The rapid growth of air travel has intensified the need for efficient airport capacity planning, particularly within terminal operations where passenger experience and resource utilization are closely interlinked. Among these operations, the check-in process represents a critical bottleneck, often accounting for the majority of passenger waiting time. Addressing the limitations of physical expansion and the complexity of diverse allocation criteria, this paper introduces an Integrated Check-in Capacity Planning System (ICCPS) that comprehensively supports strategic, tactical, and operational planning decisions. The framework jointly addresses the three core phases of the Check-in Capacity Planning Process (CCPP) by developing: (i) a bi-objective, compact integer programming formulation for sizing and assignment, which incorporates expected queue parameters derived from machine learning-based passenger forecasts and empirically calibrated arrival and service time patterns, combined with an offline, discrete agent-based queue simulator; (ii) an adjacent counter allocation module, solved using a tailored rolling horizon-based heuristic to ensure computational scalability; and (iii) a conflict resolution module to address potential criticalities in capacity-constrained settings and to derive time-varying counter allocations. Extensive computational experiments, based on synthetic instances representative of medium- and large-sized airports, demonstrate the framework’s scalability and effectiveness, with the ability to efficiently generate Pareto-optimal solutions in a matter of minutes using parallelization techniques. Ultimately, a real-world case study at Milan Bergamo Airport (BGY) further validates the practical value of the ICCPS by evaluating: (i) check-in planning over a month-long horizon; (ii) scenarios involving both dedicated and common check-in strategies; and (iii) long-term forecasts of flight growth and demand distributions. The results confirm the system’s capability to minimize expected queuing times and resource usage while providing strategic insights for layout design, scenario evaluation, and day-to-day operational planning.

In this paper, we address the joint optimization of fleet size and mix, along with vehicle routing, under uncertain customer demand. We propose a two-stage stochastic mixed-integer programming model, where first-stage decisions concern the composition of the delivery fleet and the design of consistent baseline routes. In the second stage, approximate recourse actions are introduced to adapt the initial routes in response to realized customer demands. The objective is to minimize the total delivery cost, including vehicle acquisition, travel distance, and penalty costs for unserved demand. To tackle the computational challenges arising in realistic problem instances, we develop a path-based reformulation of the model and design a Kernel Search-based heuristic to enhance scalability. Computational experiments on small synthetic instances, generated through a population-density-based sampling approach, are conducted to validate the formulation and assess the effects of demand stochasticity through standard stochastic measures, after applying a scenario reduction technique. Additional tests on large-scale real-world instances, based on data from the Italian postal company, demonstrate the effectiveness of the proposed approach and provide managerial and practical insights.

In this paper, a dynamic model for an e-bike is presented and its performance is evaluated. The model includes the main components constituting the vehicle electric drive such as the electric motor, the inverter, the DC-DC converter for speed control and regenerative braking, and the battery pack. The implementation is carried out in MATLAB-Simulink environment with the purpose of analyzing the power flows on the vehicle during target driving profiles. Unlike other models available in the literature, the developed model can represent a valuable tool to aid the designer in selecting and comparing different structural and component solutions. Knowing the efficiency of each component is possible to evaluate the global efficiency of the system in various operating conditions.

Revenue management practices are widely employed in various sectors. These mechanisms dynamically adjust prices observed by consumers typically through the control of price classes and their availabil-ity. As such, these pricing environments tend to exhibit some predictable behaviors as the product ap-proaches the expiration or consumption date but may also result in varying degrees of price volatility. Such price paths may ultimately alter consumer behavior, e.g., via delayed purchase timing (i.e., strate-gic behavior) or different willingness to pay. Accordingly, we assess consumers' responses to the real-ized price changes induced by revenue management mechanisms, using fare and sales data from aviation markets. Our empirical analyses reveal that price elasticity decreases (in absolute terms) in the degree of price volatility, whereas the realized demand is lower when price volatility is higher. This suggests that as prices become more volatile, consumers become more oblivious to these price fluctuations and may end up paying more for the products-thereby confirming and generalizing a behavior previously documented for consumer packaged goods (CPGs), which follow dramatically different pricing regimes. While this may suggest support for higher and more volatile prices, we find that given the higher prices, the overall demand decreases, highlighting a delicate trade-off firms face. To distill the insights from the empirical analyses, we formulate two hypotheses, which we test in a laboratory setting by means of an ad hoc experiment. (c) 2023 Elsevier B.V. All rights reserved.

