ISIS—a knowledge‐based system for factory scheduling

Abstract

Analysis of the job shop scheduling domain has indicated that the crux of the scheduling problem is the determination and satisfaction of a large variety of constraints. Schedules are influenced by such diverse and conflicting factors as due date requirements, cost restrictions, produdion levels. machine capab&bes and substitutability, alternative production processes, order characteristics, resource requirements, and resource availability. This paper describes ISIS. a scheduling system capable of incorporating all relevant constraints in the cowtrwbon ' ofjob shop schedules. We examine both the representation of constraints within ISIS, and the manner in which these constraints are used in conducting a constraintdirected search for an acceptable schedule. The important issues relating to the relaxation of constraints are addressed. Finally, the interawe scheduling faalities provided by lSlS are considered.

Achieving Flexible Autonomy in Multiagent Systems Using Constraints

“…Fox and Smith (1984) describe a similar categorization for constraintbased scheduling. However, while these types of constraints are also required in multiagent planning, the differences in problem structure introduce a number of additional types of information that are ideally represented as constraints (Evans and Anderson, 1990b).…”

“…However, while these types of constraints are also required in multiagent planning, the differences in problem structure introduce a number of additional types of information that are ideally represented as constraints (Evans and Anderson, 1990b). Thus, while some entirely new types of constraints useful in planning and coordinating cooperative problem-solvin g activities have emerged, others in the list have been adapted from Fox's work (Fox and Smith, 1984).…”

Intensity Function in Evaluation of Production Process Stability

“…Researchers apply the existing knowledge predominantly in predictive maintenance, however, there are more and more frequent applications of the knowledge in production scheduling. The approach which aims at elimination of potential disruptions in manufacturing is known as robust scheduling [7,8].…”

Extending the RCPSP for modeling and solving disruption management problems

“…Particularly the development of the popular factory scheduling systems ISIS [6] and its successor OPIS (Opportunistic Intelligent Scheduler, see [7]) helped to define and separate the concepts of scheduling and rescheduling. While the former scheduler focused generating plans using predictive scheduling (or baseline scheduling), the latter one was intended to be able to react to disruptions by implementing reactive scheduling.…”