Long-term planning in manufacturing production systems under uncertain conditions

P. Caricato, A. Grieco, F. Nucci and A. Anglani

International Journal of Automotive Technology and Management, 2003, vol. 3, issue 3/4, 293-314

Abstract: Nowadays, the frequency of decisions related to the configuration and capacity evaluation of manufacturing production systems is increasing in more and more industrial sectors, especially in the automotive field. This is due to a variety of factors, such as the reduction of the life cycle of the product, increasing competition, etc. In such a context, decision makers have to take their actions in shorter times than they ever did in the past: as an example, they typically need to take quick decisions about different production system alternatives. This specific problem has increased in complexity because of the necessity to take into account all the sources of variability and each related level of uncertainty in the available data definition. Two main aspects lead to such difficulties: the lack of a proper decision support system and the need to contextually model the uncertain data. This paper presents the first step in this direction. In particular, a decision support system (DSS) has been developed to help decision makers take productive capacity planning decisions according to the uncertain characterisation of the market evolution. First, a strategy evaluation tool allows the decision maker to specify several productive capacity expansion policies and, then, uses a fuzzy discrete event simulation paradigm (Fuzzy-DEVS) to compare them, providing the possibility of choosing between the different alternatives according to performance indicators. A strategy design tool helps the decision maker by inferring the best expansion policy on the basis of the system analysis conducted in the first step. Finally, our approach has been validated by means of an industrial test case in the automotive sector.

Keywords: long–term capacity planning; Fuzzy-DEVS; discrete event simulation; automobile industry; decision support systems; DSS. (search for similar items in EconPapers)
Date: 2003
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