National airport system plans serve as the primary programmatic documents employed by policy-makers to outline the roles of different airports and devise strategies for their coordinated and integrated development, encompassing economic, environmental, and social perspectives. This paper proposes a modeling framework to estimate the strength of each airport's influence and contribution to the surrounding territories, providing methodological foundation for assessing airport demand and delineating the scope of airport interactions. We propose a novel origin-based nested logit model of airport demand based on a comprehensive utility function-denoted as con-accessibility-integrating -integrating advanced metrics of ground accessibility and airport connectivity. To address the lack of extensive pairwise municipality-airport data, we cast the estimation problem as a nonlinear constrained least-squares optimization problem, solved via a differential evolution algorithm. The framework's applicability and insights are demonstrated in a real-world case study of the latest Italian national airport system plan. We highlight the model's capability in addressing three key policy questions: (i) characterizing airport catchments toward investigating the degree of overlap and airport interactions in serving contended areas; (ii) systematically quantifying the overall level of con-accessibility in any region to assess deficits or surpluses and pinpoint areas for strategic interventions; (iii) supporting the assessment and prioritization of various initiatives, including the upgrade of ground access networks, the expansion of airport supply, and the establishment of new airport facilities.

A comparative study between two regenerative braking strategies implemented on a 100 kg (bike + user) overall mass e-bike system is presented. The simulation models are built in MATLAB Simulink (R) environment and a commercial in-wheel direct-drive brushless DC (BLDC) motor is assumed as benchmark electrical machine. In the paper, a brief literature review about regenerative braking is firstly presented. Secondly a simulation comparison between two drive schemes is reported. The first scheme is composed by a buck converter and an inverter with six-step commutation. The second scheme, however, doesn't have the buck converter and uses two different Pulse Width Modulation techniques to control the inverter for motoring function and electric braking function. The aim of the work is to study the possibilities of the implementation and the advantages of this kind of brake, useful for extending the autonomy range of a LEV.

Travelers' choices have been widely studied, and insights have been established on the values associated with different itinerary attributes. This paper extends the current literature by including a novel determinant of air travelers' behavior—that is, the extent to which a connecting itinerary is marketed and handled by an operating carrier. By relying on a stated preference survey, we collect passengers' preferences and analyze them by means of a latent class choice model accounting for both service-related and individual characteristics. Individual characteristics comprise attitudes derived from a factor analysis performed on attitudinal responses toward travel, namely anxiety and timeliness and quality. The results reveal that individuals who care more about timeliness and quality are less sensitive to price, more sensitive to time, and more likely to opt for a nonstop alternative. Additionally, the value assigned to handled connections is considerable (higher for anxious travelers).

This study investigates the impact of airports on population density distribution in the surrounding areas. Using data for 197 European airports, results show that a 10% increase in airport activity leads to a growth in population density of 3.8%. While moving farther from the city centre leads to a decline in population density, we demonstrate that, on average, population density increases up to 7.5 km from the airport, then starts to decrease beyond that threshold. This threshold can be interpreted as the point in space where positive effects start outweighing negative externalities.

Schedule padding is a frequent strategy applied by transport providers, often used to improve their on-time performance. Normally, time efficiency is evaluated in terms of arrival delays. However, in the air transport industry, evidence shows that flights tend to depart late, although arriving on time. This study investigates the carriers' turnaround time strategy by comparing the scheduled and actual daily movements of all flights from or to Europe in 2019. Results show that turnaround variations are more frequent in larger airports, during peak hours, and when an aircraft performs a higher number of flights. Low-cost carriers usually set a significantly shorter turnaround time than traditional carriers, using a larger part of the scheduled block time for ground operations. This strategy greatly influences on-time performance measures and other efficiency indicators traditionally based on scheduled activities, with important implications for both airlines and passengers.

