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Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.

The purpose of this paper is to focus on disruption management responding to large‐area flight delays (LFD). It is urgent for airways to reschedule the disrupted flights so as to relieve the negative influence and minimize losses. The authors try to reduce the risk of airline company's credit and economic losses by rescheduling flights with mathematic models and algorithm.

Based on flight classifications of real‐time statuses and priority indicators, all flights are prioritized. In this paper, two mathematic programming models of flight rescheduling are proposed. For the second model, an optimum polynomial algorithm is designed.

In practice, when LFD happens, it is very important for the airline company to pay attention to real‐time statuses of all the flights. At the same time, the disruption management should consider not only the economic loss but also other non‐quantitative loss such as passengers' satisfaction, etc.

In this paper, two mathematic programming models of flight rescheduling are built. An algorithm is designed and it is proved to be an optimum polynomial algorithm and a case study is given to illustrate the algorithm. The paper provides a theory support for airways to reduce the risk brought by LFD.

The purpose of this paper is to carry out a comprehensive review for state-of-the-art works in disruption risk management of express logistics mainly supported by air-transportation. The authors aim to suggest some new research directions and insights for express logistics practitioners to develop more robust planning in air-transportation.

The authors mainly confined the research to papers published over the last two decades. The search process was conducted in two dimensions: horizontal and vertical. In the horizontal dimension, attention was paid to the evolution of disruption management across the timeline. In the vertical dimension, different foci and strategies of disruption management are employed to distinguish each article. Three keywords were used in the full text query: “Disruption management”, “Air transportation”, and “Airline Operations” in all database searches listed above. Duplications due to database overlap, articles other than those from academic journals, and papers in languages other than English were discarded.

A total of 98 articles were studied. The authors categorized the papers into two broad categories: Reactive Recovery, and Proactive Planning. In addition, based on the problem characteristics and their application scenarios, a total of 11 sub-categories in reactive recovery and nine sub-categories in proactive planning were further identified. From the analysis, the authors identified some new categories in the air-transportation recovery. In addition, by analyzing the papers in robust planning, according to the problem characteristics and the state-of-the-art research in recovery problems, the authors proposed four new research directions to enhance the reliability and robustness of air-transportation express logistics.

This study provided a comprehensive and feasible taxonomy of disruption risk management. The classification scheme was based on the problem characteristics and the application scenarios, rather than the algorithms. One advantage of this scheme is that it enables an in-depth classification of the problem, that is, sub-categories of each class can be revealed, which provides a much wider and clearer horizon to the scientific progress in this area. This helps researchers to reveal the problem’s nature and to identify the future directions more systematically. The suggestions for future research directions also point out some critical research gaps and opportunities.

This study summarized various reasons which account for the disruption in air-transportation. In addition, the authors suggested various considerations for express logistics practitioners to enhance logistics network reliability and efficiency.

There are various classification schemes in the literature to categorize disruption management. Using different algorithms (e.g. exact algorithm, heuristics, meta-heuristics) and distinct characteristics of the problem elements (e.g. aircraft, crew, passengers, etc.) are the most common schemes in previous efforts to produce a disruption management classification scheme. However, the authors herein attempted to focus on the problem nature and the application perspective of disruption management. The classification scheme is hence novel and significant.

Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost.

A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model.

The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited.

The authors confirm that this is an original paper and has not been published or under consideration in any other journal.

The objective of this work is to provide a novel aircraft allocation model for fractional business aviation. This model may provide decision-makers with alternative routing solutions that take into consideration preventive maintenance and failure prognostics information. The expected results are more efficient routing solutions when compared to conventional planning models, to help decision-makers improve operations and maintenance planning.

The model is a mixed integer linear problem formulation addressing and considering preventive maintenance and failure prognostics for optimal operations. Numerical experiments were performed using both field and synthetic data to validate the proposed method. All instances are solved using branch, price and cut algorithms from open-source software.

The results obtained in this study show that the use of failure prognostics information in aircraft routing can provide improvements in overall planning. By choosing slightly longer flight legs, the flight cost will increase, but putting an aircraft with a higher risk of failure on a leg inbound to a maintenance base can reduce maintenance and overall operating cost.

