Modern heuristic techniques for combinatorial problems
Resource-constrained project scheduling: a survey of recent developments
Computers and Operations Research
The complexity of machine scheduling for stability with a single disrupted job
Operations Research Letters
Reactive scheduling in the multi-mode RCPSP
Computers and Operations Research
A heuristic approach for resource constrained project scheduling with uncertain activity durations
Computers and Operations Research
Dynamic application model for scheduling with uncertainty on reconfigurable architectures
International Journal of Reconfigurable Computing - Special issue on selected papers from the international workshop on reconfigurable communication-centric systems on chips (ReCoSoC' 2010)
A bi-objective scheduling algorithm for desktop grids with uncertain resource availabilities
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
A bi-objective model for robust berth allocation scheduling
Computers and Industrial Engineering
A mathematical model for the management of a Service Center
Mathematical and Computer Modelling: An International Journal
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Research concerning project planning under uncertainty has primarily focused on the stochastic resource-constrained project scheduling problem (stochastic RCPSP), an extension of the basic RCPSP, in which the assumption of deterministic activity durations is dropped. In this paper, we introduce a new variant of the RCPSP, for which the uncertainty is modeled by means of resource availabilities that are subject to unforeseen breakdowns. Our objective is to build a robust schedule that meets the project deadline and minimizes the schedule instability cost, defined as the expected weighted sum of the absolute deviations between the planned and the actually realized activity starting times during project execution. We describe how stochastic resource breakdowns can be modeled, which reaction is recommended, when a resource infeasibility occurs due to a breakdown, and how one can protect the initial schedule from the adverse effects of potential breakdowns. An extensive computational experiment is used to show the relative performance of the proposed proactive and reactive strategies. It is shown that protection of the baseline schedule, coupled with intelligent schedule recovery, yields significant performance gains over the use of deterministic scheduling approaches in a stochastic setting.