A hybrid approach for real-time sequencing and scheduling

Abstract

Abstract An approach for real-time sequencing and scheduling is described which integrates artificial neural networks, real-time simulation, genetic algorithms, and a trace-driven knowledge acquisition technique. This approach will be used to solve both single machine sequencing and multi-machine scheduling problems. Single-performance, artificial neural nets are used to quickly generate a small set of candidate sequencing or scheduling rules from some larger set of heuristics. A more detailed evaluation of these candidates is carried out using realtime simulation. This evaluation is necessary to generate a ranking that specifics how each rule performs against all of the performance measures. A further reduction of these candidates can be achieved from this ranking. Genetic algorithms are applied to this remaining set of rules to generate a single 'best' schedule. To capture the knowledge contained in that schedule, a trace-driven knowledge acquisition technique is used. Then, the derived rule is added to...