Jop-Shop scheduling method based on QEA variable rotation angle distance

Abstract

The invention discloses a Jop-Shop scheduling method based on a QEA variable rotation angle distance. Quantum chromosome coding based on procedures is adopted for expressing a schedule, and by detecting the validity of the schedule, the searching efficiency of a solution space is increased; furthermore, a quantum evolution algorithm mode based on the QEA variable rotation angle is utilized for generating a filial generation, so that the diversity of solutions is effectively expanded, and the convergence to a local optimal solution is effectively prevented; in addition, the convergence speed is increased, and a global optimal solution is finally obtained with a minimal time cost (namely the optimal scheduling scheme is realized). According to the invention, the binary solution mode, the decimal solution mode, the procedure solution mode and the quantum evolution algorithm based on the quantum evolution algorithm are utilized, so that the efficiency of the method is better than the efficiency of other methods.

Description

technical field [0001] The invention relates to a Jop-Shop scheduling method based on QEA variable rotation angle distance. Background technique [0002] The QEA algorithm is the abbreviation of quantum heuristic evolutionary algorithm, which is a kind of evolutionary algorithm. The algorithm uses qubit codes to represent chromosomes, and uses quantum gate updates to complete evolutionary search. It has the characteristics of small population size, fast convergence speed and strong global optimization ability. [0003] There are many kinds of production scheduling problems and various methods, among which Job-Shop Scheduling Problem is the most basic and famous machine scheduling problem, and it is also one of the most difficult NP-hard combinatorial optimization problems. People have made decades of efforts to solve this problem, but so far the most advanced algorithms are still difficult to find the optimal solution for smaller problems. [0004] The problem can usually ...

AI Technical Summary

Problems solved by technology

[0008] (2) Each process must be processed on a designated machine;

[0011] (5) Each machine can only process one workpiece at the same time;

[0012] (6) Each workpiece can only be processed on one machine at the same time;

[0013] (7) There is no constraint on the processing sequence between workpieces;

[0017] However, these algorithms have certain defects. For example, with the expansion of the genetic algorithm, the optimal solution cannot be obtained, and the convergence speed is slow; although the simulated annealing algorithm performs better than the genetic algorithm, it can also find a better solution for more complex constraints. Good deconstruction type, but the convergence speed also needs to be improved; the particle swarm algorithm has a good improvement in the convergence speed, but there is a defect that it is easy to fall into local optimum

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