面向复杂产品概念设计的综合集成研讨厅问题求解过程与方法研究
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摘要
综合集成研讨厅(Hall for Workshop of Metasynthetic Engineering, HWME)是我国科学家提出的处理复杂系统的创造性成果,它指导人们在处理复杂问题时,把专家的智慧、计算机的高性能和各种数据、信息有机的结合起来,构成一个统一的、强大的问题求解系统。目前,HWME系统已经在地理和环境、工程、医学、社会经济、军事等诸多领域得到了广泛应用,但是由于HWME系统本身的复杂性和现有支撑技术的限制,构建一个面向复杂产品概念设计的HWME系统仍然是一个具有挑战性的课题。
系统的功能与其结构紧密相关,HWME作为一个处理复杂性问题的系统平台,如何合理的搭建它的系统体系结构对这一平台的性能有着至关重要的意义。本文以某复杂产品概念设计为背景,从问题求解的角度,对综合集成研讨厅从问题求解过程与求解方法的角度进行了研究,包括复杂产品概念设计的问题求解过程建模,问题的分解和分配、问题的求解及多目标优化技术等理论与方法,旨在为应用于复杂产品概念设计的HWME系统提供技术支撑。
首先以综合集成理论为指导,探讨并深入研究了在HWME中进行复杂产品概念设计的求解过程和方法；借鉴当前国内外学者采用进化计算求解经济、社会等复杂问题的思路,提出了HWME中问题求解过程的数学形式描述,建立了基于进化计算的HWME问题求解过程模型,利用进化计算在综合集成研讨厅的定性和定量空间中搜索,实现了人机结合、专家群体创新思维激发、多领域专家群体之间协同及其群体论证的综合集成。
问题分解是任何复杂问题求解的基础,同样在运用综合集成研讨厅来进行复杂产品概念设计过程中,也需要首先对所求解问题(即研讨问题)进行分解。当前许多研究人员对复杂产品概念设计中涉及的问题分解技术进行了研究,但并不存在一种通用的方法。本文在研究综合集成研讨厅中进行复杂系统问题分解的特殊性基础上,提出了基于专家群体研讨问题的分解模型,并提出了一个采用遗传算法与设计结构矩阵相结合来解决复杂问题分解的智能方法,以解决一般意义上的复杂系统问题求解中的问题分解。实践证明,该方法能够避免大规模修改的发生,并加速复杂系统分解过程中问题结构化的寻优效率。
在复杂产品概念设计过程中,随着待求解问题的规模及复杂度的增加,如何协调各个求解主体,调度求解资源,使整个复杂问题的求解流程更加合理和有序,是综合集成研讨厅系统问题求解过程中需要解决的关键课题。以往的HWME中复杂问题研讨求解的流程是一种经验性质的工作流规范。作为一种人机结合的系统,HWME需要计算机对其提供智能化的支持。本文通过对HWME中复杂问题求解过程与蚁群算法相似性的分析,建立了相应的数学模型,提出了复杂问题求解过程中进行任务动态分配的智能蚁群算法,收到了较好的效果。
通过概念的生成和选择形成产品的设计方案,是复杂产品概念设计的一个核心任务。在综合集成研讨厅中专家群体经由群体论证,获得复杂产品概念设计方案的过程中面临着如何在合理的时间内获得满意/优化解、提高求解效率的挑战。从复杂决策问题求解的角度,结合当前的人工智能技术,本文提出了一种基于改进的交互式遗传算法模型的群体论证方法。改进的人机交互式遗传算法充分发挥了人机各自的优势,对于求解复杂产品概念设计的问题,对人机合作的“可操作性”问题及实现从定性到定量的有效转换这类HWME中的难点问题,提供了一种有效的方法或途径。此种方式相比于研讨厅中传统的意见共识和思维收敛方法,不仅容易达成群体意见一致、有着更高的求解效率,而且更符合综合集成研讨厅在线研讨的实际,可以方便的进行多次循环论证。
复杂产品概念设计在形成产品的设计方案的过程中,设计人员需要全面的考虑各种约束条件和设计目标,经常遇到需要使得多个目标在给定的可行区域内尽可能最优的问题。本文提出了一种新的协同进化算法和交互式遗传算法相结合的复杂产品概念设计多目标优化方法。利用协同进化算法的多个种群协同合作,实现复杂产品概念设计中多目标方案的生成,在方案的评价过程中,通过交互式遗传算法根据参与者的评价直接获得隐式性能指标问题的适应度值,将设计人员的偏好、直觉、经验等主观因素和创造性知识融合到方案的生成过程中,实现人机的紧密结合。以手机产品的功能设计为例,证明该方法对同时涉及到多目标、人机交互的复杂产品概念设计问题具有较高的求解效率。
Hall for Workshop of Metasynthetic Engineering (HWME) is a creative achievement made by Chinese scientists to deal with complex system. It guides people to integrate the wisdom of the experts, high-performance of computers and a variety of data, information into a unified and powerful problem solving system. Today, HWME has been widely applied in the fields of geography and environment, engineering, medicine, social economy, military and so on. But it is a challenge to construct a HWME facing to conceptual design of complex products due to its complexity and the limitation of current supporting technologies.
