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Research of Improved Hopfield Neural Network Algorithm on Single Dynamic Scheduling Problem

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Tutor: LuoYaBo
School: Wuhan University of Technology
Course: Industrial Engineering
Keywords: Job Shop Scheduling,Optimization algorithm,Artificial Neural Networks,Single mac
CLC: F224
Type: Master's thesis
Year:  2010
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Abstract:
Shop scheduling optimization problem, as the core of the manufacturing enterprise, is a class of time constraints, order constraints, and resource constraints of combinatorial optimization problems. Reasonable scheduling of workshop tasks help to realize the rational allocation of corporate resources, improve labor productivity and processing equipment utilization, reduce production costs, and thus shop scheduling research has a very important meaning, has also become Currently one of the most gravitational field of study. In recent years, most shop scheduling complexity shop scheduling, its research methods is also diversity, use of improved Hopfield neural network algorithms to solve the stand-alone dynamic scheduling problem is a new way. Various constraints of the study was to analyze the task to establish the optimization model to solve the scheduling problem, dynamic scheduling tasks improved Hopfield neural network algorithm, weighted total extension of time to achieve the target. The main contents of this paper are as follows: (1) dynamic workshop task scheduling problem. Comparison and analysis of the research status of the previous shop scheduling problem, except, as well as their respective advantages and disadvantages, the main outlines the general shop scheduling problem, as well as a single team the single shop scheduling problem status quo with its problems, describes the classification of the production scheduling problem and production scheduling model, and a brief description of the main content of the research paper. (2) shop scheduling method theory. An overview of the theoretical knowledge as well as shop scheduling shop scheduling method most commonly used several heuristic algorithms, which introduces artificial neural network algorithm used in this study, the simulated annealing algorithm and genetic algorithm to analyze the advantages of various algorithms and inadequacies, and combined with the characteristics of the research shop scheduling problem, design a Hopfield neural network algorithm and simulated annealing algorithm combining improved Hopfield neural network algorithm. (3) the realization of the method as well as the research and development of the scheduling system scheduling problems. Dynamic scheduling problem based on the stand-alone described in this study, combined with the problem constraints and The algorithm establish a corresponding mathematical model of the scheduling problem, given the concrete steps of the research method for solving the problem. Theoretical study based on the above-mentioned problems, the development of a shop scheduling system for the research, shop scheduling process visualization, and ultimately to scheduling the results of the comparative analysis of the algorithm. Finally, the summary of the main results of this study, as well as shortcomings, the next step in the research work and analysis and outlook, and put forward their own views.
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