Flexible job-shop scheduling multi-objective method

Abstract

The invention discloses a flexible job-shop scheduling multi-objective method. The specific implementation procedures of the flexible job-shop scheduling multi-objective method are that: establishing a model for a multi-objective flexible job shop; optimizing the model by using an ant colony algorithm, and evaluating the result to check whether the scheme is the optimal scheme; and improving a pheromone updating rule according to the optimal scheme, and increasing convergence rate of the algorithm to obtain a Pareto optimal solution of a multi-objective flexible job shop scheduling problem. Compared with the prior art, the flexible job-shop scheduling multi-objective method effectively reduces workshop production cost, shortens the processing time, improves qualified rate of products, is high in practicability, and is easy to popularize.

Description

technical field [0001] The invention relates to computer integrated manufacturing technology, in particular to a flexible job shop scheduling multi-objective method with strong practicability. Background technique [0002] In the prior art workshop scheduling process, it is difficult to achieve optimal scheduling. Scheduling here refers to the scheduling of production cost, production time and product qualification rate. In actual work, the solution to the scheduling problem can be roughly There are two categories: optimization methods and approximation methods. [0003] 1. Optimization methods include mathematical programming (dynamic programming, integer programming), branch and bound method, Laplace relaxation method, etc. [0004] 2. Approximate optimization methods include heuristic methods, simulation-based methods, graph search methods, heuristic algorithms (genetic algorithm, simulated annealing algorithm, tabu search algorithm, ant colony algorithm, particle swarm ...

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Problems solved by technology

[0007] Although this type of method can obtain the optimal solution in theory, it can only solve small-scale problems due to its complicated calculation.

[0008] At the same time, the above heuristic methods have the problem of how to determine the appropriate heuristic rules for various decisions and how to implement each heuristic rule at a low level; the simulation-based method lacks theoretical basis, high cost, and the accuracy of simulation is heavily dependent on programming. The problem of personnel’s judgment and experience; the problem of how to improve the search efficiency and reduce the memory usage in the graph search method; the determination of the weight coefficient by the weight coefficient method has relatively subjective factors, which have great limitations on the experimental results

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