Descending Deviation Optimization techniques for scheduling problems

In factory automation, production line scheduling entails a number of competing issues. Finding optimal configurations often requires use of local search techniques. Local search looks for a goal state employing heuristics and random local ldquoprobesrdquo in order to move from state to state. All local search techniques, however, suffer from problems with local maxima, i.e. have the potential of getting ldquostuckrdquo in a suboptimal state. While careful introduction of randomizations is certainly a recognized technique, it can also lead the algorithm even more astray. This paper describes a heuristic technique called descending deviation optimizations (DDO) in which a gradually lowering-randomization ceiling allows a local search technique to ldquobouncerdquo randomly without going too far astray. An example applying the DDO to a local search technique and achieving significant improvement is shown.