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Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage.

By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations.

This model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work.

Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management.

The implementation and control processes of project planning and scheduling involve a wide range of methods and tools. Despite the development and modification and integration of the project management theory with newer scheduling approaches in particular, practitioners’ views on the efficiency and effectiveness of these methods and tools differ. This situation can be attributed in part to a lack of understanding of the most appropriate basis for implementing these methods and tools. This study, therefore, aims to overcome this deficiency by conceptualizing and adopting a taxonomy of planning and scheduling methods.

The study is based on a review and discourse analysis of the literature covering a large number of theoretical and empirical studies. The underlying theories of various planning and scheduling methods were analyzed with respect to the taxonomy criteria adopted in the study.

Using the taxonomy, the key characteristics of planning and scheduling methods considered in this study were identified and interpreted. These included concepts and theories; key features; suitability and usability; and benefits and limitations. Overall, the findings suggest that project managers should consider taxonomy as a support tool for selecting and prioritizing the most appropriate method or combination of methods for managing their projects. Recommendations include the need for more advanced or multi-dimensional taxonomies to cope with the diversity of project type and size.

The results of the study allow project managers to improve their current practices by utilizing taxonomy when considering the implementation of planning and scheduling methods. Moreover, taxonomy can be considered as a tool to promote learning on the part of those less experienced in planning and scheduling. Taxonomy can be considered as an initial platform for further research in this area.

Historically, scheduling of production activities has been one of the most important management problems. Scheduling involves the planning and the co‐ordination of the various activities to achieve the optimum utilisation of resources over a given time period. Production scheduling differs with the typology of the production systems. Various production systems that are encountered in practice are: continuous production, mass production, batch production, job shop production and projects. In this article, we attempt only to discuss the project scheduling problems.

This chapter discusses techniques for scheduling and organizing staff to meet guest demands and financial obligations. Key building blocks relevant to labor management are explained, such as productivity, fixed and variable labor hours, and the development of realistic performance standards to help organizations optimize productivity. As a next step, this chapter illuminates the importance of providing management labor standards and staffing models, which are key management tools. Lodging and food and beverage labor strategies are presented. Finally, effective planning of labor scheduling is also discussed.

Provides a review of the main features of the software available in the market that deal with retention management. Reviews a range of recently published works discussing retention management features and establishes a list of criteria. Reviews the most important and widely available software tools for retention management against these features and makes a comparison. Provides information about each how each criterion is satisfied by the reviewed tools and gives an overview on how the industry approaches retention management. Reveals that the list of the reviewed software is not exhaustive; also, the evaluation could have included more information on the tools and examples of how they can actually be used. Maintains, however, that this is a very useful source of information and impartial advice for everyone interested in retention management. Especially useful is the discussion on the future issues identified. Fulfils an identified information need for a discussion on how existing systems deal with retention management and offers help to an individual who wants to carry out research in this area.

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 article aims to critically analyze the project management developed by research laboratories of a Brazilian university in order to enhance their performance. For this, a critical analysis was performed to identify existing opportunities about the management of schedules and resources. Additionally, a software was developed to enable performance improvement.

The methodological procedures used were literature review, for a theoretical foundation, and case study conducted with semi-structured interviews, documentary research and on-site visits. Through a detailed critical analysis of the laboratories' management, it was possible to understand the activities developed and map the main difficulties observed.

A total of five plausible points of improvement were identified, namely reduced teams and accumulation of activities; team seasonality; centralized management; deviations from projects and schedule control. Based on the theoretical foundation, it has been proposed adjustments to minimize the mentioned difficulties that can greatly contribute for better management efficiency of multiple research projects. In addition, a software was structured based on the proposed improvements. The laboratories' performance was monitored for a month and significant improvements were observed.

The information presented here may be of great value to other researchers interested in enhancing research laboratory performance.

The academic literature presents several examples of project management guidelines application in different organizations; however, there are few studies about the application of them in research laboratories and how to improve their performance.

The purpose of this paper is to mine information on the construction process of previous projects to develop a construction plan that meets both quality requirements and schedule constraints.

This paper uses a failure mode and effect analysis to evaluate the construction quality of 311 apartments in Shanghai. The authors also evaluate construction-scheduling control using the earned value management technique and implement an artificial neural network to correlate the results. The authors then develop a quality risk and schedule correlation model based on Big Data. The model can predict the relationship between the planned schedule and the project quality risk using multiple variables such as the number of layers, the schedule performance index and budget costs.

The methodology offers an innovative approach for assessment on the relationship between quality risk and project schedule. The authors have also built a multiple regression analysis model for comparative purposes with the model. The results show that the proposed model can better describe the relationship. The model can provide a quantitative quality risk value that changes with the planned schedule, as well as help project managers to understand the relationship between quality risk and project scheduling more accurately.

The research approach only focuses on quality risk under the impact of scheduling. Future efforts might focus on developing a model that connects failure models with project schedules and costs in order to improve the effort of quality management.

The model based on Big Data in this paper is developed using real projects and reflects the relationship between project quality risk and scheduling in real environments. The created application provides support for project managers to develop and adjust quality plans and schedules, thereby reducing deviations in quality and scheduling objectives.

The authors make full use of historical project data from the perspective of both quality and schedule management, and provide a novel method to intelligently and objectively analyze the relationship between quality risk and scheduling.

The building sector of the construction industry incorporates a precipitous and volatile nature with poor safety conditions being prevalent, owing to its inability to determine an appropriate trade-off between productivity and safety. This disproportionate trade-off produces production pressures, which contribute poorly to construction performance, by encouraging workers to prioritise their working productivity ahead of safety. The purpose of this study is to investigate the impacts of production pressures in the building sector and propose mitigation strategies accordingly.

A systematic review of literature was conducted, and secondary data were extracted from peer-reviewed journal papers. The data was then analysed to achieve the objectives of this study.

The main causes of production pressures are tight construction schedules, ineffective management and construction rework. Furthermore, the negative effects of production pressures are increased levels of stress in employees, reduced craftsmanship, encouraging accident-prone environments and decreasing employee’s safety behaviour. Effective mitigation strategies in relation to scheduling, leadership, communication and motivation were proposed. Finally, a causal loop diagram of production pressures in the building sector was developed.

This research will assist in creating a safer working environment within the building sector, by providing useful information regarding the severity of production pressures and suggesting mitigation strategies that can be implemented in the construction projects.

The purpose of this paper is to present a detailed case study on the methods and organisational structure used for controlling the time schedule for a large and complex project. The paper discusses the use of “project controlling”, a term used to describe project control by a third-party organisation.

The researchers used action research to collect data for the case study. A member of the research team was a “participant-observer” on the project on a day-to-day basis for a period of 18 months collecting and analysing data which were subsequently analysed by a mixed methods approach.

The use of a “Project Controlling Unit” operated by an independent adviser organisation has significant advantages over traditional methods. It can provide timely, consolidated, independent guidance to the client and assistance to other participating organisations.

The research has confirmed the effectiveness of the method on the project under study.

The findings provide guidance for enhanced project control on large complex infrastructural projects that will be of interest to other researchers, other clients and other construction organisations both within China and internationally.

Organisations that seek to develop Project Controlling Units to implement the methods described in this paper will need to review their recruitment and training strategies to ensure that appropriate and experienced staffs are engaged.

The paper extends the knowledge relating to “project controlling” method. The findings provide additional insights to progress reporting and the management of construction production on HOPSCA and other large infrastructural projects.