Background: Risk assessment models (RAMs) are relevant approaches to identify cancer outpatients at high risk of venous thromboembolism (VTE). Among the proposed RAMs, the Khorana (KRS) and the new-Vienna CATS risk scores have been externally validated in ambulatory patients with cancer.Objectives: To test KRS and new-Vienna CATS scores in 6-month VTE prediction and mortality in a large prospective cohort of metastatic cancer outpatients during chemotherapy.Patients/Methods: Newly diagnosed patients with metastatic non-small cell lung, colorectal, gastric, or breast cancers were analyzed (n = 1286). The cumulative incidence of objectively confirmed VTE was estimated with death as a competing risk and multivariate Fine and Gray regression.Results: Within 6 months, 120 VTE events (9.7%) occurred. The KRS and the new-Vienna CATS scores showed comparable c-stat. Stratification by KRS provided VTE cumulative incidences of 6.2%, 11.4%, and 11.5% in the low-, intermediate-, and high-risk categories, respectively (p = ns), and of 8.5% vs. 11.8% (p = ns) in the low-vs. high-risk group by the single 2-point cut-off value stratification. Using a pre-defined 60-point cut-off by the new-Vienna CATS score, 6.6% and 12.2% cumulative incidences were obtained in the low-and high-risk groups, respectively (p < 0.001). Furthermore, having a KRS =2 = or a new-Vienna CATS score >60 points was also an independent risk factor for mortality.Conclusion: In our cohort, the 2 RAMs showed a comparable discriminating potential; however, after the application of cut-off values, the new-Vienna CATS score provided statistically significant stratification for VTE. Both RAMs proved to be effective in identifying patients at increased risk of mortality.

This paper proposes an integrated modeling framework to support airline frequency planning in a multi-airport system. First, a grid-based spatial model is developed to estimate passenger demand as a function of air-service characteristics (such as flight frequency and airfare) and ground accessibility. Second, an evolutionary algorithm is proposed to provide decision support for the allocation of flights to airports belonging to the multi-airport system, subject to fleet availability, flow balance and operational requirements. We apply the proposed approach to model frequent flyer passengers departing from one of the major multi-airport systems in Europe. Our results show that the degree of diversification of a multi-airport system—i.e., the presence of alternative airports—plays a significant role on both the demand and supply sides, providing passengers with more diversified and accessible services and providing airlines with higher operational flexibility and resilience in case of disruptions. Our modeling framework can be extended to capture additional managerial objectives or practical requirements.

These investigations indicate that ponatinib treatment increases gene expression of aortic prothrombotic and platelet signalling genes that promote GPVI activation. Addition of pioglitazone reduces the prothrombotic characteristics conferred by ponatinib treatment. These data indicate that cancer-associated thrombosis can be managed by non-anticoagulant pharmacologic agents.

Flight scheduling and fleet assignment are important steps of an airline planning process. In light of the reciprocal relationship between air transport supply and demand, a key element of these models is to devise effective methods to both incorporating estimation of total market demand and allocating passengers over the available itineraries in a specific market. In this paper, we present a novel mixed integer nonlinear flight scheduling and fleet assignment optimization model wherein air travel demand generation and allocation are simultaneously and consistently endogenized. Using a nested logit formulation, we jointly model competition among air travel itineraries and appraise the contribution of specific itinerary attributes to demand generation, therefore yielding a more comprehensive and explicit representation of supply-demand interactions. Computational testing based on realistic problem instances reveals that the model can optimize mid-size hub and-spoke networks within reasonable time. Further analyses illustrate the benefits that can be derived from the application of the proposed approach using real-world data for a major European airline. Results demonstrate that the proposed approach can significantly enhance operating profits by up to 6.9% and better reveal opportunities for demand stimulation against a conventional approach using inelastic trip generation. (c) 2021 Elsevier Ltd. All rights reserved.

The pricing of low-cost carriers (LCCs) compared with traditional airlines has been extensively investigated since their inception in the air transport market. Abundant empirical evidence attests that, on average, LCCs' fares (per km) are lower than those usually offered by full-service carriers (FSCs). Such literature, however, paid virtually no attention to the conditions under which LCCs lose their convenience compared to traditional airlines. The purpose of this study is to investigate the occurrence of LCCs sometimes offering higher fares than FSCs on competing flights. By using a dataset expressly collected for this purpose, we are able to quantify its frequency and suggest some possible explanations. These findings concur to cast some questions on the widely held preconception of vertical differentiation between LCCs' and FSCs' offered services. Further research will be needed in order to understand the relative weight of the suggested factors.

