Design of the Optimal Feeding Policy in an Assembly System.

This paper describes an innovative and integrated approach to component management optimization within a production/assembly system. In a mixed-models assembly process the handling of parts and components for each work station represents a substantial variable that can greatly affect job duration and efficiency. This paper is strictly related to Assembly to Order/Manufacturing to Order (ATO and MTO) systems, where lead time has to be very short and flexibility is at its maximum level. In Assembly to Order (ATO) or Make to Order (MTO) systems, the production is increasingly getting more customized in response to the demand, thanks to the progresses reached in both manufacturing and information technologies. It is becoming increasingly possible to assemble or make products specifically in response to the requests of either end customers or retailers. As a consequence of such customization, the design of the whole system must take into direct account several elements: parts warehouses location, feeding policies and feeding systems. In some cases the collection of parts and components required picking activities, in other the movement of entire units load. In several instances experts have analyzed the problems about material centralization/decentralization, storage policies and assembly feeding problem in different and independent ways, while the problem needs an integrated approach. While many researches regarding components allocation problems in ATO and MTO systems, did not consider feeding policies, material picking, packing activities and vehicles optimization, this paper cover focuses on filling such gap using an integrated framework that considers both aspects of the problem: the centralization/decentralization of components in order to minimize the total storage costs and the right feeding policies. Feeding problems in assembly lines are some of the most important aspects to consider during the analysis and design of an assembly system, to allow the maximization of efficiency and flexibility. To reach such goals, a multi-factorial analysis has been carried out during this experiment and will validate the introduced framework. An industrial application of the introduced framework is illustrated to explain its real significant production implication.