Enterprise process model multi-target parameter optimizing method based on genetic algorithm

Abstract

The invention relates to the enterprise engineering and the information technology field, and discloses an optimization method for the multi-objective enterprise process model parameters, which is based on the genetic algorithm. In the optimization field of the enterprise process model parameters, most optimization methods use the countermeasure weighing principle to combine each sub-goal into a single objective, so as to process an optimization objective, thus, some defects exist in the comprehensive evaluation process. Aiming mainly at various optimal parameters, such as the production rate of the product, the quantities of various resources, the persistent time of each activity and the configuration schemes for the required resources, as well as the scheduling strategies for the selectable resources, the invention comprehensively evaluates a plurality of indexes, such as the running time, the running cost, the final product quality, the utilization rate of the product, the queue length, etc. The invention adopts the method which divides the problem space in a multi-layer way to process the combinatorial constraint relationship among the various optimal parameters, thereby increasing the flexibility of the optimization parameter selection, and avoiding the analysis calculation to the ineffective parameter combination scheme, furthermore facilitating the maintenance of the group diversity.

Description

technical field [0001] The invention relates to the field of enterprise engineering and information technology, in particular to a genetic algorithm-based multi-objective enterprise process model parameter optimization method. Background technique [0002] The enterprise process model is a simplification and abstraction of the complex object of the enterprise, including not only the activities that make up the process and the logical relationship between the activities, but also the products that are the input and output of the activities, and the resource objects that support the execution of the activities. Through the simulation analysis and optimization of the enterprise process model, the business bottlenecks existing in the enterprise can be found, and a scientific basis can be provided for the transformation and optimized operation of the enterprise. Enterprise process optimization is divided into two tasks: structural optimization and parameter optimization. Paramete...

AI Technical Summary

Problems solved by technology

There is a great correlation between these goals. For example, the longer the execution time of an activity is under a certain amount of resources, the higher the cost of the activity will be. However, shortening the execution time of the activity does not necessarily reduce the cost of the activity, because shortening the execution of the activity Time is at the expense of increasing the number of related resources, possibly at the cost of running the final process

Images