Research Of The Interactive Multi-objective Genetic Algorithm In Shop Scheduling Knowledge Base System

At present, the manufacturing sector, increasing competition, in the ordinary course of business, would often encounter a wide variety of complex scheduling problems, shop scheduling problem-solving will have a direct impact on the operational efficiency of enterprises and end-customers satisfaction and ultimately affect the business-to-market response ability and competitiveness. Therefore, the scheduling problem has become a major field of operations management research focus.The policy-maker information which appears in view of the interactive multi-objective genetic algorithm application in determines and the expression as well as the user easy weary question in advance with difficulty by chance, proposes one kind of improvement interactive multi-objective genetic algorithm. Through multi-objective trade-off analysis and decision-makers from the elite to gradually induce goal weight preferences, the Pareto rank of the weight combination of preferences and to guide the genetic search, in order to address human-computer interaction difficult problem. Design an interactive way of decreasing interval algebra, namely: computing the initial interval algebra are more interactive, more to the post-interval the smaller the more intensive interaction, reducing user fatigue and increase speed of operation. Will improve the interactive multi-objective genetic algorithm for job shop scheduling problem can be applied to the real proof of its effectivenessAt the same time for the past, advanced shop floor scheduling system can only adapt to a specific shop environment, reconfigurable and general shortcomings of poor design and realization of universal intelligent optimization algorithms shop scheduling knowledge base system. Through knowledge acquisition, knowledge discovery, statistics and cluster analysis methods, extraction scheduling rules, and as knowledge to be stored, so that in future production, can take direct rule similar to the task of looking for ways to increase operational efficiency. Categories according to the production, workshop and scheduling objective and the scale of the problem of the nature of the use of knowledge base system selects an appropriate algorithm to calculate the optimal scheduling solution.Will improve the interactive multi-objective genetic algorithm is applied to job shop scheduling knowledge base system can be more efficient for a variety of different manufacturers, different scale of the problem solving to provide a unified program. Using the system, can reduce the original plan preparers, but also planning a more rational, scientific, meticulous, you can improve equipment utilization. To avoid equipment idle time and shorten the production cycle as far as possible under the principle of the load of each device according to task allocation, and to consider the insertion of non-urgent tasks to improve economic efficiency of enterprises. This job-shop scheduling problem in research and theoretical research results will be extended to the practical engineering field of certain significance.