The function and structure of a system are closely related to each other, so the system platform of dealing with complex problems, how to establish a reasonable structure of HWME is crucial to the capability of this system platform. In this paper, on the basis of the conceptual design of some complex product, we make research on the problem solving process and solving method of HWME from the angle of problem solving. Aiming at providing supporting technologies for HWME system applied for the conceptual design of complex product, this research includes the modeling of problem solving process of complex product, problem decomposition and allocation, problem solving and the theory and methods of multi-objective optimization technique.
At first, based on meta-synthesis theory, we make a deep research on problem solving process and solving method of conceptual design of complex product. Scholars at home and abroad have adopted evolutionary computation to solve complex decision problems of economy and society. To use their ideas for reference, we propose a mathematical description of problem solving process in HWME and establish a HWME problem solving process modeling based on evolutionary computation.
Problem decomposition is the basis of any complex problem solving. So in the process of conceptual design of complex product using HWME, the decomposition of this problem should be made firstly. Nowadays, many researchers have made research on techniques of problem decomposition involved in conceptual design of complex product, but they still didn't find a universal method. In this paper, based on the analysis of the particularity of problem decomposition of complex systematic problem in HWME, we propose a method of problem decomposition on the basis of the research of expert group. And based on interactive genetic algorithm and the characteristic of design structure matrix, we propose an intelligent method of complex systematic problem decomposition to solve the decomposition in general. Practice shows that using this method can avoid modification in large scale and improve the searching efficiency in problem-structuring of complex system analysis.
In the process of conceptual design of complex product, with the increase of scale and complexity of the problem to be solved, a key subject to be solved in the problem solving process of HWME is how to harmonize subjects, how to dispatch the resources to make the complex problem solving procedure more reasonable and ordered. The existing complex problem solving procedure in HWME is a kind of empirical workflow standard. As a human-computer cooperative system, HWME need intelligent support from computer. Based on the analysis on the similarity between complex problem solving process in HWME and ant colony algorithm, we make corresponding mathematical model and propose the dynamic allocation of task in complex problem solving and design the ant colony algorithm suitable for solving the problem which achieves good result.
The key task of complex product conceptual design is to form a design of the product through generation and choice of concept. In HWME, there is a challenge of how to get the most satisfying and optimum solution to improve solving efficiency during the process of finding conceptual design of complex product after the expert group discussion in HWME. From the angle of complex problem solving and based on current artificial intelligent technology, we propose a group discussion method based on improved interactive genetic algorithm (IGA) model. Improved interactive genetic algorithm gives full play to the advantage of human and computer and provides a effective way to make complex product conceptual design and to solve such difficulties in HWME as the manipulability of man-machine cooperation and the effective conversion from qualitative to quantitative. Compared to the traditional method of opinion consensus and thought convergence in HWME, it is more effective and easier to achieve agreement in expert group. And it confirms the reality of discussion online in HWME and can do repeated circular argument conveniently.
During the process of forming product design of complex product conceptual design, the designers are supposed to think fully about various constraint conditions and design objective and meet the problem of making multi objectives in given feasible region as best as possible. In this paper, we propose a new multi-objective optimization method for complex product conceptual design based on the combination of co-evolutionary algorithm and interactive genetic algorithm. We can form a multi-objective plan for complex product conceptual design through the cooperation of population of co-evolutionary algorithm. And during the evaluation process of the design, we can integrate designers'subjective preference, intuition and experience and creative knowledge into the formation of design through IGA to combine man and computer tightly. We take the design of functions of mobile phone products as an example to prove the high effectiveness of this method for complex product conceptual design related to multi-objective and human-computer interaction.
