基于改进遗传算法的车间作业调度问题研究
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摘要
车间作业调度问题是企业实现高效率、高柔性和高可靠性生产的关键。研究有效实用的调度方法和优化技术已成为先进制造技术的基础。本论文对车间作业调度问题展开了研究,主要内容如下：
第一章为绪论：主要介绍论文研究的背景和意义,论述了研究对象——车间作业调度问题的基本定义、特点、主要研究方法以及非柔性车间作业调度问题和柔性车间作业调度问题的研究现状。经比较分析,本文采用各方面都有很大优势的遗传算法作为本论文的主要求解方法,并对遗传算法的基本原理进行了阐述。
第二章为基于分区遗传算法求解非柔性车间作业调度问题。首先提出一种能显著减少冗余的工序编码方式——分区编码,然后对于小规模调度问题,通过字典法列出分区编码的全排列,并采用当前可加工工序查找法对各编码的可行性进行判断；对于大规模调度问题,基于分区编码方式,提出一种改进遗传算法——分区遗传算法。由于编码搜索空间较小,通过经典算例验证,证明该算法能明显提高收敛速度,并获得更好的优化结果。
第三章为基于嵌套遗传算法求解柔性车间作业调度问题。基于机器分配码及其对应的分区编码,提出了一种嵌套遗传算法,即在机器分配码的确定过程中嵌入加工顺序码的求解过程。采用双码结构编码,使个体意义更明确,避免产生冗余编码；采用拟水平均匀设计方法初始化,使个体在有限个数内最大限度地均匀分布,保证群体多样性；提出一种进化环思想,并基于该思想提出了入侵选择策略和变动概率的变异策略,使种群进化分阶段进行,既保证算法加速收敛,又避免早熟；基于约束理论思想提出瓶颈优化交叉方式,使交叉更具有目的性；用两种判别规则——基于极限最优适应度值的判别规则和基于当前最优适应度值的判别规则控制算法。通过对经典算例的对比分析,验证了该算法具有较好的优化质量和收敛性能。
第四章为基于神经网络的遗传算法参数设计。论述了遗传算法各参数的作用,采用神经网络中能以任意精度逼近任何非线性函数的BP神经网络对遗传算法参数进行设计。首先对不同车间调度问题均匀设计试验参数,并通过大量试验得到各样本问题的最优参数；然后根据问题模型构建了一个具有三层结构的BP神经网络。该神经网络经过样本检验,证明其性能很好,同时也实现了对不同车间调度问题智能设计遗传算法各参数值。
第五章为HTC公司车间作业调度实例应用：对实际车间调度情况建立模型,并基于本文的各种算法开发系统,用以解决实际问题。
第六章为全文总结与工作展望：总结本文所做的工作,详细列出本文的创新点,并指出进一步研究的工作方向。
Job Shop Scheduling Problem is the key for enterprises to achieve high efficiency, flexibility and reliability production. The research on practical and effective scheduling method and optimization technology has become the basis of advanced manufacturing technology. Job Shop Scheduling Problem was deeply studied in this paper and the main contents were as follows:
Chapter I:This chapter introduced the research background and significance of the paper, described the basic definition, features and main solving methods of the research object—Job Shop Scheduling Problem and research situation of Non-Flexible Job Shop Scheduling Problem and Flexible Job Shop Scheduling Problem. After the comparative analysis, genetic algorithm was adopted as the main methods of solving problems, and the basic principle of genetic algorithms was expounded.
Chapter II:This chapter was based on improved genetic algorithm to solve Non-Flexible Job Shop Scheduling Problem. For small-scale problems, partition coding method, a kind of anti redundant encoding method, was proposed. Then full permutation of partition coding method was listed by dictionary ordered method. And the feasibility of all codes was evaluated by searching current process method. For large-scale problems, an improved genetic algorithm—partition genetic algorithm based on partition coding method was proposed. This algorithm can significantly reduce redundant codes, narrow searching space, and improve searching efficiency. Case-studies based on some typical benchmark-examples were carried out to evaluate the algorithm, and got results.
Chapter III:This chapter was based on an improved genetic algorithm to solve Flexible Job Shop Scheduling Problem. Genetic algorithm step by step which achieved processing order after machine distribution was proposed, and avoided redundancy individuals and effectively reduced the search space. In order to clear the meaning of individual and avoid producing redundant codes, double-chain structure coding was used to code the chromosome. In order to maximum uniformly distribute the limited individual and ensure diversity, population was initialized with quasi level uniform design. An evolutionary ring was proposed in this chapter. Based on this idea, intrusion selection strategy and variable probability mutation strategy were proposed, which made evolution executes by stages, ensure algorithm accelerating convergence, and avoid early mature. Based on Theory of Constraint, optimize-bottleneck-crossover was proposed, which made crossover more purposeful. The algorithm was controlled with two rules based on extreme or current-optimal-fitness. Case-studies based on some typical benchmark-examples were carried out to evaluate the algorithm, and the results show a quick speed and powerful optimizing capability.
Chapter IV:This chapter was based on Artificial Neural Network to design parameters of genetic algorithm. The chapter discussed the role of each parameter of genetic algorithm. BP neural network was adopted, which can achieve any nonlinear function with arbitrary precision. First, testing parameters were uniformly designed for different job shop scheduling problems. Through a lot of tests, the best parameter of each sample problem was obtained. Then based on the problem model, a BP neural network with a three-layer structure was constructed. This neural network proved its performance after sample studies, achieving the goal solving genetic algorithm parameters for intelligent design of different job shop scheduling problems.
Chapter V:This chapter was the application of HTC Corporation Job Shop Scheduling case. The actual job shop scheduling situation was modeled. In order to solve the practical problem, the system was developed based on each algorithm of this article.
