Knowledge-based predictive and reactive scheduling in industrial environments

Abstract

Real-world scheduling problems are intrinsically complex because of the dynamic nature of industrial environments, conflicting organizational goals, the existence of operational constraints and preferences that are difficult to represent in a computational model. In addition, plant capacity and bottleneck stages are generally not known ahead of time since they depend upon the product mixture. Therefore, rigid scheduling procedures, designed to provide optimal or near-optimal solutions under particular circumstances, will not always be satisfactory. Moreover, purely automatic scheduling is not realistic because it neglects the important role of the human expert, who has the ultimate responsibility for all decisions and wants to be engaged in the solution process. To overcome these difficulties, many authors have adopted knowledge-based approaches. This contribution presents a knowledge-based framework, based on the object oriented technology, for building scheduling systems aimed at solving real-world problems. The paper points out the most relevant aspects of the proposed framework architecture that supports both predictive and reactive scheduling. It has been designed to enhance the problem solving capabilities of human schedulers and has been abstracted after the successful implementation of three different scheduling systems, two of which have entered into everyday industrial use. The most important lessons that were learned during the design of these systems are outlined in the paper.