The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

i-FRAME – Assessing impacts of social policy innovation in the EU: Proposed methodological framework to evaluate socio-economic returns on investment of social policy innovations

This report presents the final proposal for developing a methodological framework to assess the impacts generated by social policy innovations which promote social investment in the EU, in short i-FRAME. This framework has the objective to provide a structured approach that shall serve as a comprehensive framework for conducting analysis of the economic and social returns on investments of social policy innovations. It also aims to act as a guide to gather insights into replicability and transferability of initiatives which promote social investment across the EU. The report outlines the reviewed and improved theoretical and methodological approach developed by the JRC with help from external experts, and validated by testing the operational components proposed on a number of case studies and scenarios of use. After outlining the conceptual and methodological approach underpinning the i-FRAME (V1.0), the report discusses the proposal for building its operational components according to a structured theoretical framework of a dynamic simulation model for social impact assessment (V1.5). The final proposal for i-FRAME (V2.0) and an overview of the operational components for its implementation are then presented discussing the key elements that should be developed to build a comprehensive i-FRAME Web-Platform and simulator for social impact assessment. Conclusions are then offered in terms of implications for policy and directions for future research. These were drawn after consulting experts from different research disciplines, practitioners and representatives of relevant stakeholders and policymakers, and they include .recommendations for further developing the operational components proposed, paving the way towards building the i-FRAME (V3.0) and beyond.JRC.B.4-Human Capital and Employmen

A multicriteria approach to manage credit risk under strict uncertainty

Assessing the ability of applicants to repay their loans is generally recognized as a critical task in credit risk management. Credit managers rely on financial and market information, usually in the form of ratios, to estimate the quality of credit applicants. However, there is no guarantee that a given set of ratios contains the information needed for credit classification. Decision rules under strict uncertainty aim to mitigate this drawback. In this paper, we propose the use of a moderate pessimism decision rule combined with dimensionality reduction techniques and compromise programming. Moderate pessimism ensures that neither extreme optimistic nor pessimistic decisions are taken. Dimensionality reduction from a set of ratios facilitates the extraction of the relevant information. Compromise programming allows to find a balance between quality of debt and risk concentration. Our model produces two critical outputs: a quality assessment and the optimum allocation of funds. To illustrate our multicriteria approach, we include a case study on 29 firms listed in the Spanish stock market. Our results show that dimensionality reduction contributes to avoid redundancy and that quality-diversification optimization is able to produce budget allocations with a reduced number of firms