Chapter VI Give the summary of the whole paper; list the innovation of the paper, and point out the direction for further research.
第一章为绪论：主要介绍论文研究的背景和意义,论述了研究对象——车间作业调度问题的基本定义、特点、主要研究方法以及非柔性车间作业调度问题和柔性车间作业调度问题的研究现状。经比较分析,本文采用各方面都有很大优势的遗传算法作为本论文的主要求解方法,并对遗传算法的基本原理进行了阐述。
第二章为基于分区遗传算法求解非柔性车间作业调度问题。首先提出一种能显著减少冗余的工序编码方式——分区编码,然后对于小规模调度问题,通过字典法列出分区编码的全排列,并采用当前可加工工序查找法对各编码的可行性进行判断；对于大规模调度问题,基于分区编码方式,提出一种改进遗传算法——分区遗传算法。由于编码搜索空间较小,通过经典算例验证,证明该算法能明显提高收敛速度,并获得更好的优化结果。
第三章为基于嵌套遗传算法求解柔性车间作业调度问题。基于机器分配码及其对应的分区编码,提出了一种嵌套遗传算法,即在机器分配码的确定过程中嵌入加工顺序码的求解过程。采用双码结构编码,使个体意义更明确,避免产生冗余编码；采用拟水平均匀设计方法初始化,使个体在有限个数内最大限度地均匀分布,保证群体多样性；提出一种进化环思想,并基于该思想提出了入侵选择策略和变动概率的变异策略,使种群进化分阶段进行,既保证算法加速收敛,又避免早熟；基于约束理论思想提出瓶颈优化交叉方式,使交叉更具有目的性；用两种判别规则——基于极限最优适应度值的判别规则和基于当前最优适应度值的判别规则控制算法。通过对经典算例的对比分析,验证了该算法具有较好的优化质量和收敛性能。
第四章为基于神经网络的遗传算法参数设计。论述了遗传算法各参数的作用,采用神经网络中能以任意精度逼近任何非线性函数的BP神经网络对遗传算法参数进行设计。首先对不同车间调度问题均匀设计试验参数,并通过大量试验得到各样本问题的最优参数；然后根据问题模型构建了一个具有三层结构的BP神经网络。该神经网络经过样本检验,证明其性能很好,同时也实现了对不同车间调度问题智能设计遗传算法各参数值。
第五章为HTC公司车间作业调度实例应用：对实际车间调度情况建立模型,并基于本文的各种算法开发系统,用以解决实际问题。
第六章为全文总结与工作展望：总结本文所做的工作,详细列出本文的创新点,并指出进一步研究的工作方向。
Job Shop Scheduling Problem is the key for enterprises to achieve high efficiency, flexibility and reliability production. The research on practical and effective scheduling method and optimization technology has become the basis of advanced manufacturing technology. Job Shop Scheduling Problem was deeply studied in this paper and the main contents were as follows:
Chapter I:This chapter introduced the research background and significance of the paper, described the basic definition, features and main solving methods of the research object—Job Shop Scheduling Problem and research situation of Non-Flexible Job Shop Scheduling Problem and Flexible Job Shop Scheduling Problem. After the comparative analysis, genetic algorithm was adopted as the main methods of solving problems, and the basic principle of genetic algorithms was expounded.
Chapter II:This chapter was based on improved genetic algorithm to solve Non-Flexible Job Shop Scheduling Problem. For small-scale problems, partition coding method, a kind of anti redundant encoding method, was proposed. Then full permutation of partition coding method was listed by dictionary ordered method. And the feasibility of all codes was evaluated by searching current process method. For large-scale problems, an improved genetic algorithm—partition genetic algorithm based on partition coding method was proposed. This algorithm can significantly reduce redundant codes, narrow searching space, and improve searching efficiency. Case-studies based on some typical benchmark-examples were carried out to evaluate the algorithm, and got results.
Chapter III:This chapter was based on an improved genetic algorithm to solve Flexible Job Shop Scheduling Problem. Genetic algorithm step by step which achieved processing order after machine distribution was proposed, and avoided redundancy individuals and effectively reduced the search space. In order to clear the meaning of individual and avoid producing redundant codes, double-chain structure coding was used to code the chromosome. In order to maximum uniformly distribute the limited individual and ensure diversity, population was initialized with quasi level uniform design. An evolutionary ring was proposed in this chapter. Based on this idea, intrusion selection strategy and variable probability mutation strategy were proposed, which made evolution executes by stages, ensure algorithm accelerating convergence, and avoid early mature. Based on Theory of Constraint, optimize-bottleneck-crossover was proposed, which made crossover more purposeful. The algorithm was controlled with two rules based on extreme or current-optimal-fitness. Case-studies based on some typical benchmark-examples were carried out to evaluate the algorithm, and the results show a quick speed and powerful optimizing capability.
Chapter IV:This chapter was based on Artificial Neural Network to design parameters of genetic algorithm. The chapter discussed the role of each parameter of genetic algorithm. BP neural network was adopted, which can achieve any nonlinear function with arbitrary precision. First, testing parameters were uniformly designed for different job shop scheduling problems. Through a lot of tests, the best parameter of each sample problem was obtained. Then based on the problem model, a BP neural network with a three-layer structure was constructed. This neural network proved its performance after sample studies, achieving the goal solving genetic algorithm parameters for intelligent design of different job shop scheduling problems.
Chapter V:This chapter was the application of HTC Corporation Job Shop Scheduling case. The actual job shop scheduling situation was modeled. In order to solve the practical problem, the system was developed based on each algorithm of this article.
Chapter VI Give the summary of the whole paper; list the innovation of the paper, and point out the direction for further research.
引文
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