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The Intelligence of the Production Line Adjusts One Degree Method and It is Applied

Author: GuoKe
Tutor: YanShuTian
School: Lanzhou University of Technology
Course: Mechanical Manufacturing and Automation
Keywords: Production scheduling Genetic Algorithms CIMS Artificial Neural Networks
CLC: TH165
Type: Master's thesis
Year: 2009
Downloads: 163
Quote: 1
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Abstract


The key part of the implementation of CIMS production scheduling, production scheduling theory research or application development by academia and the business community concern. Production scheduling problems are usually multi-constraint, multi-objective, random uncertainty optimization problem., Has been proved to belong to the NP problem. Many studies have shown that the optimal solution to find a scheduling problem is very difficult, most engineering algorithm is to abandon the goal of finding the optimal solution, instead trying to find a reasonable, limited time approximate useful solution. This paper systematically studied the production scheduling problem of genetic algorithms, neural networks, fuzzy theory such as intelligent method for solving the above methods and made some improvements, and successfully applied to actual projects. The main research results obtained in this article are as follows: 1. Systematic study of many varieties, small batch production scheduling problem is discrete and non-time representation of the two modeling methods based on the uniform time discretization, explore two modeling methods relationship and its transformation methods, and batch process optimization process model is extended to batch process and continuous process of coexistence. The establishment of continuous multi-product production lines in parallel loop scheduling model, as well as the nonlinear production scheduling model linearization method. System studied genetic algorithm for flexible workshop production scheduling problem, discussed in detail in the related technology of the flexible workshop production scheduling problem genetic algorithm. Widespread uncertainties in the actual production scheduling problem, discussed the method of genetic algorithm for fuzzy flexible job shop scheduling problem. Given instance of a car engine factory machine shop flexible scheduling problem using genetic algorithm. Improved Hopfield neural network method for solving the job shop scheduling problem. In order to avoid the Hopfield neural network may converge to local minima problems, the simulated annealing algorithm is applied to the Hopfield neural network is proposed to solve the production scheduling method immediately neural network. Compared with existing algorithms, the proposed method can guarantee that the steady-state output of the neural network is adjustable. Study more variety, small batch production scheduling method based on genetic algorithms, given the many varieties of discrete and continuous variables, the genetic coding method for small batch production scheduling problem. General than the expected time of receipt of the order production process proposed dynamic scheduling method for the continuous production process with fuzzy delivery caused by changes in orders, based on orders received to achieve dynamic scheduling.

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CLC: > Industrial Technology > Machinery and Instrument Industry > Machinery Manufacturing Technology > Flexible manufacturing systems and flexible manufacturing cell
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