Gelişmiş Arama

Basit öğe kaydını göster

dc.contributor.authorSatic, Ugur
dc.contributor.authorJacko P.
dc.contributor.authorKirkbride C.
dc.date.accessioned2024-08-29T08:57:38Z
dc.date.available2024-08-29T08:57:38Z
dc.date.issued2024en_US
dc.identifier.issn03772217
dc.identifier.urihttps://doi.org/10.1016/j.ejor.2023.10.046
dc.identifier.urihttps://hdl.handle.net/20.500.12573/2359
dc.description.abstractWe consider the dynamic and stochastic resource-constrained multi-project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite-horizon discrete-time Markov decision process and seeks to maximise the expected discounted long-run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision-maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule-based algorithm and a reactive genetic algorithm.en_US
dc.language.isoengen_US
dc.publisherElsevier B.V.en_US
dc.relation.isversionof10.1016/j.ejor.2023.10.046en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectProject schedulingen_US
dc.subjectMarkov decision processesen_US
dc.subjectApproximate dynamic programmingen_US
dc.subjectDynamic resource allocationen_US
dc.subjectDynamic programmingen_US
dc.titleA simulation-based approximate dynamic programming approach to dynamic and stochastic resource-constrained multi-project scheduling problemen_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0002-9160-0006en_US
dc.contributor.institutionauthorSatic, Ugur
dc.identifier.volume315en_US
dc.identifier.issue2en_US
dc.identifier.startpage454en_US
dc.identifier.endpage469en_US
dc.relation.journalEuropean Journal of Operational Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Bu öğenin dosyaları:

Thumbnail

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster