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Maximum Maintainability of Complex Systems via Modulation Based on DSM and Module Layout Case Study: Laser Range Finder

机译:通过DSM和模块布局通过调制实现复杂系统的最大可维护性案例研究:激光测距仪

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Layout design of complex systems in detailed engineering phase is a costly and difficult job which usually entails dealing with multiple conflicting objectives. The present paper aims to investigate the effect of four objective functions have been considered simultaneously in this research. The present paper aims to investigate the effects of modularity and the layout of subsystems and parts of a complex system on its maintainability. For this purpose, four objective functions have been considered simultaneously: I) maximizing the level of accordance between system design and optimum modularity design, II) maximizing the level of accessibility and the maintenance space required HI) maximizing the providing of distance requirement and IV) minimizing the layout space. The first objective function has been put forward for the first time in the present paper and in it, the optimum system modularity design was determined using the Design Structure Matrix (DSM) technique. The second objective function is combined with the concept of Level of Repair Analysis (LoRA), thus, a new objective function is developed Simultaneous optimization of the above-mentioned objective functions has not been considered in previous studies. The multi objective problem which has been put forward was applied on a laser range finder containing 17 subsystems. As the resulting model is NP-Hard and entails quantifications of some qualitative data, a near optimal solution method is suitable to tackle it. Hence, in order to obtain the non- dominated solutions, a multi-objective particle swarm optimization (MOPSO) algorithm is used
机译:在详细的工程阶段,复杂系统的布局设计是一项昂贵且困难的工作,通常需要处理多个相互矛盾的目标。本文旨在调查在此研究中同时考虑的四个目标函数的影响。本文旨在研究模块化以及子系统和复杂系统各部分的布局对其可维护性的影响。为此,同时考虑了四个目标功能:I)最大化系统设计与最佳模块化设计之间的一致性; II)最大化可访问性和所需的维护空间; HI)最大化提供距离要求; IV)最小化布局空间。本文首次提出了第一个目标函数,其中使用设计结构矩阵(DSM)技术确定了最佳的系统模块化设计。第二目标函数与维修水平分析(LoRA)的概念相结合,因此,开发了新的目标函数。上述目标函数的同时优化在以前的研究中并未考虑。提出的多目标问题应用于包含17个子系统的激光测距仪。由于生成的模型是NP-Hard,并且需要对一些定性数据进行量化,因此适合采用一种接近最佳的求解方法。因此,为了获得非控制解,使用了多目标粒子群算法(MOPSO)

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