This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assembly plants). The intrinsic characteristics of the problem, such as stochasticity, the high number of products and components, and exogenous factors, make it complex to formulate and solve the mathematical models. Thus, new decision support systems integrating human choices and fast solution algorithms are needed. In this paper, we present an innovative and successful use case of such an approach, encompassing the decision-maker as an integral part of the optimization process. Moreover, the proposed approach allows the managers to conduct what-if analyses in real-time, taking robust decisions with respect to future scenarios, while shortening the time needed. As a byproduct, the proposed methodology requires neither the definition of a probability distribution nor the investigation of the user’s risk aversion.

A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry / Fadda, Edoardo; Perboli, Guido; Rosano, Mariangela; Etienne Mascolo, Julien; Masera, Davide. - In: SUSTAINABILITY. - ISSN 2071-1050. - 14:4(2022), pp. 1-22. [10.3390/su14042408]

A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry

Edoardo Fadda;Guido Perboli;Mariangela Rosano;
2022

Abstract

This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assembly plants). The intrinsic characteristics of the problem, such as stochasticity, the high number of products and components, and exogenous factors, make it complex to formulate and solve the mathematical models. Thus, new decision support systems integrating human choices and fast solution algorithms are needed. In this paper, we present an innovative and successful use case of such an approach, encompassing the decision-maker as an integral part of the optimization process. Moreover, the proposed approach allows the managers to conduct what-if analyses in real-time, taking robust decisions with respect to future scenarios, while shortening the time needed. As a byproduct, the proposed methodology requires neither the definition of a probability distribution nor the investigation of the user’s risk aversion.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2956439