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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 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.

The objective of this research is to model supply chain network resilience for low frequency high impact disruptions. The outputs are aimed at providing policy and practitioner guidance on ways to enhance supply chain resilience.

The research models the resilience of New Zealand's log export logistical network. A two-tier approach is developed; linear programming is used to model the aggregate-level resilience of the nation's ports, then discrete event simulation is used to evaluate operational constraints and validate the capacity of operational flows from forests to ports.

The synthesis of linear programming and discrete event simulation provide a holistic approach to evaluate supply chain resilience and enhance operational efficiency. Strategically increasing redundancy can be complimented with operational flexibility to enhance network resilience in the long term.

The two-tier modelling approach has only been applied to New Zealand's log export supply chains, so further applications are needed to insure reliability. The requirement for large quantities of empirical data relating to operational flows limited the simulation component to a single region

New Zealand's log export supply chain has low resilience; in most cases the closure of a port significantly constrains export capacity. Strategic selection of location and transportation mode by foresters and log exporters can significantly enhance the resilience of their supply chains.

The use of a two-tiered analytical approach enhances validity as each level's limitations and assumptions are addressed when combined with one another. Prior predominantly theoretical research in the field is validated by the empirical investigation of supply chain resilience.

The purpose of this paper is to investigate the importance of successful service recovery in the airline sector by examining the interrelationship between perceived justice, recovery satisfaction and overall satisfaction, customer trust and customer loyalty. Furthermore, the research assesses the mediating effect of overall satisfaction and customer trust on customer loyalty.

Data were collected via an airport intercept survey of Malaysia Airlines passengers who had experienced service failure. In total, 380 responses were used for the final analysis. The study uses partial least squares structural equation modelling technique with SmartPLS 3.0, in order to test and validate the research model and hypotheses posited.

The results reveal that: recovery satisfaction is significantly affected by procedural and interactional justice; distributive and procedural justice, as well as recovery satisfaction influenced overall satisfaction; customer trust is most influenced by interactional justice, distributive justice and recovery satisfaction; customer loyalty is positively affected by customer trust, overall satisfaction and recovery satisfaction; and the influence amongst recovery satisfaction and customer loyalty is partially mediated by customer trust and overall satisfaction.

The study contributes to a whole conceptual comprehension of the essential determinants of customer loyalty from the combined perspectives of three theories, namely, justice theory, expectancy disconfirmation theory and commitment-trust theory. This study successfully differentiates the three dimensions of perceived justice and assesses them individually to discern and compare their influence on overall satisfaction, recovery satisfaction and trust. In addition, the study finds that the influence of recovery satisfaction on loyalty is partially and sequentially mediated by trust and overall satisfaction.

This study investigates the attitudes and behaviour of airline front‐line personnel in Britain and Italy, in situations of service failure and recovery. Attitudes towards service recovery are explored through hypothetical scenarios in a questionnaire, and actual behaviours are recorded by using the critical incident technique. The results show that respondents’ attitudes toward service recovery differ by nationality with regard to their affectivity, their group orientation, and to whom they confer authority. With respect to behaviour, satisfactory outcomes from service recovery situations seem to be attributed to different factors by the two nationalities.

The purpose of this paper is to examine the precursors and outcomes of service recovery satisfaction and customer commitment among airline business customers.

Data were collected from airline travellers in South Africa using a structured questionnaire. Structural equation modelling was used to analyse the proposed hypotheses.

The results revealed that recovery expectations and perceived equity exert significant influence on levels of recovery satisfaction, which in turn influence overall satisfaction, trust and commitment. The study also revealed that trust and overall satisfaction are antecedents of customer commitment and that customer commitment has a significant positive relationship with positive word of mouth. It was also established that the quality of past service performance moderates the relationship between recovery satisfaction and commitment.

Airlines are advised to stimulate customer trust in the service delivery process through transparency in the procedures by which they resolve service failures and to remain committed to their service recovery promises to customers. It is also proposed that airlines should secure increased positive word of mouth through offering satisfactory service recovery.

Very little research in the South African context exists which focusses on the influence of customers satisfaction and commitment in the airline industry. This paper helps in establishing the antecedents of customer commitment after service failure in the airline industry.

