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In order to improve the efficiency of automated storage and retrieval system, expert systems were applied in the optimization of storage management and job scheduling of automated storage and retrieval system. By analyzing the experience of experts in the field, a knowledge base based on production rules was established. The expressions of knowledge are divided into three categories: storage allocation rules, tasks to sort rules and other rules. The application of forward inference engine designed the flow of reasoning, by which a more rational allocation of storage and quicker job scheduling were realized. Experimental results show that in the automated storage and retrieval system, applying expert system in the cargo space management and job scheduling optimization can effectively improve the operation efficiency of automated storage and retrieval system.

Optimization for the Storage Management and Job Scheduling Based on Expert System