Flexible job shop scheduling method based on multi-heuristic information ant colony system

Abstract

The invention belongs to the field of intelligent algorithm and production management and relates to a flexible job shop scheduling method based on a multi-heuristic information ant colony system, including: extraction drawing model building, initialization; construction of the solution set of FJSP; pheromone updating; peuristic information file updating; termination condition check. The inventionintroduces six kinds of heuristic information into the ant colony system, so that the algorithm can obtain ideal performance in different problem instances, thus avoiding falling into local optima, and an adaptive strategy capable of automatically selecting the heuristic information and the guiding force control parameters thereof according to the problem instance to be solved and the current search state is used, the heuristic information design of the whole ant colony converges to the current optimal design which can produce high-quality solution, improves the speed of sword collection, improves the optimization efficiency of the algorithm, and improves the efficiency of the flexible job shop scheduling.

Description

technical field [0001] The invention belongs to the field of intelligent algorithms and production management, and relates to a flexible job shop scheduling method based on a multi-heuristic information ant colony system. Background technique [0002] The Flexible Job-shop Scheduling Problem (FJSP) is an extension of the traditional Job-shop Scheduling Problem (JSP). Each process can be completed by multiple different machines, and the Completion times vary. Specifically, an N×M FJSP is described as follows: there are N workpieces to be processed and M available machines, and the workpiece set is {p 1 ,p 2 ,...,p N}, the set of machines is {m 1 ,m 2 ,...,m M}, each workpiece contains one or more processes, n of workpiece i (i=1,2,...,N) i The process consists of a collection op i ={op i1 ,op i2 ,...,op ini}express. The processing sequence of the workpiece is determined, and each process can be processed on multiple different machines. The goal of the problem is t...

AI Technical Summary

Problems solved by technology

Compared with ACS, GA cannot make full use of a large amount of heuristic information, and in different problem instances, the focus of constraints is different, so using GA to solve FJSP has certain limitations

The ACS method can make full use of a variety of heuristic information in the construction process of the solution, and has wider applicability, but the ACS method has the disadvantages of slow closing speed and easy to fall into local optimum

Images