This paper seeks to examine the effectiveness of internal processes of service quality recovery for an international airline.

An action research methodology was adopted. The research involved: a review of available service quality literature; the identification of causes of failure/errors within the host company; the development of key lessons and management guidelines.

It is argued that, for service recovery to be effective, it must be external (to the customer) as well as internal (to the organisation). The need to incorporate employees and not overlook their significance, power and influence on the delivery of quality service is highlighted. Through comparison with another airline the findings re‐assert that service quality excellence can only be achieved through employee satisfaction, commitment and loyalty as a result of senior management commitment, focus and drive.

The methodology applied was appropriate, generating data to facilitate discussion and from which to draw specific conclusions. A perceived limitation is the single case approach; however, Remenyi argues that this can be enough to add to the body of knowledge. For further investigation, there is an ongoing opportunity for future research in the area of service quality, failure and recovery, as well as the service quality gaps within the airline.

Key lessons and management guidelines for improving service quality are presented.

The paper describes how an international airline has tangible service quality failure and recovery systems in place, but fails to capitalise on the data and information generated.

This paper aims to illustrate how legacy airlines can reorientate to achieve sharp recoveries in performance following prolonged periods of stagnation, decline and eroding competitiveness.

The authors use a qualitative analysis of five longitudinal case studies of legacy airlines that embarked on strategic change between 1997 and 2006. Data collection spanned ten years and included archival data, public documents, news clippings, accounts in specialist books and internal company documentation.

The paper identifies two distinct approaches for reorientation in the legacy airline industry. Companies that have fallen behind and are in risk of failure focus on regaining customer trust and loyalty, and restructuring route networks, business processes and costs in an “improvement and innovation” reorienting approach. Underperforming airlines, for whom growth has declined in traditional markets and who note that opportunities exist elsewhere, focus on product and service development and geographical growth in an “extension and expansion” reorienting approach.

The paper develops a framework for successful reorientation in the legacy airline industry. This framework encourages executives to focus on and leverage profit maximization, quality, leadership, alliance networks, regional consolidation and staff development during periods of strategy formulation and reorientation.

This research addresses the dearth of understanding and attention afforded to the concept of reorientation in the literature on strategic turnaround. The research also serves to emphasize the presence and importance of reorientation as a strategy of change within the legacy airline industry. Furthermore, in demonstrating how this strategy can be implemented in a sharp‐bending or performance improvement context, this study illustrates how reorientation is intertwined with the broader turnaround process.

Strategic analysis of customer profitability for assessing market segmentation and reconfiguring customer relationship management (CRM) activities remains the key factor for achieving high return on CRM investment. The purpose of this paper is to map the profit-based ranking of corporate customers into the current market segments, with a view of determining the relative profitability of each market segment.

This study develops a novel model that combines activity-based costing (ABC), CRM, fuzzy analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) methods to evaluate strategically customer profitability and prioritizing corporate accounts. This case study airline company has invested heavily in CRM over the past seven years on integrating multi-functional departments that touch customers. The airline operations management and marketing functions provide key inputs. Results of the hybrid model validate feasibility of the proposed model.

The airline management makes use of the ranking results to optimize customer profitability by reconfiguring marketing programs, integrated schedule design, fleet assignment, maintenance routing, crew scheduling, and real-time optimization of schedule recovery in the aftermath of disruptions or irregularities. The proposed model also directs the marketing function to customize service offerings and introduce appropriate service levels to engage customers of different segments for the purpose of maximizing corporate profitability.

Significant amount of investment is necessary to design and implement the extensive CRM database and systems to assure customer data quality and availability so as to bear fruits in the proposed hybrid model. These data requirements can especially be a critical barrier for small to medium-sized companies.

This hybrid model is able to capitalize on the benefits of the ABC, CRM, fuzzy AHP, and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification.

This study represents the first move to adopt the fuzzy AHP and TOPSIS methods to analyze the ABC and CRM data inputs of an airline company. In mapping the profit-based ranking of corporate customers into the current market segments, the relative profitability of each market segment can be determined.