Abstract
In the original version of this article, "Data availability" statement was mistakenly inserted. The following data availability statement should be removed. As a final point, while the traditional independent demand model involves comparing unconstrained bookings with unconstrained demand forecasts to assess prediction accuracy, handling dependent demand is more complex, since the availability of a class affects the demand for other classes. Therefore, it is essential to have forecast data for all control policies, as advocated by Fiig et al. (2014), to establish a standardized method for computing forecast errors. This ensures the accurate functionality of the predictive model for optimal margin correction. The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Limited 2024.

Abstract
Traditional revenue management systems are built under the assumption of independent demand per fare. The fare adjustment theory is a methodology to adjust fares that allows for the continued use of optimization algorithms and seat inventory control methods, even with the shift toward dependent demand. Since accurate demand forecasts are a key input to this methodology, it is reasonable to assume that for a scenario with uncertainties it may deliver suboptimal performance. Particularly, during and after COVID-19, airlines faced striking challenges in demand forecasting. This study demonstrates, firstly, the theoretical dominance of the fare adjustment theory under perfect conditions. Secondly, it lacks robustness to forecast errors. A Monte Carlo simulation replicating a revenue management system under mild assumptions indicates that a forecast error of ±20% can potentially prompt a necessity to adjust the margin employed in the fare adjustment theory by -10%. Moreover, a tree-based machine learning model highlights the forecast error as the predominant factor, with bias playing an even more pivotal role than variance. An out-of-sample study indicates that the predictive model steadily outperforms the fare adjustment theory. © The Author(s), under exclusive licence to Springer Nature Limited 2024.

Abstract
Maintaining process quality is one of the biggest challenges manufacturing industries face, as production processes have become increasingly complex and difficult to monitor effectively in today's manufacturing contexts. Reliance on skilled operators can result in suboptimal solutions, impacting process quality. In doing so, the importance of quality monitoring and diagnosis methods cannot be undermined. Existing approaches have limitations, including assumptions, prior knowledge requirements, and unsuitability for certain data types. To address these challenges, we present a novel unsupervised monitoring and detection methodology to monitor and evaluate the evolution of a quality characteristic's degradation. To measure the degradation we created a condition index that effectively captures the quality characteristic's mean and scale shifts from the company's specification levels. No prior knowledge or data assumptions are required, making it highly flexible and adaptable. By transforming the unsupervised problem into a supervised one and utilising historical production data, we employ logistic regression to predict the quality characteristic's conditions and diagnose poor condition moments by taking advantage of the model's interpretability. We demonstrate the methodology's application in a glass container production process, specifically monitoring multiple defective rates. Nonetheless, our approach is versatile and can be applied to any quality characteristic. The ultimate goal is to provide decision-makers and operators with a comprehensive view of the production process, enabling better-informed decisions and overall product quality improvement.

Abstract
This study estimates alighting stops and transfers from entry-only Automatic Fare Collection (AFC) data. The methodology adopted includes two main steps: an implementation of the Trip Chaining Method (TCM) to estimate the alighting stops from AFC records and the subsequent application of criteria for the identification of transfers. For each pair of consecutive AFC records on the same smart card, a transfer is identified considering a threshold for the walking distance, a threshold for the time required to perform an activity, and the validation of different boarding routes. This methodology was applied to the case study of Porto, Portugal, considering all trips performed by a set of 19999 smart cards over one year. The results of this methodology allied with visualization techniques allowed to study Origin-Destination (OD) patterns by type of day, seasonally, and by user frequency, each analyzed at the stop level and at the geographic area level.

Abstract
The implementation of novel regulatory and technical requirements for the distribution of vehicle axle weights in road freight transport introduces a new set of constraints on vehicle routing. Until now, axle weight distribution in determining the load plan for freight transport units has been overlooked in the vehicle routing process. Compliance with these axle weight constraints has become paramount for road freight transport companies, since noncompliance with the axle weight distribution legislation translates into heavy fines. This work aims to provide a tool capable of generating cargo loading plans and routing sequences for a palletised cargo distribution problem. The problem addressed integrates the capacitated vehicle routing problem with time window and the two-dimensional loading problem with load balance constraints. Two integrative solution approaches are proposed, one giving greater importance to the routing and the other prioritising the loading. In addition, a novel MILP model is proposed for the 2D pallet loading problem with load-balance constraints that take advantage of the standard dimension of the pallets. Extensive computational experiments were performed with a set of well-known literature benchmark instances, extended to incorporate additional features. The computational results show the effectiveness of the proposed approaches.

Abstract
The practical relevance and challenging nature of the Vehicle Routing Problem (VRP) have motivated the Operations Research community to consider different practical requirements and problem variants throughout the years. However, businesses still face increasingly specific and complex transportation re-quirements that need to be tackled, one of them being synchronisation. No literature contextualises syn-chronisation among other types of problem aspects of the VRP, increasing ambiguity in the nomenclature used by the community. The contributions of this paper originate from a literature review and are three-fold. First, new conceptual and classification schemas are proposed to analyse literature and re-organise different interdependencies that arise in routing decisions. Secondly, a modelling framework is presented based on the proposed schemas. Finally, an extensive literature review identifies future research gaps and opportunities in the field of VRPs with synchronisation.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )