Exploration of the ordering for a sequential airport ground movement algorithm

Guiding aircraft around the airport's surface while ensuring conflict-free routings is an important problem at airports. Sequential routing and scheduling algorithms can be advantageous for providing fast online solutions for decision support systems to help controllers. However, the effectiveness of such algorithms can depend upon the sequence of consideration of the aircraft, which is often chosen to be first-come-first-served. This research analyses the effects of different heuristics to find better sequences. Results are presented, utilising real data from Zurich Airport. These show that sophisticated heuristics can substantially improve the solution with comparatively little additional computational time. Furthermore, one approach aims to modify relatively few existing routes as it progresses, in order to minimise the workload of the controllers in communicating changes in an online environment

Exploration of the ordering for a sequential airport ground movement algorithm

Guiding aircraft around the airport's surface while ensuring conflict-free routings is an important problem at airports. Sequential routing and scheduling algorithms can be advantageous for providing fast online solutions for decision support systems to help controllers. However, the effectiveness of such algorithms can depend upon the sequence of consideration of the aircraft, which is often chosen to be first-come-first-served. This research analyses the effects of different heuristics to find better sequences. Results are presented, utilising real data from Zurich Airport. These show that sophisticated heuristics can substantially improve the solution with comparatively little additional computational time. Furthermore, one approach aims to modify relatively few existing routes as it progresses, in order to minimise the workload of the controllers in communicating changes in an online environment

Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset

© 2018 Informa UK Limited, trading as Taylor & Francis Group. With most Additive Manufacturing (AM) technology variants, build processes take place inside an internal enclosed build container, referred to as a ‘build volume’. It has been demonstrated that the effectiveness with which this volume is filled with product geometries forms an important determinant of overall process efficiency in AM. For effective operations management, it is important to understand not only the problem faced, but also which methods have proved effective (or ineffective) for problems with these characteristics in the past. This research aims to facilitate this increased understanding. The build volume packing task can be formulated as a three-dimensional irregular packing (3DIP) problem, which is a combinatorial optimisation problem requiring the configuration of a set of arbitrary volumetric items. This paper reviews existing general cutting and packing taxonomies and provides a new specification which is more appropriate for classifying the problems encountered in AM. This comprises a clear-cut problem definition, a set of precise categorisation criteria for objectives and problem instances, and a simple notation. Furthermore, the paper establishes an improved terminology with terms that are familiar to, but not limited to, researchers and practitioners in the field of AM. Finally, this paper describes a new dataset to be used in the evaluation of existing and proposed computational solution methods for 3DIP problems encountered in AM and discusses the importance of this research for further underpinning work

Scheduling airline reserve crew using a probabilistic crew absence and recovery model

Airlines require reserve crew to replace delayed or absent crew, with the aim of preventing consequent flight cancellations. A reserve crew schedule specifies the duty periods for which different reserve crew will be on standby to replace any absent crew. For both legal and health-and-safety reasons the reserve crew's duty period is limited, so it is vital that these reserve crew are available at the right times, when they are most likely to be needed and will be most effective. Scheduling a reserve crew unnecessarily, or earlier than needed, wastes reserve crew capacity. Scheduling a reserve crew too late means either an unrecoverable cancellation or a delay waiting for the reserve crew to be available. Determining when to schedule these crew can be a complex problem , since one crew member could potentially cover a vacancy on any one of a number of different flights, and flights interact with each other, so a delay or cancellation for one flight can affect a number of later flights. This work develops an enhanced mathematical model for assessing the impact of any given reserve crew schedule, in terms of reduced total expected cancellations and any resultant reserve induced delays, whilst taking all of the available information into account, including the schedule structure and interactions between flights, the uncertainties involved, and the potential for multiple crew absences on a single flight. The interactions between flights have traditionally made it very hard to predict the effects of cancellations or delays, and hence to predict when best to allocate reserve crew and lengthy simulation runs have traditionally been used to make these predictions. This work is motivated by the airline industry's need for improved mathematical models to replace the time-consuming simulation-based approaches. The improved predictive probabilistic model which is introduced here is shown to produce results that match a simulation model to a high degree of accuracy, in a much shorter time, making it an effective and accurate surrogate for simulation. The modelling of the problem also provides insights into the complexity of the problem that a purely simulation based approach would miss. The increased speed enables potential deployment within a real time decision support context, comparing alternative recovery decisions as disruptions occur. To illustrate this, the model is used in this paper as a fitness function in meta-heuristics algorithms to generate disruption minimising reserve crew schedules for a real airline schedule. These are shown to be of a high quality, demonstrating the effectiveness and reliability of the proposed approach

Tabu assisted guided local search approaches for freight service network design

The service network design problem (SNDP) is a core problem in freight transportation. It involves the determination of the most cost-effective transportation network and the character- istics of the corresponding services, subject to various constraints. The scale of the problem in real-world applications is usually very large, especially when the network contains both the geographical information and the temporal constraints which are necessary for modelling mul- tiple service-classes and dynamic events. The development of time-efficient algorithms for this problem is, therefore, crucial for successful real-world applications. Earlier research indicated that guided local search (GLS) was a promising solution method for this problem. One of the advantages of GLS is that it makes use of both the information collected during the search as well as any special structures which are present in solutions. Building upon earlier research, this paper carries out in-depth investigations into several mechanisms that could potentially speed up the GLS algorithm for the SNDP. Specifically, the mechanisms that we have looked at in this paper include a tabu list (as used by tabu search), short-term memory, and an aspiration crite- rion. An efficient hybrid algorithm for the SNDP is then proposed, based upon the results of these experiments. The algorithm combines a tabu list within a multi-start GLS approach, with an efficient feasibility-repairing heuristic. Experimental tests on a set of 24 well-known service network design benchmark instances have shown that the proposed algorithm is superior to a previously proposed tabu search method, reducing the computation time by over a third. In ad- dition, we also show that far better results can be obtained when a faster linear program solver is adopted for the sub-problem solution. The contribution of this paper is an efficient algorithm, along with detailed analyses of effective mechanisms which can help to increase the speed of the GLS algorithm for the SNDP

Exploration of the ordering for a sequential airport ground movement algorithm

Guiding aircraft around the airport's surface while ensuring conflict-free routings is an important problem at airports. Sequential routing and scheduling algorithms can be advantageous for providing fast online solutions for decision support systems to help controllers. However, the effectiveness of such algorithms can depend upon the sequence of consideration of the aircraft, which is often chosen to be first-come-first-served. This research analyses the effects of different heuristics to find better sequences. Results are presented, utilising real data from Zurich Airport. These show that sophisticated heuristics can substantially improve the solution with comparatively little additional computational time. Furthermore, one approach aims to modify relatively few existing routes as it progresses, in order to minimise the workload of the controllers in communicating changes in an online environment