an mcdm-dea approach for technology selection

technology selection is an important part of management of technology. recently karsak and ahiska (2005) proposed a novel common weight multiple criteria decision making (mcdm) methodology for selection of the best advanced manufacturing technology (amt) candidates based on a number of attributes. however, amin et al. (2006), by means of a numerical example demonstrated the convergence difficulty of the karsak and ahiska algorithms, and then introduced an improvement model to rectify that running problem. this paper presents an mcdm-dea methodology in order to evaluate the relative efficiency of amts with respect to multiple outputs and a single exact input. using displaced ideal methodology, a practical common weight is developed and its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking obtained by the proposed mcdm framework with that obtained by a data envelopment analysis (dea) classic model.