A multicriteria approach to manage credit risk under strict uncertainty

[EN] Assessing the ability of applicants to repay their loans is generally recognized as a critical task in credit risk management. Credit managers rely on financial and market information, usually in the form of ratios, to estimate the quality of credit applicants. However, there is no guarantee that a given set of ratios contains the information needed for credit classification. Decision rules under strict uncertainty aim to mitigate this drawback. In this paper, we propose the use of a moderate pessimism decision rule combined with dimensionality reduction techniques and compromise programming. Moderate pessimism ensures that neither extreme optimistic nor pessimistic decisions are taken. Dimensionality reduction from a set of ratios facilitates the extraction of the relevant information. Compromise programming allows to find a balance between quality of debt and risk concentration. Our model produces two critical outputs: a quality assessment and the optimum allocation of funds. To illustrate our multicriteria approach, we include a case study on 29 firms listed in the Spanish stock market. Our results show that dimensionality reduction contributes to avoid redundancy and that quality-diversification optimization is able to produce budget allocations with a reduced number of firms.Pla Santamaría, D.; Bravo Selles, M.; Reig-Mullor, J.; Salas-Molina, F. (2021). A multicriteria approach to manage credit risk under strict uncertainty. Top. 29(2):494-523. https://doi.org/10.1007/s11750-020-00571-0S494523292Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Reviews Comput Stat 2(4):433–459Adams W, Einav L, Levin J (2009) Liquidity constraints and imperfect information in subprime lending. Am Econ Rev 99(1):49–84Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23(4):589–609Altman EI, Iwanicz-Drozdowska M, Laitinen EK, Suvas A (2016) Financial and non-financial variables as long-horizon predictors of bankruptcy. J Credit Risk 12(4):49–78Angilella S, Mazzù S (2015) The financing of innovative smes: a multicriteria credit rating model. Eur J Oper Res 244(2):540–554Armor DJ (1973) Theta reliability and factor scaling. Sociol Methodol 5:17–50Avery RB, Bostic RW, Calem PS, Canner GB (2000) Credit scoring: statistical issues and evidence from credit-bureau files. Real Estate Econ 28(3):523–547Ballestero E (2002) Strict uncertainty: a criterion for moderately pessimistic decision makers. Decis Sci 33(1):87–108Ballestero E (2006) Ranking alternatives from the decision maker’s preferences: an approach based on utility and the notion of marginal action. J Oper Res Soc Jpn 49(1):49–65Ballestero E, Pla-Santamaria D (2004) Selecting portfolios for mutual funds. Omega 32(5):385–394Ballestero E, Romero C (1998) Multiple criteria decision making and its applications to economic problems. Kluwer Academic Publishers, DordrechtBallestero E, Günther M, Pla-Santamaria D, Stummer C (2007) Portfolio selection under strict uncertainty: a multi-criteria methodology and its application to the frankfurt and vienna stock exchanges. Eur J Oper Res 181(3):1476–1487Bartlett MS (1937) Properties of sufficiency and statistical tests. Proc R Soc Lond Ser A Math Phys Sci 160(901):268–282Bellman R (1957) Dynamic programming. Princeton University Press, New JerseyBravo M, Pla-Santamaria D (2012) Evaluating loan performance for bank offices: a multicriteria decision-making approach. Inf Syst Oper Res 50(3):127–133Breeden JL (2016) Incorporating lifecycle and environment in loan-level forecasts and stress tests. Eur J Oper Res 255(2):649–658Carmines EG, Zeller RA (1979) Reliability and validity assessment. Quantitative applications in the social sciences. SAGE, Thousand OaksCerny BA, Kaiser HF (1977) A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivar Behav Res 12(1):43–47Chen M-Y (2014) Using a hybrid evolution approach to forecast financial failures for taiwan-listed companies. Quant Financ 14(6):1047–1058Conway JM, Huffcutt AI (2003) A review and evaluation of exploratory factor analysis practices in organizational research. Organ Res Methods 6(2):147–168Doumpos M, Zopounidis C (2011) A multicriteria outranking modeling approach for credit rating. Decis Sci 42(3):721–742Edelberg W (2006) Risk-based pricing of interest rates for consumer loans. J Monet Econ 53(8):2283–2298Einav L, Jenkins M, Levin J (2012) Contract pricing in consumer credit markets. Econometrica 80(4):1387–1432Einav L, Jenkins M, Levin J (2013) The impact of credit scoring on consumer lending. Rand J Econ 44(2):249–274Elisabetsky R (1976) Um modelo matemático para decisões de crédito no banco comercial. PhD thesis, Dissertação (Mestrado)–Escola Politécnica, Universidade de São Paulo, São PauloEriksson K, Jonsson S, Lindbergh J, Lindstrand A (2014) Modeling firm specific internationalization risk: an application to banks’ risk assessment in lending to firms that do international business. Int Bus Rev 23(6):1074–1085Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ (1999) Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods 4(3):272Falangis K (2007) The use of MSD model in credit scoring. Oper Res Int J 7(3):481–503Ford JK, MacCallum RC, Tait M (1986) The application of exploratory factor analysis in applied psychology: a critical review and analysis. Pers Psychol 39(2):291–314Freeman L, Hamilton D (2002) A dream deferred or realized: the impact of public policy on fostering black homeownership in new york city throughout the 1990’s. Am Econ Rev 92(2):320–324George W, Snedecor CWG (1989) Statistical methods. Iowa State University Press, IowaJory SR, Ngo TN, Wang D (2016) Credit ratings and the premiums paid in mergers and acquisitions. J Empir Financ 39:93–104Kaiser HF (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23(3):187–200Kaiser HF (1974) An index of factorial simplicity. Psychometrika 39(1):31–36Kanitz S (1974) Como prever falencias de empresas. Revista de Negocios Em Exame, Dezembro, pp 95–102Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms. Wiley, New YorkKealhofer S (1993) Portfolio management of default risk. KMV Corporation, San FranciscoLaplace P-S (1825) Essai philosophique sur les probabilités. H. Remy, ParisLegault, J. and Score, A. (1987). Ca score. warning system for small business failures. Bilanas, June, pages 29–31Li H, Hong L-Y, Zhou Q, Yu H-J (2015) The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation. Int J Syst Sci 46(11):2072–2086Magee S (2013) The effect of foreign currency hedging on the probability of financial distress. Account Financ 53(4):1107–1127McQuown J (1993) Market versus accounting-based measures of default risk. KMV Corporation, San FranciscoMencía J (2012) Assessing the risk-return trade-off in loan portfolios. J Bank Financ 36(6):1665–1677Merton RC (1974) On the pricing of corporate debt: the risk structure of interest rates. J Financ 29(2):449–470Mester LJ (1997) What’s the point of credit scoring? Bus Rev 3(Sep/Oct):3–16Nunnally JC, Bernstein I (1994) Psychometric theory (McGraw-Hill Series in Psychology), vol 3. McGraw-Hill, New YorkSaunders D, Xiouros C, Zenios SA (2007) Credit risk optimization using factor models. Ann Oper Res 152(1):49–77Sengupta R, Bhardwaj G (2015) Credit scoring and loan default. Int Rev Financ 15(2):139–167Shi J, Xu B (2016) Credit scoring by fuzzy support vector machines with a novel membership function. J Risk Financ Manag 9(4):13Sirignano J, Giesecke K (2019) Risk analysis for large pools of loans. Manag Sci 65(1):107–121Sirignano JA, Tsoukalas G, Giesecke K (2016) Large-scale loan portfolio selection. Oper Res 64(6):1239–1255Spector PE (1992) Summated rating scale construction: An introduction, vol 82. Sage, Newbury ParkSpringate GL (1978) Predicting the possibility of failure in a Canadian firm: a discriminant analysis. PhD thesis, Simon Fraser UniversityThomas LC (2009) Consumer credit models: pricing, profit and portfolios: pricing, profit and portfolios. Oxford University Press, OxfordVasicek O (1984) The philosophy of credit valuation: the credit valuation model. KMV Corporation, San FranciscoWald A (1950) Statistical decision functions. Wiley, New YorkWang J, Wang J (2015) Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing 156:68–78Weber O (2012) Environmental credit risk management in banks and financial service institutions. Bus Strate Environ 21(4):248–263Woerheide W, Persson D (1992) An index of portfolio diversification. Financ Serv Rev 2(2):73–85Yu P-L (1985) Multiple-criteria decision making: concepts, techniques, and extensions. Plenum Press, New YorkZeleny M (1973) Compromise programming. In: Cochrane JL, Zeleny M (eds) Multiple criteria decision making. University of South Carolina Press, Columbia, pp 262–301Zeleny M (1982) Multiple criteria decision making. McGraw-Hill, New Yor

