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Mixed model assembly lines are becoming increasingly used in manufacturing facilities to meet the diversified demands of customers. In this paper we deal with syncronous flow lines and wish to determine the job input sequence minimizing total tardiness. A ILP formulation of the problem is given. We prove the strong NP-hardness of the problem, we try to solve the linear relaxation of the problem and then introduce a number of cutting planes to solve the separation problem. Computational results confirm the effectiveness of the procedure.

In a plant for automobile assembly, sub-assemblies are produced in shops consistingof sequential groups of parallel workstations. The problem of optimally sequencing n jobs in such an environmentis a generalization of the flow shop problem. In this paper a new dispatching rule is proposed that also considersjobs that have not yet completed the previous operation. The next job to be machined in the successive stage is selected by solving an instance of a bottleneck assignment problem based on the actual state of the shop. The results are verified via simulation of a real automobile assembly line

The problem of task assignment for a class of flexible manufacturing cells is considered. The cell is designed for cyclic production of one product. The product is characterized by a sequence of operations of given length and each requiring a robot: the problem is therefore to allocate the operations to the robots in order to maximize the throughput. The problem of finding a task assignment such that the parts are never in a waiting state is discussed. Notice that, if such assignment exists, the maximum theoretical throughput is achieved. The complexity of the problem for some structures of the system by means of graph-theoretical models.

In this paper a set of new algorithms for the No-Wait Flow Shop problem is proposed for processing a mix of s lots, different in dimensinn and part-type, on a Pipeline Manufacturing System: in the two-machines case, an O(s log s) algorithm is given, yielding a very small bound on the relative error. The larger the lots, the better the approximation and the greater the time saving with respect to the exact algorithm due to Gilnlore and Gomory. Notice that lot sizes need not be identical and the exact duration of the operations is not required (only the knowledge of the relative ranking of the operation times is needed). In the m-machines case, with the additional constraint of no machine idle times (i.e. neither the parts nor the machines have to wait), an O(ms) algorithm is given; if such solution exists, it corresponds to both throughput maximization and inventory cost mininlization.