Book Description

Excerpt from Real-Time Simulation and Production Scheduling Systems Flexible Manufacturing Systems (fms) have been installed in numerous factories around the world. Production scheduling is the function responsible for assigning fms resources to various manufacturing tasks. Efficient use of these resources is critical to a company's goal of increased profits. In fact, poor scheduling decisions tend to reduce profits because they increase idle time on machines, cause bottlenecks on the shop floor, and push customer orders past their due date. Mathematical programming approaches to solving the scheduling problem have received considerable attention in the literature. Graves [gra81] and Raman [ram85] have provided excellent surveys on these techniques. However, these techniques tend to have prohibitive computational requirements and restrictive assumptions. Another major drawback to these approaches is that they typically do not include material handling constraints. Discrete event simulation, expert systems, and other heuristics [mil86, nor86, jac86] are other methods used to generate schedules. While simulation and expert systems packages allow the manufacturing system to be modelled to any level of detail, they still have unacceptable computational requirements. In addition, all of these methods generate feasible solutions with no measure of optimality. These undesirable properties tend to limit the applicability of all these approaches in a real fms environment. There are two other major drawbacks to all of the aforementioned approaches. First, they are all run off-line usually once or twice a day. Consequently, they are not able to respond quickly to unexpected events in the fms. These events are usually handled on an ad hoc basis by the fms supervisor with little understanding of the impact of his decisions on the overall schedule. Second, they do not take advantage of the vast amount of real-time shop floor data provided by fms computer systems. Davis and Jones [dav88] have proposed an algorithm for real time production scheduling (see Figure l). The algorithm first selects R candidate scheduling alternatives and L performance indices to be used in evaluating those alternatives. Both selections based on actual shop floor data and are subject to continuous modification as the system evolves over time. Oh-line, concurrent, Monte Carlo simulations are run, in real - time, to evaluate the performance of these rules. This type of simulation analysis introduces four problems which typically are not addressed in the simulation literature. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.