We study the problem of levelling resources in a project with generalized precedence relationships, given a deadline for the completion of all the activities and variable execution intensities and flexible durations of the activities. Variable execution intensities have been taken into account firstly by Kis in 2005 applied to a real world scenario in which, due to the physical characteristics of some manufacturing processes, the effort associated with a certain activity for its execution may vary over time. Generalized precedence relationships and variable intensity execution and duration have not been dealt with together to the best of our knowledge. For this novel problem we propose a mixed integer linear programming formulation, a lower bound based on Lagrangian relaxation, and a branch and bound algorithm. Computational results on known benchmarks are provided.
Bianco, L., Caramia, M., Giordani, S. (2016). Resource levelling in project scheduling with generalized precedence relationships and variable execution intensities. OR SPECTRUM, 38(2), 405-425 [10.1007/s00291-016-0435-1].
Resource levelling in project scheduling with generalized precedence relationships and variable execution intensities
BIANCO, LUCIO;CARAMIA, MASSIMILIANO;GIORDANI, STEFANO
2016-01-01
Abstract
We study the problem of levelling resources in a project with generalized precedence relationships, given a deadline for the completion of all the activities and variable execution intensities and flexible durations of the activities. Variable execution intensities have been taken into account firstly by Kis in 2005 applied to a real world scenario in which, due to the physical characteristics of some manufacturing processes, the effort associated with a certain activity for its execution may vary over time. Generalized precedence relationships and variable intensity execution and duration have not been dealt with together to the best of our knowledge. For this novel problem we propose a mixed integer linear programming formulation, a lower bound based on Lagrangian relaxation, and a branch and bound algorithm. Computational results on known benchmarks are provided.File | Dimensione | Formato | |
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