We propose a general hierarchical procedure to address real-life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its own route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time, and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on an Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four-phase, hierarchical, operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real-life shops. We therefore report on a real-life application.Queueing Networks, Leadtime Estimation, Lot Size, Multimachine Scheduling

Much research has been devoted to the job shop scheduling problem since its introduction in the late 50's. Despite these efforts, even moderate sized benchmarking problems remained unsolved for many years. Given the complexity of the job shop scheduling problem, there is little hope for solving large real-life problems optimally within reasonable time. We therefore rely on heuristics, of which the Shifting Bottleneck Procedure, developed by Adams et al. (1988), is performing excellently. We examine several extensions of the Shifting Bottleneck Procedure towards real-life applications. We introduce due dates, release dates, assembly structures, split structures, overlapping operations, setup times, transportation times, parallel machines and beginning inventory. This generalized shifting bottleneck procedure is compared with priority dispatching rules on a set of large test problems.