系统的功能与其结构紧密相关,HWME作为一个处理复杂性问题的系统平台,如何合理的搭建它的系统体系结构对这一平台的性能有着至关重要的意义。本文以某复杂产品概念设计为背景,从问题求解的角度,对综合集成研讨厅从问题求解过程与求解方法的角度进行了研究,包括复杂产品概念设计的问题求解过程建模,问题的分解和分配、问题的求解及多目标优化技术等理论与方法,旨在为应用于复杂产品概念设计的HWME系统提供技术支撑。
首先以综合集成理论为指导,探讨并深入研究了在HWME中进行复杂产品概念设计的求解过程和方法；借鉴当前国内外学者采用进化计算求解经济、社会等复杂问题的思路,提出了HWME中问题求解过程的数学形式描述,建立了基于进化计算的HWME问题求解过程模型,利用进化计算在综合集成研讨厅的定性和定量空间中搜索,实现了人机结合、专家群体创新思维激发、多领域专家群体之间协同及其群体论证的综合集成。
问题分解是任何复杂问题求解的基础,同样在运用综合集成研讨厅来进行复杂产品概念设计过程中,也需要首先对所求解问题(即研讨问题)进行分解。当前许多研究人员对复杂产品概念设计中涉及的问题分解技术进行了研究,但并不存在一种通用的方法。本文在研究综合集成研讨厅中进行复杂系统问题分解的特殊性基础上,提出了基于专家群体研讨问题的分解模型,并提出了一个采用遗传算法与设计结构矩阵相结合来解决复杂问题分解的智能方法,以解决一般意义上的复杂系统问题求解中的问题分解。实践证明,该方法能够避免大规模修改的发生,并加速复杂系统分解过程中问题结构化的寻优效率。
在复杂产品概念设计过程中,随着待求解问题的规模及复杂度的增加,如何协调各个求解主体,调度求解资源,使整个复杂问题的求解流程更加合理和有序,是综合集成研讨厅系统问题求解过程中需要解决的关键课题。以往的HWME中复杂问题研讨求解的流程是一种经验性质的工作流规范。作为一种人机结合的系统,HWME需要计算机对其提供智能化的支持。本文通过对HWME中复杂问题求解过程与蚁群算法相似性的分析,建立了相应的数学模型,提出了复杂问题求解过程中进行任务动态分配的智能蚁群算法,收到了较好的效果。
通过概念的生成和选择形成产品的设计方案,是复杂产品概念设计的一个核心任务。在综合集成研讨厅中专家群体经由群体论证,获得复杂产品概念设计方案的过程中面临着如何在合理的时间内获得满意/优化解、提高求解效率的挑战。从复杂决策问题求解的角度,结合当前的人工智能技术,本文提出了一种基于改进的交互式遗传算法模型的群体论证方法。改进的人机交互式遗传算法充分发挥了人机各自的优势,对于求解复杂产品概念设计的问题,对人机合作的“可操作性”问题及实现从定性到定量的有效转换这类HWME中的难点问题,提供了一种有效的方法或途径。此种方式相比于研讨厅中传统的意见共识和思维收敛方法,不仅容易达成群体意见一致、有着更高的求解效率,而且更符合综合集成研讨厅在线研讨的实际,可以方便的进行多次循环论证。
复杂产品概念设计在形成产品的设计方案的过程中,设计人员需要全面的考虑各种约束条件和设计目标,经常遇到需要使得多个目标在给定的可行区域内尽可能最优的问题。本文提出了一种新的协同进化算法和交互式遗传算法相结合的复杂产品概念设计多目标优化方法。利用协同进化算法的多个种群协同合作,实现复杂产品概念设计中多目标方案的生成,在方案的评价过程中,通过交互式遗传算法根据参与者的评价直接获得隐式性能指标问题的适应度值,将设计人员的偏好、直觉、经验等主观因素和创造性知识融合到方案的生成过程中,实现人机的紧密结合。以手机产品的功能设计为例,证明该方法对同时涉及到多目标、人机交互的复杂产品概念设计问题具有较高的求解效率。
Hall for Workshop of Metasynthetic Engineering (HWME) is a creative achievement made by Chinese scientists to deal with complex system. It guides people to integrate the wisdom of the experts, high-performance of computers and a variety of data, information into a unified and powerful problem solving system. Today, HWME has been widely applied in the fields of geography and environment, engineering, medicine, social economy, military and so on. But it is a challenge to construct a HWME facing to conceptual design of complex products due to its complexity and the limitation of current supporting technologies.
