Maximizing the expected net present value ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Maximizing the expected net present value of a project with phase-type distributed activity durations: An efficient globally optimal solution procedure
Author(s) :
Journal title :
European Journal of Operational Research
Pages :
16-22
Publisher :
Elsevier
Publication date :
2018-05-16
ISSN :
0377-2217
English keyword(s) :
Project scheduling
Project management
NPV maximization
SNPV
Stochastic activity durations
Project management
NPV maximization
SNPV
Stochastic activity durations
HAL domain(s) :
Sciences de l'Homme et Société/Gestion et management
English abstract : [en]
We study projects with activities that have stochastic durations that are modeled using phase-type distributions. Intermediate cash flows are incurred during the execution of the project. Upon completion of all project ...
Show more >We study projects with activities that have stochastic durations that are modeled using phase-type distributions. Intermediate cash flows are incurred during the execution of the project. Upon completion of all project activities a payoff is obtained. Because activity durations are stochastic, activity starting times cannot be defined at the start of the project. Instead, we have to rely on a policy to schedule activities during the execution of the project. The optimal policy schedules activities such that the expected net present value of the project is maximized. We determine the optimal policy using a new continuous-time Markov chain and a backward stochastic dynamic program. Although the new continuous-time Markov chain allows to drastically reduce memory requirements (when compared to existing methods), it also allows activities to be preempted; an assumption that is not always desirable. We prove, however, that it is globally optimal not to preempt activities if cash flows are incurred at the start of an activity. Moreover, this proof holds regardless of the duration distribution of the activities. A computational experiment shows that we significantly outperform current state-of-the-art procedures. On average, we improve computational efficiency by a factor of 600, and reduce memory requirements by a factor of 321.Show less >
Show more >We study projects with activities that have stochastic durations that are modeled using phase-type distributions. Intermediate cash flows are incurred during the execution of the project. Upon completion of all project activities a payoff is obtained. Because activity durations are stochastic, activity starting times cannot be defined at the start of the project. Instead, we have to rely on a policy to schedule activities during the execution of the project. The optimal policy schedules activities such that the expected net present value of the project is maximized. We determine the optimal policy using a new continuous-time Markov chain and a backward stochastic dynamic program. Although the new continuous-time Markov chain allows to drastically reduce memory requirements (when compared to existing methods), it also allows activities to be preempted; an assumption that is not always desirable. We prove, however, that it is globally optimal not to preempt activities if cash flows are incurred at the start of an activity. Moreover, this proof holds regardless of the duration distribution of the activities. A computational experiment shows that we significantly outperform current state-of-the-art procedures. On average, we improve computational efficiency by a factor of 600, and reduce memory requirements by a factor of 321.Show less >
Language :
Anglais
Popular science :
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