DocumentCode
2167084
Title
Hierarchical production planning using a hybrid system dynamic-discrete event simulation architecture
Author
Venkateswaran, Jayendran ; Son, Young-Jun ; Jones, Albert
Author_Institution
Dept. of Syst. & Ind. Eng., Arizona Univ., Tucson, AZ, USA
Volume
2
fYear
2004
fDate
5-8 Dec. 2004
Firstpage
1094
Abstract
Hierarchical production planning provides a formal bridge between long-term plans and short-term schedules. A hybrid simulation-based production planning architecture consisting of system dynamics (SD) components at the higher decision level and discrete event simulation (DES) components at the lower decision level is presented. The need for the two types of simulation has been justified. The architecture consists of four modules: enterprise-level decision maker, SD model of enterprise, shop-level decision maker and DES model of shop. The decision makers select the optimal set of control parameters based on the estimated behavior of the system. These control parameters are used by the SD and DES models to determine the best plan based on the actual behavior of the system. High level architecture has been employed to interface SD and DES simulation models. Experimental results from a single-product manufacturing enterprise demonstrate the validity and scope of the proposed approach.
Keywords
corporate modelling; decision making; discrete event simulation; enterprise resource planning; job shop scheduling; manufacturing systems; optimal control; production planning; DES shop model; SD enterprise model; control parameters; dynamic-discrete event simulation architecture; enterprise-level decision maker; hierarchical production planning; high level architecture; hybrid simulation; hybrid system; long-term planning; production planning architecture; shop-level decision maker; short-term schedule; single-product manufacturing enterprise; system dynamics components; Aggregates; Discrete event simulation; Enterprise resource planning; Job shop scheduling; Large-scale systems; Linear programming; Manufacturing; Materials requirements planning; Production planning; Stochastic systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Simulation Conference, 2004. Proceedings of the 2004 Winter
Print_ISBN
0-7803-8786-4
Type
conf
DOI
10.1109/WSC.2004.1371434
Filename
1371434
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