In the last decades, supply chains have increasingly transcended national boundaries developing into global supply chains. Along with the many opportunities arising from international sourcing and the extended commercial presence over the world, the management of a globally dispersed supply chain is highly complex. A key issue to consider when dealing with the global supply chain design is the location of facilities, not only with respect to firms' owned facilities but also the supply and distribution side as factors that affect supply chain complexity and operational performance. This paper sets out a methodological framework to characterize the geographical configuration of a firm's suppliers and retailer networks. Quantitative indexes of network spatial concentration and relative proximity measures based on a nonparametric kernel density estimator are developed to identify both intra- and inter-firm patterns between the supply and point of sales' distributions. The method is first described by means of a series of theoretical-illustrative examples and exemplified by analyzing the geographical dispersion of four practical cases from the fashion-textile industry (i.e., Adidas, Benetton, C&A, and Puma). Subsequently, managerial implications and potential use of the metrics are discussed, showing how the proposed approach can support researchers and practitioners to improve supply chain location decisions and logistic integration, and evaluate changes in either the purchasing or distribution strategy.

Over the past years, airport regulation has been generating a lot of interest in Europe, and despite the passing of Airport Charges Directive in 2009, there is ongoing debate on the need for introducing tighter airport regulations. The aim of the paper is twofold. First, acknowledging that regulation is usually applied in markets where competition is weak or absent, we evaluate the ex-ante need for price regulation in the air transport industry. By focusing on the Italian airport industry, our analysis provides evidence of a high level of competitive pressure faced by airports (both inside and outside the industry), suggesting that tighter price regulation may not be the optimal solution. Second, assuming that stricter regulation of the airport industry is necessary, we empirically investigate the applicability of yardstick regulation to the Italian airport system, outlining critical challenges and issues that may arise when applying benchmarking techniques in setting the optimum level of efficiency at regulated airports. According to current literature, applicability of empirical benchmarking techniques requires some basic research requirements to be met, such as high-quality data, a homogeneous production function, and a sufficient number of comparable observations. We find that both heterogeneity and the relatively small number of comparable airports, along with the complexity of gathering proper data, may compromise the applicability of a regulation scheme based on yardstick principles at the national level.

Background Cancer patients present with a hypercoagulable state often associated with poor disease prognosis. Objectives This study aims to evaluate whether thrombin generation (TG), a global coagulation test, may be a useful tool to improve the identification of patients at high risk of early disease recurrence (ie, E-DR within 2 years) after breast cancer surgery. Patients/Methods A cohort of 522 newly diagnosed patients with surgically resected high-risk breast cancer were enrolled in the ongoing prospective HYPERCAN study. TG potential was measured in plasma samples collected before starting systemic chemotherapy. Significant predictive hemostatic and clinic-pathological parameters were identified in the derivation cohort by Cox regression analysis. A risk prognostic score for E-DR was generated in the derivation and tested in the validation cohort. Results After a median observation period of 3.4 years, DR occurred in 51 patients, 28 of whom were E-DR. E-DR subjects presented with the highest TG values as compared to both late-DR (from 2 to 5 years) and no relapse subjects (P < .01). Multivariate analysis in the derivation cohort identified TG, mastectomy, triple negative and Luminal B HER2-neg molecular subtypes as significant independent predictors for E-DR, which were utilized to generate a risk assessment score. In the derivation and validation cohorts, E-DR rates were 2.3% and 0% in the low-risk, 10.1% and 6.3% in the intermediate-risk, and 18.2% and 16.7%, in the high-risk categories, respectively. Conclusions Inclusion of TG in a risk-assessment model for E-DR significantly helps the identification of operated breast cancer patients at high risk of very early relapse.

This paper proposes an origin-based air travel demand model that assumes saturation at the origin level and explicitly accounts for substitutability between destinations. We simultaneously integrate demand generation and allocation by means of a multilevel aggregate nested logit formulation that covers the choices of whether or not to travel by air, where to travel (destination), and how to travel (itinerary). Two specifications are proposed to reflect systematic differences between lengths of haul and the bootstrap is applied to jointly address endogeneity issues and data missingness. The validity of the proposed approach is tested over the entire network of outbound air trips from Italy in 2018.