Explanatory Factors of Business Failure: Literature Review and Global Trends

This study aims to provide a bibliometric analysis of business failure research, recognise the main existing research topics and establish future research challenges. The results, based on a sample of 588 articles, show that the number of published papers and citations has grown steadily, especially in the last 14 years. The most productive and relevant journals, countries, institutions and authors are presented using bibliometric performance indicators. In addition, through the graphical mapping of strategic diagrams, this study identifies the most significant research trends and proposes several directions for future research. The results of this research may be helpful for beginner researchers and experts in business failure, as they contribute to bringing clarity to this line of investigation. These results reveal all the aspects involved in business failure research, analysing its temporal and methodological characterisation, and the most prolific authors who have participated in its study (see, i.e., H. Li), leading journals (see, i.e., Expert Systems with Applications) or academic institutions that have headed the scientific analysis of this business phenomenon. Likewise, it has been possible to identify three main areas in which the research on business failure has been focused: business, management and accounting; economics, econometrics and finance; and social sciences. In addition, a complete, synthesised and organised summary of the various definitions, perspectives and research trends are presented

The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

Shifting liabilities and logics of decision-making: the political economy of disaster risk financing

The objective of this research is to analyse the political economy of disaster risk financing (DRF). DRF is an umbrella term for a set of policy mechanisms which enable response agencies to respond earlier to disasters, based on a measure of disaster risk, pre-arranged finance and plans, and a mechanism to trigger response. DRF is a varied policy landscape and in this thesis, I address mechanisms which allow agencies to act in advance of disasters occurring, as well as those which aim to respond earlier to disasters which have already occurred. What they have in common is linking a measure of risk – whether that is a forecast or a proxy measurement for a hazard - to a trigger which enacts a response. Proponents argue that DRF will result in more efficient and effective response and is therefore one way to ‘square the circle’ of shortfalls in humanitarian financing. DRF has been gaining traction and momentum for some time, and 2021 was a watershed year when key donors significantly scaled up their commitments to DRF. Critically though, because these mechanisms are designed to enact a response based on measures of risk, rather than existing humanitarian need, they pose a challenge for policymakers and practitioners because of the potential for acting erroneously or for missing events. DRF therefore opens up important questions about decision-making, mandates and liability. However, there is currently a dearth of critical literature about DRF, and almost no social science-based literature which analyses these mechanisms as part of a wider policy landscape. As key donors and response agencies are significantly scaling up funding through DRF, it is imperative that we understand the implications of this move towards enacting a response based on measures of risk, rather than existing need, which poses a potentially momentous shift in the liabilities and logics of disaster response agencies. Based on expert interviews, participant observation and desk-based document analysis, this thesis makes an original contribution to understanding the political economy of DRF. It explores how politics, mandates and questions of liability are shaping DRF mechanisms, and analyses the politics enacted by DRF, outlining how risk is operationalised as a calculative logic which is taking shape as a novel form of biopolitics within the humanitarian sector

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains