Abstract
At the practical level, the static stability constraint is one of the most important constraints in practical pallet loading problems, such as air cargo palletizing. Approaches to modeling static stability, which range from base support and mechanical equilibrium calculations to physical simulation, differ in workflow, focus, and assumptions, so choosing the right static stability approach has a substantial impact on the quality of the solution and, ultimately, on loading security. To date, little research has investigated the structural differences between approaches. The aim of this paper is to integrate knowledge and shed light on the applicability of the different approaches for the practical scenario of air cargo palletizing. We tackle this problem through (1) a reformulation and extension of static stability toward loading stability, (2) a conceptual analysis of current approaches, and (3) benchmarking that employs an independent multibody simulation on multiple heterogeneous datasets. Our results show that all approaches are prone to structure errors and vary significantly in their premises and information usage. Further, full base support is revealed to be the most restrictive approach by far, while physical simulation achieves the greatest accuracy. Given the trade-off between accuracy and runtime, the mechanical equilibrium approach is a good choice, while partial base support performs best for lower support values.

Abstract
Port wine stocks ameliorate during storage, facilitating product differentiation according to age. This induces a trade-off between immediate revenues and further maturation. Varying climate conditions in the limited supply region lead to stochastic purchase prices for wine grapes. Decision makers must integrate recurring purchasing, production, and issuance decisions. Because stocks from different age classes can be blended to create final products, the solution space increases exponentially in the number of age classes. We model the problem of managing port wine inventory as a Markov decision process, considering decay as an additional source of uncertainty. For small problems, we derive general management strategies from the long-run behavior of the optimal policy. Our solution approach for otherwise intractable large problems, therefore, first aggregates age classes to create a tractable problem representation. We then use machine learning to train tree-based decision rules that reproduce the optimal aggregated policy and the enclosed management strategies. The derived rules are scaled back to solve the original problem. Learning from the aggregated optimum outperforms benchmark rules by 21.4% in annual profits (while leaving a 2.8%-gap to an upper bound). For an industry case, we obtain a 17.4%-improvement over current practices. Our research provides distinct strategies for how producers can mitigate climate risks. The purchasing policy dynamically adapts to climate-dependent price fluctuations. Uncertainties are met with lower production of younger products, whereas strategic surpluses of older stocks ensure high production of older products. Moreover, a wide spread in the age classes used for blending reduces decay risk exposure.

Abstract
The textile industry is a complex and dynamic system where structured decision-making processes are essential for efficient supply chain management. In this context, mathematical programming models offer a powerful tool for modeling and optimizing the textile supply chain. This systematic review explores the application of mathematical programming models, including linear programming, nonlinear programming, stochastic programming, robust optimization, fuzzy programming, and multi-objective programming, in optimizing the textile supply chain. The review categorizes and analyzes 163 studies across the textile manufacturing stages, from fiber production to integrated supply chains. Key results reveal the utility of these models in solving a wide range of decision-making problems, such as blending fibers, production planning, scheduling orders, cutting patterns, transportation optimization, network design, and supplier selection, considering the challenges found in the textile sector. Analyzing those models, we point out that sustainability considerations, such as environmental and social aspects, remain underexplored and present significant opportunities for future research. In addition, this study emphasizes the importance of incorporating multi-objective approaches and addressing uncertainties in decision-making to advance sustainable and efficient textile supply chain management.

Abstract
This study seeks to comprehensively analyze the multidimensional determinants underlying perceptions of safety, security, and comfort in transport mode choice, specifically focusing on private transport, public transport and walking. The research begins with an extensive literature review to identify and delve into the factors influencing perceptions of safety, security, and comfort across various transport modes. This inquiry is further enhanced by organizing two focused group sessions. A total of 35 key factors were identified, forming the basis for subsequent investigation. The study then progressed to the development and administration of a survey aimed at capturing responses from a diverse audience, with the goal of exploring the factors influencing perceptions related to different transport modes. A total of 302 responses were collected and meticulously analyzed to discern the factors impacting various relationships and to identify consistent perceptions across diverse transport modes. Additionally, a factor analysis was conducted to validate the findings derived from the data. The outcomes of this research constitute a significant contribution to the existing literature, offering valuable insights that pave the way for a more holistic understanding of the factors guiding transport mode choices. © The Author(s) 2025.

Abstract
Purpose - Customer-provider relationships unfold through multiple touchpoints across different channels. However, some touchpoints are more important than others. Such important touchpoints are viewed as moments of truth (MOTs). This study examines the impact of a series of touchpoints on an MOT, and the role MOTs play in determining future profitability and other behavioral outcomes (e.g. customer retention and customer cross-buy) in a business-to-business (B2B) context. Design/methodology/approach - Building upon social exchange theory, a conceptual model is proposed and tested that examines the impact of human, digital, and physical touchpoints and past MOTs on customer evaluation of a current MOT and on future customer outcomes. This research employs a longitudinal methodology based on a unique panel dataset of 2,970 B2B customers. Findings - Study results show that all touchpoints significantly contribute to MOTs, while human and physical touchpoints maintain their primacy during MOTs. The impact of MOTs on future customer outcomes is also demonstrated. Practical implications - This study highlights the need for prioritizing human and physical touchpoints in managing MOTs, and for carefully managing MOTs across time. Originality/value - Given its B2B outlook and longitudinal approach, this research contributes to the multichannel and interactive marketing literature by determining relevant touchpoints for B2B customers.

Abstract
Engagement in the ongoing energy transition is particularly challenging for energy-poor citizens. As such, there is a pressing need for a better understanding of their experiences and for strategies that enable their engagement. In this study, we identify different groups of citizens based on their energy poverty issues and examine their engagement behaviors (seeking information, proactive managing, sharing feedback, helping others, and advocating). Using cluster analysis and multiple correspondence analysis, we analyzed a sample of 915 citizens from eight European cities participating in a Horizon2020 EU project (Alkmaar-NL, Bari-IT, Celje-SI, Evora-PT, Granada-ES, Hvidovre-DK, Ioannina-GR, & Uacute;jpest-HU). Several groups of citizens reported either multiple energy issues, a single issue (energy bills, insulation, cooling, heating), or no issues, and the statistical tests showed significant differences across these groups in terms of engagement in seeking information, helping, and advocating. Moreover, we identified that certain groups tend to have specific levels of engagement (high, medium, low) and that sharing feedback generally has a low level of engagement. Overall, this study provides empirical insights into how energy-poor citizens exercise agency through engagement behaviors and offers actionable insights for designing measures to mitigate energy poverty in complementarity with technical and economical solutions.