Real time scheduling system (RTSS)

Traditional research in Job Shop Scheduling (JSS) is largely based on combinatorial analysis. Unfortunately, the NP-complete nature of the problem forces many assumptions into existing models that result in wide discrepancies between the real nature of the problem and the specifically limiting solutions that are obtainable through mathematical analysis. In recent years, several researchers have called for research into the Dynamic Job Shop Scheduling (DJSS) problem using a new approach that combines traditional techniques with other methodologies. The work described in this dissertation presents a framework that blends a classical decision rule, with other relevant characteristics pertaining to customers, jobs and their potential future values for decision making in a real time scheduling environment, while accounting for various contingencies involving planned and unplanned downtime, customer order changes, etc. The implementation of structured modeling permits the interchange of components, thus permitting configuration of the system by the decision maker (DM) to satisfy his particular requirements. Imbedded expert systems (ES) control constraint violations. For example, if a job is in danger of being late, an ES can make changes automatically, or suggest alternatives to an interactive DM. Also, an ES can handle a machine that has more scheduled work than time available (bottleneck condition) and take corrective action, or suggest alternatives to the DM. For illustrative purposes, a Real Time Dynamic Job Shop Scheduling System (RTSS) is developed on the basis of such a framework. The impact on the set of classical job shop scheduling assumptions is reviewed and most can be relaxed, indicating flexibility in the system. This research represents a significant contribution to reducing the difference between traditional job shop scheduling research and the real problems of dynamic job shop scheduling.