Personnel scheduling models in industrial engineering typically represent fatigue as a static counter computed from schedule structure, ignoring the time-dependent dynamics of homeostatic sleep debt and circadian-modulated recovery documented in the biomathematical literature. This paper proposes a system dynamics model that integrates the two-process homeostatic-circadian formulation, circadian-phase-dependent sleep efficiency, and chronotype heterogeneity within an explicit stock-and-flow structure with two feedback loops. The model is implemented in Vensim with hourly resolution and applied to four canonical shift patterns under three chronotypes. The contribution is methodological: it brings the system dynamics framework to a problem domain dominated by closed-form ODE models and provides a transparent policy-evaluation tool aligned with the conference theme From Data to Policy.

Work-related stress is a growing occupational health concern that affects employee wellbeing and organisational performance. It occurs when job demands exceed workers’ capacity and is closely associated with psychosocial risks, including excessive workload, job insecurity, limited decision-making authority, poor support, and unclear roles. This paper proposes a mixed-methods framework for assessing psychosocial risks and work-related stress in the United Arab Emirates (UAE). Published studies were reviewed to identify methodological gaps and inform a two-phase design combining quantitative survey assessment with qualitative interviews. The framework supports risk identification, contextual interpretation, and evidence-based mitigation planning. Given the productivity burden of mental health problems in the UAE, the proposed approach offers a practical basis for proactive psychosocial risk management and workplace wellbeing improvement.

Problem framing is a critical component of the design thinking process, even though it is often overlooked in practice. While significant attention has been directed toward problem-solving or solution development, the way a problem is initially defined plays a key role in shaping the effectiveness of the outcome. This paper examines how problem framing can transform an initial, often incomplete, or assumption-driven understanding of a problem into a clearer and more actionable definition. A qualitative approach is implemented, in which participants are asked to describe a real problem they experienced and then reflect on it through a structured problem framing exercise. This exercise incorporates reflection tools to guide participants in exploring underlying causes and multiple perspectives. Initial problem definitions are often narrow and influenced by unexamined assumptions, limiting the effectiveness of proposed solutions. Through guided reframing, participants can expect a deeper and more holistic understanding of the problem, uncovering root causes, considering broader human and systemic factors. This study aims to highlight the value of problem framing. It demonstrates how design thinking, supported by structured reflection tools, can help individuals navigate ambiguity, challenge assumptions, and redefine problems to better align with real needs. The findings contribute to ongoing research on problem framing and offer practical insights for improving innovation outcomes by emphasizing the importance of defining the right problem before pursuing solutions.

Logistics 5.0 extends digital logistics by integrating cyber physical systems, intelligent platforms, wearable technologies, human centered interfaces, and real time decision support. Yet its successful implementation depends not only on technological maturity but also on the adoption behavior of frontline last mile logistics workers. This research in progress study examines how last mile workers cognitively and behaviorally respond to Logistics 5.0 platform features using simultaneous EEG and Tobii eye tracking. The proposed experiment compares traditional logistics platforms, and Logistics 5.0 platforms with worker task choice. Neurophysiological measures, behavioral task outcomes, and self-reports will be used to examine trust, autonomy, cognitive workload, and adoption intention. The study contributes to industrial engineering, operations management, supply chain, and logistics by explaining worker level adoption mechanisms in intelligent logistics systems.