Reconfiguration Methodology to improve the agility and sustainability of Plug and Produce Systems

Abstract: The emergence of globalisation, market turbulence and sustainability requirements is challenging production companies to devise new strategies to offer large product diversity, keep low inventories, and timely produce small batches of customised and personalised products. Agile shop-floors that can be promptly deployed and re-configured with minimum integration and programming efforts are perceived as a promising strategy to tackle this problem.This has led to the advent of the Plug and Produce (P&P) concept, where different production modules can be plugged in the system and start working autonomously without ceasing production. P&P systems support structure and functionality transformations through plug/unplug of modules, and dynamic production and fault-tolerance through self-organization. This will naturally increase its complexity in design, operation and exact performance predictability, and therefore it sets the need for the definition of methodologies and decision supporting tools that can help system designers and production managers deciding which layouts and configurations could accommodate constantly changing production requirements (i.e. different product plans and volumes). This thesis focuses exactly on those points, aiming at providing a reconfiguration methodology that can contribute to the increase of agility and sustainability of P&P systems.This methodology enables the systematic generation and assessment of reconfiguration alternatives for P&P systems. For this purpose, it uses graph theory and a set of metrics to assess the potential performance of different reconfiguration alternatives. The experimental tests provided present evidence that the use of the proposed methodology can help designers selecting a suitable reconfiguration alternative whenever new product requirements are posed. The use of this methodology can therefore increase the agility and sustainability of P&P systems and potentially contribute to their industrial deployment.