The function and structure of a system are closely related to each other, so the system platform of dealing with complex problems, how to establish a reasonable structure of HWME is crucial to the capability of this system platform. In this paper, on the basis of the conceptual design of some complex product, we make research on the problem solving process and solving method of HWME from the angle of problem solving. Aiming at providing supporting technologies for HWME system applied for the conceptual design of complex product, this research includes the modeling of problem solving process of complex product, problem decomposition and allocation, problem solving and the theory and methods of multi-objective optimization technique.
At first, based on meta-synthesis theory, we make a deep research on problem solving process and solving method of conceptual design of complex product. Scholars at home and abroad have adopted evolutionary computation to solve complex decision problems of economy and society. To use their ideas for reference, we propose a mathematical description of problem solving process in HWME and establish a HWME problem solving process modeling based on evolutionary computation.
Problem decomposition is the basis of any complex problem solving. So in the process of conceptual design of complex product using HWME, the decomposition of this problem should be made firstly. Nowadays, many researchers have made research on techniques of problem decomposition involved in conceptual design of complex product, but they still didn't find a universal method. In this paper, based on the analysis of the particularity of problem decomposition of complex systematic problem in HWME, we propose a method of problem decomposition on the basis of the research of expert group. And based on interactive genetic algorithm and the characteristic of design structure matrix, we propose an intelligent method of complex systematic problem decomposition to solve the decomposition in general. Practice shows that using this method can avoid modification in large scale and improve the searching efficiency in problem-structuring of complex system analysis.
In the process of conceptual design of complex product, with the increase of scale and complexity of the problem to be solved, a key subject to be solved in the problem solving process of HWME is how to harmonize subjects, how to dispatch the resources to make the complex problem solving procedure more reasonable and ordered. The existing complex problem solving procedure in HWME is a kind of empirical workflow standard. As a human-computer cooperative system, HWME need intelligent support from computer. Based on the analysis on the similarity between complex problem solving process in HWME and ant colony algorithm, we make corresponding mathematical model and propose the dynamic allocation of task in complex problem solving and design the ant colony algorithm suitable for solving the problem which achieves good result.
The key task of complex product conceptual design is to form a design of the product through generation and choice of concept. In HWME, there is a challenge of how to get the most satisfying and optimum solution to improve solving efficiency during the process of finding conceptual design of complex product after the expert group discussion in HWME. From the angle of complex problem solving and based on current artificial intelligent technology, we propose a group discussion method based on improved interactive genetic algorithm (IGA) model. Improved interactive genetic algorithm gives full play to the advantage of human and computer and provides a effective way to make complex product conceptual design and to solve such difficulties in HWME as the manipulability of man-machine cooperation and the effective conversion from qualitative to quantitative. Compared to the traditional method of opinion consensus and thought convergence in HWME, it is more effective and easier to achieve agreement in expert group. And it confirms the reality of discussion online in HWME and can do repeated circular argument conveniently.
During the process of forming product design of complex product conceptual design, the designers are supposed to think fully about various constraint conditions and design objective and meet the problem of making multi objectives in given feasible region as best as possible. In this paper, we propose a new multi-objective optimization method for complex product conceptual design based on the combination of co-evolutionary algorithm and interactive genetic algorithm. We can form a multi-objective plan for complex product conceptual design through the cooperation of population of co-evolutionary algorithm. And during the evaluation process of the design, we can integrate designers'subjective preference, intuition and experience and creative knowledge into the formation of design through IGA to combine man and computer tightly. We take the design of functions of mobile phone products as an example to prove the high effectiveness of this method for complex product conceptual design related to multi-objective and human-computer interaction.
引文
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