Abstract
Nowadays, organizations are often faced with the development of complex and innovative projects. This type of projects often involves performing tasks which are subject to failure. Thus, in many such projects several possible alternative actions are considered and performed simultaneously. Each alternative is characterized by cost, duration, and probability of technical success. The cost of each alternative is paid at the beginning of the alternative and the project payoff is obtained whenever an alternative has been completed successfully. For this problem one wishes to find the optimal schedule, i.e., the starting time of each alternative, such that the expected net present value is maximized. This problem has been recently proposed in Ranjbar (Int Trans Oper Res 20(2):251–266, 2013), where a branch-and-bound approach is reported. Since the problem is NP-Hard, here we propose to solve the problem using genetic algorithms.
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Notes
- 1.
Since each alternative consists of a single activity, here and hereafter we will use indifferently alternative and activity.
- 2.
The number of precedence-related activity pairs divided by the theoretically maximum number of such pairs in the network [20].
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Acknowledgements
This work was partially supported by projects PTDC/EGE-GES/117692/ 2010 and NORTE-07-0124-FEDER-000057 funded by the North Portugal Regional Operational Programme (ON.2 – O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF) and the Programme COMPETE, and by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT).
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Fontes, D.B.M.M., Gonçalves, J.F. (2015). A Genetic Algorithm for Scheduling Alternative Tasks Subject to Technical Failure. In: Migdalas, A., Karakitsiou, A. (eds) Optimization, Control, and Applications in the Information Age. Springer Proceedings in Mathematics & Statistics, vol 130. Springer, Cham. https://doi.org/10.1007/978-3-319-18567-5_7
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