Airline Crew Scheduling with Potts Neurons

A Potts feedback neural network approach for finding good solutions to resource allocation problems with a non-fixed topology is presented. As a target application the airline crew scheduling problem is chosen. The topological complication is handled by means of a propagator defined in terms of Potts neurons. The approach is tested on artificial random problems tuned to resemble real-world conditions. Very good results are obtained for a variety of problem sizes. The computer time demand for the approach only grows like \mbox{(number of flights)}^3. A realistic problem typically is solved within minutes, partly due to a prior reduction of the problem size, based on an analysis of the local arrival/departure structure at the single airportsComment: 9 pages LaTeX, 3 postscript figures, uufiles forma

Network planning for scheduling operations in air cargo handling : a tool in medium term goods flow control

In the hub of a hub-and-spokes network for airfreight transportation, the main part of the incoming and outgoing goodsflow is in special loading units for airfreight. These loading units are metal pallets and containers up to eighteen cubic meters in size. The key part of operations in the hub is breaking down incoming loading units and building up outgoing loading units. These operations are subject to resource restrictions (limited number of platforms and manpower teams) and time restrictions (between arrival times of flights and departure times of flights). With some substitutions, standard software for network planning is successfully applied to scheduling transshipment operations of loading units

Examining factors contributing to fatigue among KLM cabin crew

This research study examines the factors influencing fatigue levels among cabin crew members. The study utilizes interviews and a survey questionnaire to collect data on various variables. While factor analysis was initially intended, it was found unsuitable due to low variable correlation and a high unsuitable number of identified components. Consequently, regression and ANOVA analysis was performed. The dependent variables, reveals average fatigue levels among respondents compared to other healthy adult populations. The independent variable, time difference of 6-7 hours and quality of the hotel facilities have a positive significant influence and a flight duration of 8-12 hours has a negative significant influence on current fatigue levels in the regression model. Alarming fatigue signals from cabin crew are observed from West-American destinations and the Airbus A330 aircraft. The influence of WIFI on fatigue levels is also studied with a situational recall experiment. A paired sample t-test shows a significant difference of increased fatigue levels on planes with WIFI than those without. Although the appropriateness of using current fatigue levels as the dependent variable is questioned, the findings offer valuable insights into identifying fatigue among cabin crew members. These results emphasize the importance of considering multiple factors to mitigate fatigue-related issues in the aviation industry.Este estudo de investigação examina os factores que influenciam os níveis de fadiga dos membros da tripulação de cabina. O estudo utiliza entrevistas e um questionário de inquérito para recolher dados sobre diversas variáveis. Embora inicialmente se pretendesse realizar uma análise factorial, esta foi considerada inadequada devido à baixa correlação entre as variáveis e a um número elevado e inadequado de componentes identificados. Consequentemente, foi efectuada uma análise de regressão e ANOVA. A variável dependente revela os níveis médios de fadiga dos inquiridos em comparação com outras populações adultas saudáveis. A variável independente, a diferença horária de 6-7 horas e a qualidade das instalações do hotel têm uma influência significativa positiva e a duração do voo de 8-12 horas tem uma influência significativa negativa nos actuais níveis de fadiga no modelo de regressão. Os sinais alarmantes de fadiga da tripulação de cabina centram-se nos destinos da América Ocidental e no avião Airbus A330. A influência do WIFI nos níveis de fadiga é também estudada através de uma experiência de recordação situacional. Um teste t de amostras emparelhadas mostra uma diferença significativa de aumento dos níveis de fadiga nos aviões com WIFI em relação aos aviões sem WIFI. Embora se questione a adequação da utilização dos níveis de fadiga actuais como variável dependente, os resultados oferecem informações valiosas para a identificação da fadiga entre os membros da tripulação de cabina. Estes resultados sublinham a importância de considerar múltiplos factores para atenuar os problemas relacionados com a fadiga na indústria da aviação

Planning the Size and Organization of KLM's Aircraft Maintenance Personnel

Develops a decision support system (DSS) for the aircraft maintenance department of KLM Royal Dutch Airlines at Schiphol Airport in Amsterdam, Netherlands. Tasks of the department; Support provided by the DSS to management; Analyzing several capacity planning problems related to the size and the organization of the workforce

A simulation scenario based mixed integer programming approach to airline reserve crew scheduling under uncertainty

The environment in which airlines operate is uncertain for many reasons, for example due to the effects of weather, traffic or crew unavailability (due to delay or sickness). This work focuses on airline reserve crew scheduling under crew absence uncertainty and delay for an airline operating a single hub and spoke network. Reserve crew can be used to cover absent crew or delayed connecting crew. A fixed number of reserve crew are available for scheduling and each requires a daily standby duty start time. This work proposes a mixed integer programming approach to scheduling the airline’s reserve crew. A simulation of the airline’s operations with stochastic journey time and crew absence inputs (without reserve crew) is used to generate input disruption scenarios for the mixed integer programming simulation scenario model (MIPSSM) formulation. Each disruption scenario corresponds to a record of all of the disruptions that may occur on the day of operation which are solvable by using reserve crew. A set of disruption scenarios form the input of the MIPSSM formulation, which has the objective of finding the reserve crew schedule that minimises the overall level of disruption over the set of input scenarios. Additionally, modifications of the MIPSSM are explored, a heuristic solution approach and a reserve use policy derived from the MIPSSM are introduced. A heuristic based on the proposed MIPSSM outperforms a range of alternative approaches. The heuristic solution approach suggests that including the right disruption scenarios is as important as the quantity of disruption scenarios that are added to the MIPSSM. An investigation into what makes a good set of scenarios is also presented

Probabilistic Airline Reserve Crew Scheduling Model

This paper introduces a probabilistic model for airline reserve crew scheduling. The model can be applied to any schedules which consist of a stream of departures from a single airport. We assume that reserve crew demand can be captured by an independent probability of crew absence for each departure. The aim of our model is to assign some fixed number of available reserve crew in such a way that the overall probability of crew unavailability in an uncertain operating environment is minimised. A comparison of different probabilistic objective functions, in terms of the most desirable simulation results, is carried out, complete with an interpretation of the results. A sample of heuristic solution methods are then tested and compared to the optimal solutions on a set of problem instances, based on the best objective function found. The current model can be applied in the early planning phase of reserve crew scheduling, when very little information is known about crew absence related disruptions. The main conclusions include the finding that the probabilistic objective function approach gives solutions whose objective values correlate strongly with the results that these solutions will get on average in repeated simulations. Minimisation of the sum of the probabilities of crew unavailability was observed to be the best surrogate objective function for reserve crew schedules that perform well in simulation. A list of extensions that could be made to the model is then provided, followed by conclusions that summarise the findings and important results obtained