In modern manufacturing environments, production system analysis is becoming more and more complex in consequence of an increasing use of integration and automation processes which call for far-reaching adjustments in the cost structures of firms: cuts on variable costs and corresponding increases in fixed costs with concomitant greater investment risks. Due to the sheer complexity of both their production systems and associated investment risks, firms tend to earmark sizable proportions of their investment resources for the system design and management phases. The most widely used analysis techniques are based on computer-aided simulation models, .i.e. tools which are able to simulate any, even the most complex, aspects of a production system and which guarantee highly accurate results close to real-case scenarios. The use of fuzzy numbers to estimate the performance characteristics of production systems is associated with a number of difficulties which stem in part from the fact that the methods used to execute mathematical operations with fuzzy numbers are as yet not generally accepted and in part from the explosion of the support of the fuzzy sets fired by the considerable number of computations to be performed. In this chapter we will analyse the problems associated with the description of vaguely known characteristic variables in a production system and the procedures to evaluate performance indices using queueing network models with fuzzy parameters.
Design of Advanced Manufacturing Systems:A fuzzy performance evaluator of AMS’s
CAIAZZO, Fabrizia;PASQUINO, Raimondo;SERGI, Vincenzo;
2005-01-01
Abstract
In modern manufacturing environments, production system analysis is becoming more and more complex in consequence of an increasing use of integration and automation processes which call for far-reaching adjustments in the cost structures of firms: cuts on variable costs and corresponding increases in fixed costs with concomitant greater investment risks. Due to the sheer complexity of both their production systems and associated investment risks, firms tend to earmark sizable proportions of their investment resources for the system design and management phases. The most widely used analysis techniques are based on computer-aided simulation models, .i.e. tools which are able to simulate any, even the most complex, aspects of a production system and which guarantee highly accurate results close to real-case scenarios. The use of fuzzy numbers to estimate the performance characteristics of production systems is associated with a number of difficulties which stem in part from the fact that the methods used to execute mathematical operations with fuzzy numbers are as yet not generally accepted and in part from the explosion of the support of the fuzzy sets fired by the considerable number of computations to be performed. In this chapter we will analyse the problems associated with the description of vaguely known characteristic variables in a production system and the procedures to evaluate performance indices using queueing network models with fuzzy parameters.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
