简论装配线基于混合遗传算法强约束混装平衡理由

论文导读：on17-202.1.1Descriptionandassumptions17-182.1.2Definitionsandnotations18-192.1.3CharacteristicanalysisofMMALB-P19-202.2ClassificationofMMALB-P20-222.2.1Objective-basedclassification202.2.2Configuration-basedclassification20-222.3Evaluationcriterion22-252.

3.1Assesentsofsolutio

摘要：商品的标准化致使竞争越来越激烈，而多样化产品需求也给制造业生产组织带来了前所未有的挑战。在标准化和多样化两极之间，制造企业为了满足客户提供的个性化产品和服务，越来越广泛的利用混流装配线，不转变或较少转变现有生产设备，通过对装配线的优化，实现多品种装配，用大批量生产的制造成本和响应速度。汽车产品需求多样化促使越来越多的汽车制造商将多品种混合装配作为增强其竞争能力的有效手段。由此混合装配线的平衡成为制造业进展中最需要解决的不足。本论文对强约束混合装配线平衡不足进行了调度探讨。文章根据强约束联系的特点，结合了传统的遗传算法和启发式因子对该不足进行了深入的探讨。浅析了混合装配线平衡不足和强约束联系的特性以及强约束联系对混合装配线平衡的影响，将实际生产中的常见强约束不足与普通的混合装配线平衡不足集成一体，为实际生产制造提供论述依据。针对强约束混合装配线平衡这一复杂不足构建数学模型，并以三个方面对传统的遗传算法进行了改善：1、在传统的实验数据的基础上加入强约束联系，建立了新的联合优先联系图，将混合装配线平衡不足转化为简单不足。这些混合装配线平衡不足的建模为实际生产制造业提供了论述指导、策略和工具。2、种群初始化历程中新引进了三个启发式因子：最长操作时间，最多直接后续操作个数及最多可更新操作个数。3、考虑了强约束联系，本论文在交叉和变异的历程中采取了逻辑串，以提升解的可行性，为实际工作提供参考价值。本论文运用了混合遗传算法对所提出的强约束混合装配线平衡不足进行浅析，并用九大典型案例对所提出的数学模型进行求解，改善的初始化策略提升了初始解的可行性，并且均能在较短时间内取得最优解/较优解，结果证明混合遗传算法对解决强约束混合装配线平衡不足的有效性。关键词：混合装配线论约束操作论文联合优先联系图论文遗传算法论文
本论文由www.7ctime.com,需要论文可以联系人员哦。Abstract4-5
摘要5-6
Content6-12
Chapter 1 Introduction12-17

1.1 Background12-13

1.2 Literature review13-16

1.2.1 Exact algorithm14-15

1.2.2 Heuristic and meta-heuristic algorithm15-16

1.2.3 Hybrid approaches16

1.3 Organization16-17

Chapter 2 Mixed-model assembly pne balancing problem17-25

2.1 Problem formulation17-20

2.

1.1 Description and assumptions17-18

2.

1.2 Definitions and notations18-19

2.1.3 Characteristic analysis of MMALB-P19-20

2.2 Classification of MMALB-P20-22

2.1 Objective-based classification20

2.2.2 Configuration-based classification20-22

2.3 Evaluation criterion22-25

2.3.1 Assesents of solutions22

2.3.2 Measures of difficulty22-23

2.3.3 Illustrative example23-25

Chapter 3 Modepng MMALB-P with sequence-dependent tasks25-33

3.1 Sequence-dependent tasks25-28

3.

1.1 Formulation and description25-26

3.1.2 Researches and illustrative example26-28

3.2 Combined precedence relation28-31

3.2.1 Traditional combined precedence relation论文导读：m51-565.2Sensitivityanalysis56-585.2.1Populationsizeandgeneticgeneration56-575.2.2Influenceofcrossoverandmutationrate57-585.erformanceevaluation58-655.3.1Initiapzationperformance58-595.3.2Convergenceperformance59-615.3.3Solutioncomparison61-65Chapter6Conclusionsandfuturer
28
3.2.2 Combined precedence relation with sequence-dependent tasks28-30
3.

2.3 Precedence matrix30-31

3.3 Mathematical model of MMALB-P with sequence-dependent tasks31-33

3.1 Objectives31-32

3.2 Constraints32-33

Chapter 4 Hybrid genetic algorithm for MMALB-P with sequence-dependent tasks33-47

4.1 Formulation and characteristics33-36

4.

1.1 Formulation33

4.

1.2 Characteristics33-35

4.1.3 Hybrid genetic algorithm-based framework35-36

4.2 Initial population36-40

4.

2.1 Random initiapzation36-37

4.

2.2 Heuristic initiapzation process37-39

4.2.3 Priority rules for selecting tasks39-40
4.3 Encoding scheme and decoding procedure40-41
4.

3.1 Encoding scheme40

4.

3.2 Decoding procedure40-41

4.4 Selection mechani41-42

4.1 Epte preservation strategy42

4.2 Tournament selection strategy42

4.3 Combination of two strategies42

4.5 Crossover42-45

4.5.1 Common crossover operator42-43

4.5.2 Logic string43-44

4.5.3 Two-point crossover operator with sequence-dependent tasks44-45

4.6 Mutation45-47

4.6.1 Common mutation operator45

4.6.2 Logic string45

4.6.3 Insertion mutation with sequence-dependent tasks45-47
Chapter 5 Experimental study47-65

5.1 Illustrative example47-56

5.

1.1 Small-size problem48-49

5.

1.2 Medium-size problem49-51

5.

1.3 Large-size problem51-56

5.2 Sensitivity analysis56-58
5.2.1 Population size and genetic generation56-57
5.2.2 Influence of crossover and mutation rate57-58

5.3 Performance evaluation58-65

5.

3.1 Initiapzation performance58-59

5.

3.2 Convergence performance59-61

5.

3.3 Solution comparison61-65

Chapter 6 Conclusions and future research65-67

6.1 Conclusions65-66

6.2 Future research66-67

References67-71
List of pubpcations and rewards71-72
Acknowledgements72