This study is concerned with the applications of linear goal programming techniques and artificial intelligence algorithm (fuzzy theory and genetic algorithm) to the analysis of management and operational problems in the radioactive processing system (RWPS). A typical RWPS is modeled as a linear functions to study and resolve the effects of conflicting objectives such as cost, limitation of released radioactivity to the environment, equipment utilization and total treatable radioactive waste volume before discharge and disposal. The developed model is validated and verified using actual data obtained from the RWPS at Kyoto University in Japan. The solution by goal programming would show the optimal operation point which is to maximize the total treatable radioactive waste volume and minimize the released radioactivity of liquid waste even under the restricted resources. But goal programming has a demerit that the target values are decided by decision maker arbitrarily. To complement the goal programming``s demerit, the fuzzy set theory is introduced and the target values are analyzed by it.
Genetic algorithm is combined with goal programming and the results by it is compared with that of goal programming only.