On-line scheduling using a distributed simulation technique for intelligent manufacturing systems

Abstract

On-line scheduling has traditionally been restricted to a single machine or workcell. In this paper, a distributed simulation technique for on-line scheduling is expanded to encompass several machines .or workcells. Additional machines introduce several difficulties, however, the simulation better reflects scheduling demands in manufacturing environments. IntroductionIncreasing economic pressure has steadily driven manufactures toward partial or complete automation of their production facilities. While the costs associated with automation have decreased, the demand for low cost, high quality goods have increased enormously. Besides the efficient use of raw materials by producing less scrape and waste, automation also reduccs Work In Progress (WIP) by introducing less variability and thus allowing for more efficient scheduling. Although numerical methods have traditionally been used for analysis and evaluation, the simplifying assumptions made by numerical methods and their complexity when applied to real world problems restrict the use of such methods in the manufacturing domain. Simulation is thus very useful as an analysis and evaluation tool to study the effects of scheduling in manufacturing applications. A maximally constrained schedule contains all the necessary details to instruct every machine so that the schedule can be executed. Many scheduling techniques will not hold at this level of detail as a result of their simplifying initial assumptions. For instance, relaxation techniques, as in [ChLi94], have limited practical application because they assume that processing times, down times, success rates, etc., are deterministically known. Queuing theory methods can model the steady-state operation, but cannot model transients due to' the assumptions made to improve computational efficiency. Evaluation of a maximally constrained schedule demands that the true dynamic nature of the system be modeled. Simulation methods to support the scheduling activity can be used to model both steady-state and transient system behavior.The input to a simulator is a plan and by definition, a plan contains all possible methods for producing a part regardless of the production capabilities of the factory. Given such a plan, the scheduler enforces further ordering constraints on the operator application, thereby, creating a schedule. 0-7803-2559-1/95 $4.00 0 1995 IEEE 21 59 at Austin, Austin, TX 78712-1084Tailoring the plan to the current state and capabilities of the factory creates a schedule. If the state changes, the schedule needs to be modified, but the plan does not change. Since planning is often done off-line, the planner does not have access to run-time information such as the current availability of resources (machines, operators), failure information, supplyhnventory related information, etc. This lack of information forces a plan to be conservative or requires the generation of multiple plans so that at least one will meet the run-time requirements. Such a plan is called a minimally constrained plan specification.S...