Multivariate regression metamodel: A DSS application in industry

Abstract A materials handling system simulation (written using GPSS/H) was developed to predict the Automated Guided Vehicle requirements necessary for a major manufacturer to maintain desired levels of production in one of its automobile assembly plants. Rather than use the simulation as a representational DSS and risk complicating the user interface, validated simulation outputs were collected and used to produce a multivariate regression metamodel. This metamodel formed the centerpiece of a narrow-scope suggestion model DSS used on the factory floor to aid in day to day allocations of resources. This article looks at the metamodel development methodology and offers this technique as an effective means of producing a suggestion model DSS from a more complex representational DSS.

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