Improved particle swarm optimization method based on streamline production scheduling of fuzzy due date

A technology of pipeline production and optimization method, applied in the field of improved particle swarm optimization algorithm, which can solve the problem of particle swarm optimization algorithm falling into local extremum

Inactive Publication Date: 2010-10-13
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, in the actual optimization process, the particle swarm algorithm may fall into the local extre

Method used

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  • Improved particle swarm optimization method based on streamline production scheduling of fuzzy due date
  • Improved particle swarm optimization method based on streamline production scheduling of fuzzy due date
  • Improved particle swarm optimization method based on streamline production scheduling of fuzzy due date

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Embodiment Construction

[0050] An improved particle swarm optimization method based on production scheduling, comprising the following steps:

[0051] Step 1. Encode the particles.

[0052] represents a solution to the shop-shop scheduling problem, where n represents the dimensionality of the particles in the particle swarm, Indicates the position value of the i-th particle in the k-th generation in evolution, if Indicates that the particle i (workpiece) of the kth generation is not processed on the jth machine, if Indicates that the k-th generation particle i (workpiece) is processed on the j-th machine; Indicates the speed of the i-th particle when it evolves to the k-th generation, k is the number of iterations; the best position experienced by the individual particle is recorded as P best , the best position experienced by the whole group of particles is denoted as G best .

[0053] Step 2. Determine the objective function for the completion period of the artifact.

[0054] In the F...

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Abstract

The invention relates to an improved particle swarm optimization method based on flow production scheduling of a fuzzy due date. The optimal solution of the existing algorithm can not be found easily. The method comprises the following steps: using the penalty function for neighborhood block design of key lines based on the needs of the flow shop scheduling of the fuzzy due date, establishing a taboo table, and adopting the neighborhood search strategy, thereby enhancing the effect of optimal solution of the improved particle swarm algorithm. Since the stagnation can easily occur to the particle swarm optimization, the concepts of exchange operators and exchange sequences are introduced to reconstruct the particle location formula and the speed optimization formula of the particle swarm optimization, and the introduction of the taboo search algorithm helps the particle swarm optimization to search the best solution in the local area. Simulation experiments on the flow production scheduling of the fuzzy due date demonstrate that the improved particle swarm optimization method facilitates the overall solution.

Description

technical field [0001] The invention belongs to the technical field of information and control, and relates to an improved particle swarm optimization algorithm. Background technique [0002] With the increasingly fierce market competition, every enterprise is looking for a better production and operation management method to improve the production, operation and management efficiency of the enterprise, thereby enhancing the competitiveness of the enterprise. The core of the entire advanced manufacturing system to realize the development of management technology, optimization technology, automation and computer technology is the problem of production scheduling. The production scheduling problem is a problem of studying the processing sequence of workpieces on the machine and the division of production batches. Therefore, it is a typical NP problem that is comparable to the difficulty of the salesman problem (TSP) in the case of urban asymmetry. The scope of its research is ...

Claims

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Application Information

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IPC IPC(8): G05B13/02
Inventor 柳毅张树人李道国
Owner HANGZHOU DIANZI UNIV
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