• Achieving Agility in Evacuation Operations: An Evidence-Based Framework

      Rodríguez-Espíndola, O.; Despoudi, S.; Albores, P.; Sivarajah, Uthayasankar (2019)
      There is an agreement among European countries about the need to achieve efficient, effective and responsive evacuations as part of disaster management. Evacuations face uncertain and dynamic conditions, which often challenge the expectations at the planning stage. This research looks at the adoption of agility in evacuation operations. Managers involved in disaster operations in three countries were interviewed to identify current practices and needs during evacuations. This article looks at the potential of beneficiary engagement, staff and information, cooperation, and fitness for change to incorporate agile practices at each one of the stages of evacuation planning. The purpose is to provide an Agile Evacuation Operations (AEO) evidence-based framework to inform theory and practice. The analysis provided shows that along with current practices it is important to engage the beneficiaries more closely, empower and train the staff to react to unexpected conditions, and take advantage of local knowledge to enhance operations.
    • Artificial Intelligence and Food Security: Swarm Intelligence of AgriTech Drones for Smart AgriFood Operations

      Spanaki, K.; Karafili, E.; Sivarajah, Uthayasankar; Despoudi, S.; Irani, Zahir (2021)
      The Sustainable Development Goals (SDGs) present the emerging need to explore new ways of AgriFood production and food security as ultimate targets for feeding future generations. The study adopts a Design Science methodology and proposes Artificial Intelligence (AI) techniques as a solution to food security problems. Specifically, the proposed artefact presents the collective use of Agricultural Technology (AgriTech) drones inspired by the biomimetic ways of bird swarms. The design (artefact) appears here as a solution for supporting farming operations in inaccessible land, so as unmanned aerial devices contribute and improve the productivity of farming areas with limited capacity. The proposed design is developed through a scenario of drone swarms applying AI techniques to address food security issues. The study concludes by presenting a research agenda and the sectoral challenges triggered by the applications of AI in Agriculture.
    • Disruptive Technologies in Agricultural Operations: A Systematic Review of AI-driven AgriTech Research

      Spanaki, K.; Sivarajah, Uthayasankar; Fakhimi, M.; Despoudi, S.; Irani, Zahir (2021-01)
      The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of Agricultural Technology (AgriTech) with applications of Artificial Intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations.
    • Idiosyncratic risk and the cross-section of stock returns: the role of mean-reverting idiosyncratic volatility

      Bozhkov, S.; Lee, H.; Sivarajah, Uthayasankar; Despoudi, S.; Nandy, M. (2020)
      A key prediction of the Capital Asset Pricing Model (CAPM) is that idiosyncratic risk is not priced by investors because in the absence of frictions it can be fully diversified away. In the presence of constraints on diversification, refinements of the CAPM conclude that the part of idiosyncratic risk that is not diversified should be priced. Recent empirical studies yielded mixed evidence with some studies finding positive correlation between idiosyncratic risk and stock returns, while other studies reported none or even negative correlation. We revisit the problem whether idiosyncratic risk is priced by the stock market and what are the probable causes for the mixed evidence produced by other studies, using monthly data for the US market covering the period from 1980 until 2013. We find that one-period volatility forecasts are not significantly correlated with stock returns. The mean-reverting unconditional volatility, however, is a robust predictor of returns. Consistent with economic theory, the size of the premium depends on the degree of 'knowledge' of the security among market participants. In particular, the premium for Nasdaq-traded stocks is higher than that for NYSE and Amex stocks. We also find stronger correlation between idiosyncratic risk and returns during recessions, which may suggest interaction of risk premium with decreased risk tolerance or other investment considerations like flight to safety or liquidity requirements. The difference between the correlations of the idiosyncratic volatility estimators used by other studies and the true risk metric the mean-reverting volatility is the likely cause for the mixed evidence produced by other studies. Our results are robust with respect to liquidity, momentum, return reversals, unadjusted price, liquidity, credit quality, omitted factors, and hold at daily frequency.