Digital Library of the European Council for Modelling and Simulation

Title:	Future Demand Uncertainty In Personnel Scheduling: Investigating Deterministic Lookahead Policies Using Optimization And Simulation
Authors:	Michael Roemer, Taieb Mellouli
Published in:	(2016).ECMS 2016 Proceedings edited by: Thorsen Claus, Frank Herrmann, Michael Manitz, Oliver Rose, European Council for Modeling and Simulation. doi:10.7148/2016 ISBN: 978-0-9932440-2-5 30^th European Conference on Modelling and Simulation, Regensburg Germany, May 31^st – June 3^rd, 2016
Citation format:	Michael Roemer, Taieb Mellouli (2016). Future Demand Uncertainty In Personnel Scheduling: Investigating Deterministic Lookahead Policies Using Optimization And Simulation, ECMS 2016 Proceedings edited by: Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose European Council for Modeling and Simulation. doi:10.7148/2016-0502
DOI:	http://dx.doi.org/10.7148/2016-0502
Abstract:	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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