A decision aiding methodology was required by a company of the aeronautical sector to support their engineering design processes. An integrated use of mathematical programming and multiple criteria methods was proposed to orient the conceptual design of functional and physical solutions. As a first step, linear programming was used in relation to some case studies to generate design alternatives that satisfy the initial requirements. Subsequently multiple criteria methods were proposed to interact transparently with the client in relation to some aspects that were not included in the linear programming model. Design alternatives were evaluated and compared, in order to identify and formalize expectations that the first design solutions were not able to meet. An iterative use of the two approaches, in a cyclic procedure of mutual learning, can allow the requirements to be defined more clearly and a final satisfying solution to be reached.

Integrated use of mathematical programming and multiple criteria methods in engineering design processes / Norese, Maria Franca; Liguigli, Ersilia; Novello, Chiara. - In: INTERNATIONAL JOURNAL OF MULTICRITERIA DECISION MAKING. - ISSN 2040-1078. - 3:4(2013), pp. 346-361.

Integrated use of mathematical programming and multiple criteria methods in engineering design processes

NORESE, Maria Franca;LIGUIGLI, ERSILIA;NOVELLO, CHIARA
2013

Abstract

A decision aiding methodology was required by a company of the aeronautical sector to support their engineering design processes. An integrated use of mathematical programming and multiple criteria methods was proposed to orient the conceptual design of functional and physical solutions. As a first step, linear programming was used in relation to some case studies to generate design alternatives that satisfy the initial requirements. Subsequently multiple criteria methods were proposed to interact transparently with the client in relation to some aspects that were not included in the linear programming model. Design alternatives were evaluated and compared, in order to identify and formalize expectations that the first design solutions were not able to meet. An iterative use of the two approaches, in a cyclic procedure of mutual learning, can allow the requirements to be defined more clearly and a final satisfying solution to be reached.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2503028
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