Local scheduling problems submitted to global FIFO processing constraints

This paper deals with local scheduling problems as part of a global process management policy. The industrial context is an automotive assembly plant. An automobile manufacturing process creates a list that schedules orders so as to optimize production costs. Such a list must respect the vast majority of shop-related constraints, notably those of the assembly shop, which are the hardest to schedule. This list allows one to provide information to suppliers regarding the parts they are required to deliver to the assembly plant. PSA Peugeot-Citroën has set the ambitious objective of ensuring compliance with all list entries upon arrival at the assembly plant. This objective is equivalent to instituting a global first-in, first-out (FIFO) management policy for all operations upstream of the assembly line (i.e. body shop, paint shop) and in particular it enables one to make significant reductions in component inventories. However, FIFO-based flows are difficult to achieve. They first require setting up local production scheduling in the plant in order to satisfy constraints specific to each shop (body shop, paint shop, assembly line); this step will be referred to as ‘local scheduling’. Moreover, a number of disturbances intervene to change the initial order. The aim of our work is to lay out a new policy for managing flows (called ‘reorderable scheduling’). The premise is to define the maximum scheduling level allowed upon the input into each shop so as to guarantee restoration of the initial list order (i.e. reordering) at the assembly entrance. We will start by studying, from a theoretical perspective, a very general base case and then we will characterize the maximum authorized local scheduling disturbance that enables us to fulfil the global management objective (i.e. FIFO). We will also show the implementation of this method in our industrial case within the context of an automotive assembly plant and finally perform the validation step by means of simulation.