Controlling flexible manufacturing systems based on a dynamic selection of the appropriate operational criteria and scheduling policy

Abstract

This study presents the development of a multi-criteria control methodology for flexible manufacturing systems (FMSs). The control methodology is based on a two-tier decision making mechanism. The first tier is designed to select a dominant decision criterion and a relevant scheduling rule set using a rule-based algorithm. In the second tier, using a look-ahead multi-pass simulation, a scheduling rule that best advances the selected criterion is determined. The decision making mechanism was integrated with the shop floor control module that comprises a real-time simulation model at the top control level and RapidCIM methodology at the low equipment control level.A factorial experiment was designed to analyze and evaluate the two-tier decision making mechanism and the effects that the main design parameters have on the system's performance. Next, the proposed control methodology was compared to a selected group of scheduling rules/policies using DEA. The results demonstrated the superiority of the suggested control methodology as well as its capacity to cope with a fast changing environment.

A parametric fuzzy logic approach to dynamic part routing under full routing flexibility

“…those reported in Chen, Yang, Abraham, & Peng, 2007) to enhance systematic design of fuzzy logic systems, the survey presented above reveals that the majority of implementations in FMS context adopt an ad-hoc behaviour in constructing the fuzzy model, while a group of researchers rely on expert knowledge and another group employs simulation for this purpose. Getting the weights of different criteria from human experts is not likely to produce satisfactory results in the area of FMS operational control, since information on relationships between decision variables and system performance is seldom explicit (Naso & Turchiano, 1998;Shnits & Sinreich, 2006). Extracting the parameters of the fuzzy model by means of simulation, on the other hand, although a valid approach, tunes the model with respect to a given FMS instance; and, requires a substantial effort of model building and experimentation that needs to be replicated each time shop conditions or product mix change.…”