Using prototype expert systems for AGV selection. (automated guided vehicles)(includes related articles on sample rules, parameter travel)

As research fields in artificial intelligence accelerate and a high number of experts are demanded by industry, expert systems play an important role in meeting the technological sophistication required in today's competitive world. Industries are demanding the assistance of human experts to solve their complicated problems. However, there is a shortage of experts due to this demand. Currently, many top universities and colleges are racing to develop the most sophisticated artificial intelligence (AI) that can simulate a human expert's thought processes and knowledge. As research is being done, industries are pulling together to find ways to use AI to solve their complex problems. Presently, there are many companies using expert systems (one of the fields of AI) to improve their operations and help solve their problems.

Automated Guided Vehicles (AGVs) have become an integral part of today's manufacturing and warehousing environments. They are instrumental in transporting material efficiently and cost-effectively. In production facilities, AGVs are often found transporting material between departments, either exclusively between two departments or throughout the manufacturing process. They are also located in warehousing facilities where their uses include transporting material, and storing and retrieving pallets and unit loads. As a result of their wide use, engineers frequently face the problem of choosing an appropriate AGV to fit a particular material handling system. The decision making process of selecting an AGV involves prioritizing important criteria and then choosing alternatives based on the resulting order. This process parallels that of developing an expert system.

In 1987, Malmborg found a match between the problem of selecting an industrial truck type and the use of an expert system. Presented here is a method to use expert systems to assist in the procedure for selecting AGVs.

There is an increasing number of AGV manufacturers producing various models to meet the demands of industry. As the number of models increases, engineers are having difficulty selecting the best one for their company. To overcome this difficulty, engineers must develop extensive knowledge of facts, rules, and relationships on material handling and the available AGVs.

The problem of selecting AGVs can be solved by using an expert system and its shell. A solution can be found in the development of the expert system prototype for AGV selection using Personal …