A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).
Deadline for manuscript submissions: 30 April 2021.
Dear Colleagues,
Many of the issues we are facing today, such as the COVID-19 pandemic, public health, climate change, inequalities, digitalization (e.g., Industry 4.0), job obsolescence, and habitat destruction, are ill-defined, multidimensional and complex. Most real-world problems are 'wicked' in nature (Churchman, 1967; Crul, 2014), also called 'ill-structured' (Simon, 1973) or 'complex' (Frensch and Funke, 1995; Sternberg and Frensch, 1991). The COVID-19 pandemic, for example, is an unprecedented sanitary disruption to our way of life around the world, which has caused the global economy to slow down and led to confusion in most of our institutions. The interwoven relationships between the economic, social, and environmental dimensions make most decision and policy problems wicked. The decision problems, thus, are usually characterized by their vagueness, fluidity, competing value systems, multifaceted ramifications, intractability, competing objectives, and unconventional solutions. Wicked problems are prevalent in real-world social, economic, and environmental systems because their novel and disruptive dynamics are not effectively accommodated by unidimensional, stable and linear causal mechanisms (McMillan and Overall, 2016; Waddock, Meszoely, Waddell, and Dentoni, 2015).
The concept of multidimensional risks refers to settings where consequences in one domain can have impacts across other domains. For instance, exposure to a pandemic such as COVID-19, combined with vulnerability in health systems and supply chains, significantly increases the risk of global economic and social disruptions. The types of different categories of risk stress the importance of understanding the interconnectedness, synergies, and complexity of multidimensional problems. Human societies have become more connected and interdependent at multiple levels, i.e., between individuals, communities, nations, institutions, and sociotechnical systems. Growing complexity also increases rates of dependencies, change, and vulnerability. The complexities of the many risk factors and their interaction call for a multidimensional approach to risk management (Cagno, Caron, and Mancini, 2007).
Multiple Criteria Decision Analysis (MCDA), or Multiple Criteria Analysis (MCA), addresses wicked or complex decision-making problems involving various conflicting and noncommensurable evaluations, both quantitative and qualitative. The MCDA concepts are entirely consistent with value maximization behaviour, where value is often multidimensional and subject to imperfections (uncertainties, conflicts, non-commensurability, information types, incompleteness, etc.). MCDA methods have evolved to integrate several information imperfection theories, such as stochastic modelling, belief functions, fuzzy sets, rough sets, and heterogeneous modelling. When used for collective decision making, MCDA helps groups of decision agents to build a constructive conversation around decision opportunities in a way that allows multiple stakeholders' perspectives to be considered. MCDA offers a decision analysis approach appropriate for many practical businesses, as well as environmental, social, and technical applications, e.g., risk management, healthcare, artificial intelligence, and supply chain management.
The emergence of Industry 4.0, through the availability of Big Data and the improved predictive capability of artificial intelligence methods, has revolutionized the way we manage complex systems such as healthcare, supply chain management, and sustainability. Global systems of interconnected entities, physical and/or digital, have the potential to become 'smarter'. Artificial intelligence applications have evolved to support real-time decision-making, to monitor the performance of complex processes, to reduce risks, and to achieve operational excellence. Machine learning algorithms, the connective branch of artificial intelligence, present application hypotheses compatible with the nature of big data and can be grouped into three categories: (i) supervised and unsupervised learning, (ii) deep learning, and (iii) reinforcement learning. The increase in the size of data from different complex and interdependent processes and the speed of computation time has led to the development of innovative solutions for risk management with applications in healthcare, supply chain, and environmental stewardship (Zage et al. 2013; Garvey et al. 2015; Papadopoulos et al. 2017; Baryannis et al. 2019).
Scope of the special issue
This issue will feature multidisciplinary innovative contributions from MCDA and Artificial Intelligence to solve multidimensional risk management and decision analysis. This issue welcomes theoretical and empirical contributions, particularly with applications to healthcare management, supply chain management, and environment and sustainability. The topics may include but not be limited to design, production, logistics, distribution, demand forecasting, supply management, energy management, waste management, service management, digitization, automation, decision support systems, and sustainable management.
The Special Issue will be in line with the editorial expectations of IJERPH. Authors must produce a concise, comprehensive, and rigorous manuscript. Full empirical details must be provided so that the results can be reproduced. IJERPH requires that authors publish all experimental controls and make full datasets available where possible (see the guidelines on Supplementary Materials and references to unpublished data). Manuscripts submitted to this Special Issue of IJERPH should neither have been published before nor be under consideration for publication in another journal. Authors should carefully familiarize themselves with IJERRH instructions: https://www.mdpi.com/journal/ijerph/instructions
References
Cagno, E., Caron, F., & Mancini, M. (2007). A Multi-Dimensional Analysis of Major Risks in Complex Projects. Risk Management, 9(1), 1-18. doi: 10.1057/palgrave.rm.8250014
Churchman, C. W. (1967). Free for All. Management Science, 14(4), B-141-B-146. doi: 10.1287/mnsc.14.4.B141
Crul, L. (2014). Solving wicked problems through action learning. Action Learning: Research and Practice, 11(2), 215-224. doi: 10.1080/14767333.2014.909185
Frensch, P. A., & Funke, J. (1995). Complex problem solving: the European perspective. Hillsdale, N.J: L. Erlbaum Associates.
McMillan, C., & Overall, J. (2016). Management relevance in a business school setting: A research note on an empirical investigation. International Journal of Management Education, 14(2), 187-197. doi: 10.1016/j.ijme.2016.04.005
Simon, H. A. (1973). The structure of ill structured problems. Artificial Intelligence, 4(3-4), 181-201. doi: 10.1016/0004-3702(73)90011-8
Sternberg, R. J., & Frensch, P. A. (1991). Complex problem solving: principles and mechanisms. Hillsdale, N.J: L. Erlbaum Associates.
Waddock, S., Meszoely, G. M., Waddell, S., & Dentoni, D. (2015). The complexity of wicked problems in large scale change. Journal of Organizational Change Management, 28(6), 993-1012. doi: 10.1108/Jocm-08-2014-0146
Zage, D., Glass, K., Colbaugh, R., Improving supply chain security using big data, in: 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics, IEEE, Seattle, WA, USA, 2013, pp. 254–259, http://dx.doi.org/ 10.1109/ISI.2013.6578830.
Baryannis, G., Dani S., Antoniou, G., Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems 101 (2019) 993–1004
Garvey, M., Carnovale, S., Yeniyurt, S., An analytical framework for supply network risk propagation: A Bayesian network approach, European J. Oper. Res. 243 (2) (2015) 618–627, http://dx.doi.org/10.1016/j.ejor.2014.10.034.
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S.J., FossoWamba, S., The role of big data in explaining disaster resilience in supply chains for sustainability, J. Cleaner Prod. 142 (2017) 1108–1118, http: //dx.doi.org/10.1016/j.jclepro.2016.03.059
Dr. Adel Guitouni
Dr. Loubna Benabbou
Dr. Hans Wehn
Guest Editors
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Adel Guitouni
Associate Professor
University of Victoria
Victoria BC
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