Les Inscriptions à la Bibliothèque sont ouvertes en
ligne via le site: https://biblio.enp.edu.dz
Les Réinscriptions se font à :
• La Bibliothèque Annexe pour les étudiants en
2ème Année CPST
• La Bibliothèque Centrale pour les étudiants en Spécialités
A partir de cette page vous pouvez :
Retourner au premier écran avec les recherches... |
Détail de l'auteur
Auteur Siqian Shen
Documents disponibles écrits par cet auteur
Affiner la rechercheExpectation and chance-constrained models and algorithms for insuring critical paths / Siqian Shen in Management science, Vol. 56 N° 10 (Octobre 2010)
[article]
in Management science > Vol. 56 N° 10 (Octobre 2010) . - pp. 1794-1814
Titre : Expectation and chance-constrained models and algorithms for insuring critical paths Type de document : texte imprimé Auteurs : Siqian Shen, Auteur ; J. Cole Smith, Auteur ; Shabbir Ahmed, Auteur Année de publication : 2010 Article en page(s) : pp. 1794-1814 Note générale : Management Langues : Anglais (eng) Mots-clés : Project management Integer programming Reformulation-linearization technique Chance-constrained programming Sample average approximation Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : In this paper, we consider a class of two-stage stochastic optimization problems arising in the protection of vital arcs in a critical path network. A project is completed after a series of dependent tasks are all finished. We analyze a problem in which task finishing times are uncertain but can be insured a priori to mitigate potential delays. A decision maker must trade off costs incurred in insuring arcs with expected penalties associated with late project completion times, where lateness penalties are assumed to be lower semicontinuous nondecreasing functions of completion time. We provide decomposition strategies to solve this problem with respect to either convex or nonconvex penalty functions. In particular, for the nonconvex penalty case, we employ the reformulation-linearization technique to make the problem amenable to solution via Benders decomposition. We also consider a chance-constrained version of this problem, in which the probability of completing a project on time is sufficiently large. We demonstrate the computational efficacy of our approach by testing a set of size-and-complexity diversified problems, using the sample average approximation method to guide our scenario generation. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/cgi/content/abstract/56/10/1794 [article] Expectation and chance-constrained models and algorithms for insuring critical paths [texte imprimé] / Siqian Shen, Auteur ; J. Cole Smith, Auteur ; Shabbir Ahmed, Auteur . - 2010 . - pp. 1794-1814.
Management
Langues : Anglais (eng)
in Management science > Vol. 56 N° 10 (Octobre 2010) . - pp. 1794-1814
Mots-clés : Project management Integer programming Reformulation-linearization technique Chance-constrained programming Sample average approximation Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : In this paper, we consider a class of two-stage stochastic optimization problems arising in the protection of vital arcs in a critical path network. A project is completed after a series of dependent tasks are all finished. We analyze a problem in which task finishing times are uncertain but can be insured a priori to mitigate potential delays. A decision maker must trade off costs incurred in insuring arcs with expected penalties associated with late project completion times, where lateness penalties are assumed to be lower semicontinuous nondecreasing functions of completion time. We provide decomposition strategies to solve this problem with respect to either convex or nonconvex penalty functions. In particular, for the nonconvex penalty case, we employ the reformulation-linearization technique to make the problem amenable to solution via Benders decomposition. We also consider a chance-constrained version of this problem, in which the probability of completing a project on time is sufficiently large. We demonstrate the computational efficacy of our approach by testing a set of size-and-complexity diversified problems, using the sample average approximation method to guide our scenario generation. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/cgi/content/abstract/56/10/1794