A Network Flow Approach to Crew Scheduling Based on an Analogy to an Aircraft/Train Maintenance Routing Problem

Mellouli, Taïeb

doi:10.1007/978-3-642-56423-9_6

Cited by 15 publications

(2 citation statements)

References 11 publications

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“…For our experiments, we formulated (2) as a mixed-integer linear program -that is, X t is formulated as a set of linear inequalities and integer constraints -we solve using a standard solver. Note that instead of using a compact formulation as discussed in Santos et al (2014) or an extremepoint formulation as proposed by Burke and Curtois (2014), we use a multi-commodity flow formulation in the spirit of (Mellouli, 2001) and (Römer and Mellouli, 2011) dealing with airline crew scheduling.…”

Section: Deterministic Lookahead Policiesmentioning

confidence: 99%

Future Demand Uncertainty In Personnel Scheduling: Investigating Deterministic Lookahead Policies Using Optimization And Simulation

Römer¹,

Mellouli²

2016

ECMS 2016 Proceedings Edited by Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose

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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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Section: Deterministic Lookahead Policiesmentioning

confidence: 99%

Future Demand Uncertainty In Personnel Scheduling: Investigating Deterministic Lookahead Policies Using Optimization And Simulation

Römer¹,

Mellouli²

2016

ECMS 2016 Proceedings Edited by Thorsten Claus, Frank Herrmann, Michael Manitz, Oliver Rose

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“…Column generation using network models to price new columns is proposed by Mason and Smith (1998). Mellouli (2001) uses a "state-expanded aggregated time-space network"…”

Section: Network Flow Tour Scheduling (Airline Mass Transit Nurse Amentioning

confidence: 99%

A Network-Based Mathematical Programming Approach to Optimal Rostering of Continuous Heterogeneous Workforces

Knighton¹

2005

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, Problems of this complexity routinely occur in industry but are computationally prohibitive to solve. Integer solutions to our tour scheduling problem are found without branching, bounding, or cutting schemes. This allows the CPLEX interior-point method to generate solutions orders of magnitude faster than the corresponding integer program does. Our methodology is demonstrated by finding an optimal tour schedule for staffing the four main Computer Commons at Arizona State University, a problem of order 10,000 binary variables and 1,000 constraints in 0.12 CPU seconds. It is also used to perform the calculations for a generalized staff sizing problem.

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Fallstudie Transportlogistik im ÖPV: Netzwerkbasierte Modellierung und Optimierung

Springer-Lehrbuch

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A Network Flow Approach to Crew Scheduling Based on an Analogy to an Aircraft/Train Maintenance Routing Problem

Cited by 15 publications

References 11 publications

Future Demand Uncertainty In Personnel Scheduling: Investigating Deterministic Lookahead Policies Using Optimization And Simulation

Future Demand Uncertainty In Personnel Scheduling: Investigating Deterministic Lookahead Policies Using Optimization And Simulation

A Network-Based Mathematical Programming Approach to Optimal Rostering of Continuous Heterogeneous Workforces

Fallstudie Transportlogistik im ÖPV: Netzwerkbasierte Modellierung und Optimierung

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