Lot streaming in flow shops

Lot streaming is permitting partial transfer of processed portions of a job to downstream machines, thus allowing ovelapping operations. The primary motivation is to improve the measures of performance by the quick movement of work in the shop. In this thesis, we study various forms of the lot streaming problem in flow shops to derive the characteristics of optimal solutions. We first analyze single job lot streaming problems, then extend the results to multi-job problems. When there is a single job, the lot streaming problem is to find the best transfer batch sizes that optimizes the given criterion. We consider three different measures of performance, job, sublot and item completion time criteria. We derive a closed form solution for a special case of job completion time criterion. Under sublot completion time criterion, when the first machine has the largest processing time, we show that partial transfers of equal size are optimal. We propose two polynomial time algorithms for the problem in which only two transfer batches are permitted between each consecutive machines for sublot and item completion time criteria. In multi-job lot streaming problems, the sequencing and lot streaming decisions must be considered simultaneously. For multi-job problems we investigate the hierarchical application of lot streaming and sequencing decisions.