs (CQIPs), we survey and critically analyze the landscape of research on CQIPs, highlight similarities and differences between the underlying quality philosophies, and discuss the limitations of the current generic designs of CQIPs; namely, Just-in-Time (JIT), Benchmarking, Kaizen, International Organization for Standardization (ISO), Business Process Reengineering (BPR), and Six Sigma. Our analysis of the literature revealed that, with the exception of Six Sigma, most published design and implementation procedures of CQIPs ignore a problem definition phase, most programs ignore performance measurement and evaluation as a formal phase along with the specification of the relevant criteria according to which performance is to be assessed, most programs lack the explicit integration of auditing, monitoring, control and feedback mechanisms, most published research on quality programs tend either to ignore the explicit integration of quality tools or to refer to a very limited number of potential tools without any guidelines as to which phases they could be used at, most continuous quality improvement programs lack a theoretical grounding in management theories as well as conceptual models, and no published research formally integrates critical success factors into the design methodology of a quality program. In this paper, we attempt to address this last methodological problem by proposing a classification of critical factors of CQIPs that could be used to assist managers in designing and customizing specific programs to their specific environments. In addition, we discuss the potential benefits of hybridization of quality philosophies and programs whereby several quality philosophies, concepts, programs, and tools are coherently integrated into a hybrid CQIP for the purpose of improving quality and reducing waste. Finally, we outline some future research directions.
Proceedings of the 24th International Academic Conference, Barcelona, 2016
In practice, investors, portfolio managers, and regulators continuously assess and monitor the pe... more In practice, investors, portfolio managers, and regulators continuously assess and monitor the performance of corporations. Such assessment and monitoring exercise is typically performed using a variety of tools including prediction models of distress. With the enormous number of prediction models, a strand of literature has focused on comparing the performance of alternative distress prediction models. In this research, we explore dynamic modelling and prediction frameworks of corporate distress and propose new ones. A dynamic evaluation framework is also proposed to assess the relative performance of these dynamic models in predicting corporate distress using a sample of UK firms listed on the London Stock Exchange (LSE).
Annals of Operations Research, 2017
Nowadays, data envelopment analysis (DEA) is a well-established non-parametric methodology for pe... more Nowadays, data envelopment analysis (DEA) is a well-established non-parametric methodology for performance evaluation and benchmarking. DEA has witnessed a widespread use in many application areas since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978. However, to the best of our knowledge, no published work formally addressed out-of-sample evaluation in DEA. In this paper, we fill this gap by proposing a framework for the out-of-sample evaluation of decision making units. We tested the performance of the proposed framework in risk assessment and bankruptcy prediction of companies listed on the London Stock Exchange. Numerical results demonstrate that the proposed out-of-sample evaluation framework for DEA is capable of delivering an outstanding performance and thus opens a new avenue for research and applications in risk modelling and analysis using DEA as a non-parametric frontier-based classifier and makes DEA a real contender in industry applications in banking and investment.
Prediction of corporate failure is one of the major activities in auditing firms risks and uncert... more Prediction of corporate failure is one of the major activities in auditing firms risks and uncertainties. The design of reliable models to predict bankruptcy is crucial for many decision making processes. Although a large number of models have been designed to predict bankruptcy, the relative performance evaluation of competing prediction models remains an exercise that is unidimensional in nature, which often leads to reporting conflicting results. In this research, we overcome this methodological issue by proposing an orientation-free super-efficiency data envelopment analysis model as a multi-criteria assessment framework. Furthermore, we perform an exhaustive comparative analysis of the most popular bankruptcy modeling frameworks for UK data including our own models. In addition, we address two important research questions; namely, do some modeling frameworks perform better than others by design? and to what extent the choice and/or the design of explanatory variables and their nature affect the performance of modeling frameworks?, and report on our findings.
Application of Feature Engineering with Classification Techniques to Enhance Corporate Tax Default Detection Performance
Advances in intelligent systems and computing, Dec 17, 2020
Energy Procedia, Feb 1, 2019
District heating networks are commonly addressed in the literature as one of the most effective s... more District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand-outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.
A new adaptive probabilistic broadcast scheme for vehicular networks
In VANETs, there are many applications that use broadcast communication as a fundamental operatio... more In VANETs, there are many applications that use broadcast communication as a fundamental operational tool, in disseminating information of interest to other road users under the umbrella of both safety and entertainment applications. Recently, the probabilistic broadcasting scheme is suggested as an efficient broadcast approach. Although a number of probabilistic schemes found in the literature, they still suffer from a high level of rebroadcast redundancy, which often leads to the Broadcast Storm Problem (BSP). Thus, in this paper a new efficient probabilistic broadcast scheme is developed, to target both achieving a high delivery ratio and reducing broadcast redundancy. Using simulation experiment, we compared the performance of the proposed scheme against the recent well known probabilistic schemes and our results confirm the superiority of our scheme over existing schemes in terms of key performance metrics, namely Reachability (RE) and Saved Rebroadcast (SR).
International Transactions in Operational Research
In this paper, we address the problem of the ranking of companies based on their diversity and fi... more In this paper, we address the problem of the ranking of companies based on their diversity and financial performance. The addressed problem is a multiple criteria decision-making problem where a composite measure needs to be obtained to rank firms. Taking as a reference the methodological approach followed by Refinitiv in the construction of their Diversity and Inclusion Index, we propose an alternative ranking framework that overcomes some of the problems identified in the methodological approach of Refinitiv. In particular, the proposed method in this work does not require the a priori establishment of a weighting scheme and is able to incorporate the past behavior of the companies in terms of diversity in their workplaces.
Journal of Behavioral and Experimental Finance, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Design of Hybrid Metaheuristics for Vehicle Routing Problems
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Papers by Jamal Ouenniche