Abstract. Airlines' and railways' expensive resources, especially crews and aircraft or trains are to be optimally scheduled to cover flights or trips of timetables. Aircraft and trains require regular servicing. They are to be routed as to regularly pass through one of the few maintenance bases, e.g., every three to four operation days for inspection. Apart from complicating workrules, crews are to be scheduled so as to "pass through" their horne bases weekly for a two-day rest. This analogy is utilized in order to recognize opportunities for integrating classical planning processes for crew scheduling, and to transfer solution methodologies. A mixed-integer flow model based on a state-expanded aggregated time-space network is developed. This mathematical model, used to solve large-scale maintenance routing problems for German Rail's intercity trains, is extended to the airline crew scheduling problem where maintenance states are replaced by crew states . The resulting network flow approach to an integrated crew scheduling process involving multiple crew domiciles and various crew requests is tested with problems from a European airline. A decision support system and computational results are presented.
One of the main characteristics of personnel scheduling problems is the multitude of rules governing schedule feasibility and quality. This paper deals with an issue in personnel scheduling which is both relevant in practice and often neglected in academic research: When evaluating a schedule for a given planning period, the scheduling history preceding this period has to be taken into account. On the one hand, the history restricts the space of possible schedules, in particular at the beginning of the planning period and with respect to rules a scope transcending the planning period. On the other hand, the schedule for the planning period under consideration affects the solution space of future planning periods. In particular if the demand in future planning periods is subject to uncertainty, an interesting question is how to account for these effects when optimizing the schedule for a given planning period. The resulting planning problem can be considered as a multistage stochastic optimization problem which can be tackled by different modeling and solution approaches. In this paper, we compare different deterministic lookahead policies in which a one-week scheduling period is extended by an artificial lookahead period. In particular, we vary both the length and the way of creating demand forecasts for this lookahead period. The evaluation is carried out using a stochastic simulation in which weekly demands are sampled and the scheduling problems are solved exactly using mixed integer linear programming techniques. Our computational experiments based on data sets from the Second International Nurse Rostering Competition show that the length of the lookahead period is crucial to find good-quality solutions in the considered setting.
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