000029034 001__ 29034
000029034 005__ 20150420164350.0
000029034 0248_ $$aoai:doc.rero.ch:20120427142657-TK$$particle$$ppostprint$$pcdu33$$prero_explore$$phesge$$zreport$$zthesis$$zbook$$zjournal$$zcdu16$$zhegge$$zpreprint$$zcdu1$$zdissertation$$zthesis_urn$$zcdu34
000029034 041__ $$aeng
000029034 080__ $$a33
000029034 100__ $$aHaurie, Alain$$uUniversité de Genève
000029034 245__ $$9eng$$aA stochastic programming approach to manufacturing flow control
000029034 269__ $$c2000
000029034 520__ $$9eng$$aThis paper proposes and tests an approximation of the solution of a class of piecewise deterministic control problems, typically used in the modeling of manufacturing flow processes. This approximation uses a stochastic programming approach on a suitably discretized and sampled system. The method proceeds through two stages: (i) the Hamilton-Jacobi-Bellman (HJB) dynamic programming equations for the finite horizon continuous time stochastic control problem are discretized over a set of sampled times; this defines an associated discrete time stochastic control problem which, due to the finiteness of the sample path set for the Markov disturbance process, can be written as a stochastic programming problem; and (ii) the very large event tree representing the sample path set is replaced with a reduced tree obtained by randomly sampling over the set of all possible paths. It is shown that the solution of the stochastic program defined on the randomly sampled tree converges toward the solution of the discrete time control problem when the sample size increases to infinity. The discrete time control problem solution converges to the solution of the flow control problem when the discretization mesh tends to zero. A comparison with a direct numerical solution of the dynamic programming equations is made for a single part manufacturing flow control model in order to illustrate the convergence properties. Applications to larger models affected by the curse of dimensionality in a standard dynamic programming techniques show the possible advantages of the method.
000029034 695__ $$9eng$$amanufacturing processes ; approximation theory ; piecework ; production control ; Markov processes ; stochastic programming
000029034 700__ $$aMoresino, Francesco$$uHaute école de gestion de Genève
000029034 773__ $$g2000/32/10/907-919$$tIIE Transactions
000029034 8564_ $$fMoresino_2000_stochastic_programming.pdf$$qapplication/pdf$$s312063$$uhttp://doc.rero.ch/record/29034/files/Moresino_2000_stochastic_programming.pdf$$yorder:1$$zTexte intégral
000029034 918__ $$aHaute école de gestion de Genève$$bCampus de Battelle, Bâtiment F, 7 route de Drize, 1227 Carouge$$cCentre de recherche appliqué en gestion (CRAG)
000029034 919__ $$aHaute école de gestion de Genève$$bGenève$$ddoc.support@rero.ch
000029034 980__ $$aPOSTPRINT$$bHEGGE$$fART_JOURNAL
000029034 990__ $$a20120427142657-TK