The model and method provide decision-makers with routing solutions that consider new aspects of planning, not used in previous works, such as failure. Most of the literature focuses on solving routing problems for large commercial airlines. Considering that, few solutions are found in literature for fractional business operators, which have their own operational particularities, such as a company managing a fleet of aircraft belonging to multiple shareowners. In such operation, clients may not always fly in the aircraft that they are shareowners, but an aircraft from the fractional fleet of the same category. Here, the company managing the aircraft guarantees that an aircraft will be ready to attend client demands in minimum time. One of the major differences from other models of operation is the dynamic nature of its flight demands, thus requiring flexible and agile planning limiting the available time to find a routing solution.

To reduce the time of flight rescheduling, reduce the total delay cost of all flights to a minimum and put forward more references for passengers to take flights, this paper aims to mainly study the recovery of flights affected by snow disaster within the minimum delay time.

The temporal and spatial network flight recovery model is used to optimize all flights of various types of aircraft, and the adjusted flight schedule based on minute delay time is obtained. In addition, for passenger travel flights, the impact of passenger delay cost on the total delay time is minimized as an objective function to calculate the passenger delay cost.

In this paper, the actual departure time of aircraft is sorted in ascending order. Up to five planes can take off from the runway every 5 min, and the 10-min decision interval is successively delayed. The actual arrival time is sorted by the same method and the sequential delay is calculated to obtain the adjusted flight schedule. As a result, it takes less time to reschedule flights.

In this paper, heuristic algorithm is used to adjust the schedule of delayed flights flexibly, which is convenient for manual modification. This decision method has good robustness and can partially adjust the interrupted flights without affecting other scheduled flights while maintaining the stable operation of the whole plan, greatly improving the efficiency of civil aviation operations and reducing the impact of flight delays.

Despite mixed evidence, researchers often suggest that married adults tend to live generally healthier lifestyles than their unmarried counterparts. In this chapter, we propose and test a reconceptualization of the health lifestyle that distinguishes between “homebody” risks and “hedonic” risks that may help to make sense of previous findings concerning marriage and health-related behavior.

Using data from the 2004 Survey of Adults (n = 1,385), we employ ordinary least squares regression to model indices of normative and conventional homebody risks (greater body mass, infrequent exercise, poorer diet, and abstinence from alcohol) and unconventional and potentially dangerous hedonic risks (smoking, heavy drinking, going out to bars, eating out, inadequate sleep, and driving without seatbelts) as a function of marital status.

Our key findings indicate that married adults tend to score higher on homebody risks and lower on hedonic risks than never married adults, net of controls for age, gender, race/ethnicity, citizenship, interview language, education, employment status, household income, and religious involvement.

Research limitations include cross-sectional data, restricted indicators of health-related behavior, and narrow external validity.

Contrary to previous research, we conclude that the lifestyle of married adults is not uniformly healthy.

The purpose of this paper is to discuss how the use of information technology in schools can influence students’ democratic comprehension.

First, two different ideas of democracy are introduced and how these ideas are linked to cognitivistic and social constructivistic learning theories, respectively, is illustrated. Next, a case study is described, where Engeström’s mediational triangle is used for analysing how the use of interactive whiteboards (IWB) influences the teaching of democracy in a fifth-grade school class.

The paper lists a set of preconditions and recommendations for a use of IWB as support for students’ experience of democracy as a way of living.

As the paper focuses on research design and development of didactical designs, future research and articles can further study the effects of the didactical designs and the democratic comprehension supported hereby. The paper is set in a Danish school context.

It is argued that the IWB can be used as support for developing the students’ democratic comprehension by focusing on and, if necessary, changing the elements of the activity system, e.g. the rules and the roles concerning the use of the IWB.

The paper’s linkage of democratic ideas, learning theory and information technology is relevant for researchers. Teachers can use the paper, as it offers didactical principles for using information technology as support for students’ democratic comprehension.

Describes how, in a highly competitive environment, Larsen & Toubro attracts and retains its talent in a strategic way.

Researches the firm’s various talent-management and leadership-training strategies.

Reveals that 360-degree talent management is necessary to empower employees and to develop leadership capacities.

Presents a framework for talent management and leadership development in a manufacturing business which can be recast to suit different organizational set-ups.

Explains that the company recruits many of its workers from rural India. Employees are recruited for their potential as much as for their existing skills.

Describes a new dimension of talent management being practiced and its success in a manufacturing business.