Abstract
This paper presents an attempt to automatically and flexibly solve the sequencing problem of machining operations that enable to produce a part under machining constraints. To realize an flexible planning mechanism, two approaches are performed. In these approaches, operation planning problems are modeled as combinatorial optimization problems. First, genetic algorithms (GAs) are applied directly. To implement the GAs, a string is encoded as an order of removing operations. Thus, a process that produces a finished part from a raw material by removing the machinable volumes is represented as a sequence of the numbers of machinable volumes. As the second approach, the classifier system (CS) is applied to this problem to obtain the further advantages. In this approach, a classifier rule corresponds to a production rule that instructs the next removal volume for a current machining state. As a result of the learning, a near optimal process plan is represented as a chain of classifiers with higher strength values. Moreover, a construction of synthesized planning systems based on GA-approach and CS-approach is also discussed. On the basis of the proposed approaches, a genetic based operation planning simulator is constructed and some numerical experiments are carried out.
