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1.
The collaborative design of a complicated mechanical product often involves conflicting multidisciplinary objectives, thus one key problem is conflict resolution and coordination among the different disciplines. Since the characteristics such as cooperative competition, professional dependence, compromise, overall utility and so on exist in multidisciplinary collaborative design (MCD), an effective way to gradually eliminate the conflicts among the multiple disciplines and reach an agreement is the negotiation by which a compromise solution that satisfies all parties is got. By comprehensively analyzing the characteristics in MCD and considering the benefit equilibrium among discipline individuals and team, a negotiation strategy is presented, which maximize the union satisfaction degree of system overall objective under the premise of ensuring the higher satisfaction degree level of each discipline’s local objective. A design action of a discipline is abstractly expressed as a concession in the negotiation strategy, and a negotiation model used for MCD is generated by establishing the relation between concession and satisfaction degree. By the relation between satisfaction degree and objective function, the mapping relationship between satisfaction degree domain and physical domain is built to get the design solution. A negotiation process is planned, and a negotiation system framework is designed to support the negotiation among multiple disciplines and assist the different disciplines rapidly reach a consistent compromise solution. A design example of automotive friction clutch is given to illustrate the proposed method.  相似文献   

2.
Collaborative design is a common practice in modern product development. Companies specializing in different disciplines, which are often geographically separated, work together to develop solutions for the benefit of overall design. In such inter-organizational collaboration, participants hiding individual skills and design rationales is highly desirable. To fulfill this requirement, this study proposes a negotiation mechanism based on a price schedules decomposition algorithm originally developed in economics. The mechanism searches for globally optimal designs, where no participant is necessary to own full knowledge of the entire design space. This paper also applies multi-agent system technologies to realize a secure environment for automating distributed collaborative design. A test scenario of distributed tolerance allocation in assembly design validates the proposed mechanism.  相似文献   

3.
4.
To create a more realistic distributed collaborative environment, three Texas universities - Texas Christian University, the University of Texas at Arlington and Texas Tech University - developed an innovative method for teaching collaborative software development in distributed multidisciplinary environments.  相似文献   

5.
This paper presents a numerical investigation of the non-hierarchical formulation of Analytical Target Cascading (ATC) for coordinating distributed multidisciplinary design optimization (MDO) problems. Since the computational cost of the analyses can be high and/or asymmetric, it is beneficial to understand the impact of the number of ATC iterations required for coordination and the number of iterations required for disciplinary feasibility on the quality of the obtained MDO solution. At each “outer” ATC iteration, the disciplinary optimization subproblems are solved for a predefined maximum number of “inner” loop iterations. The numerical experiments consider different numbers of maximum outer iterations while keeping the total computational budget of analyses constant. Solution quality is quantified by optimality (objective function value) and consistency (violation of coordination-related consistency constraints). Since MDO problems are typically simulation-based (and often blackbox) problems, we compare implementations of the mesh-adaptive direct search optimization algorithm (a derivative-free method with convergence properties) to the gradient-based interior-point algorithm implementation of the popular Matlab optimization toolbox. The impact of the values of two parameters involved in the alternating directions updating scheme of the augmented Lagrangian penalty functions (aka method of multipliers) on solution quality is also investigated. Numerical results are provided for a variety of MDO test problems. The results indicate consistently that a balanced modest number of outer and inner iterations is more effective; moreover, there seems to be a specific combination of parameter value ranges that yield better results.  相似文献   

6.
Structural and Multidisciplinary Optimization - We present a novel technique for implementing non-hierarchical analytical target cascading to coordinate distributed multidisciplinary design...  相似文献   

7.
A novel negotiation algorithm of swarm global exploration, based on Fuzzy Matter Element Particle Swarm Optimization (FMEPSO), is presented to resolve the conflicts in complex product distributed collaborative creative design systems. Firstly, an extensive formalized expression of conflicts in the fuzzy matter element model is given, next, the fuzzy matter element optimization method (FMEOM) is used to change the multi-object negotiation problems to single-object negotiation problems, and the regularized correlation function is regarded as the fitness function judging the stand and fall of particle; Then, in the implementation process of the PSO algorithm, the mutation mechanics is introduced to mutate the inactive particle and the particle with the smallest fitness according to mutation probability, which can not only effectively solve the premature convergence problem, but also significantly speed up the convergence; Finally, an example of the parameter optimization design in a power train is used to show the validity of this algorithm.  相似文献   

8.
Set-based design is a design approach where feasible regions for the design variables are determined from different disciplines, with the goal of locating and working with the areas of feasible overlap. During the process the constraints are adjusted in order to accommodate conflicting requirements between disciplines. The main objective of set-based design is to narrow the design space, while delaying the pursuit of a single point design as much as possible. This process avoids finalizing decisions early and allows for flexibility in dealing with requirement creep. This paper presents the development and application of a new multidisciplinary design optimization (MDO) algorithm inspired by the principles of set-based design. The new MDO algorithm was developed with the core concept of describing the design using sets to incorporate features of set-based design and achieve greater flexibility than with a single-point optimization. The MDO algorithm was applied to a ship design problem and the ship design application demonstrated the value of utilizing set-based design as a space-reducing technique before approaching the problem with a point-based optimization. Furthermore, incorporating flexibility in the constraints allowed the optimization to handle a problem with very strict constraints in a rational manner and minimize the necessary constraint violation.  相似文献   

9.
《Applied Soft Computing》2008,8(2):1093-1104
Although a considerable amount of efforts has been devoted to developing optimum negotiation for dynamic scheduling, most of them are inappropriate for the non-cooperative, self-interested participants in a distributed project for practical purpose. In this paper, an agent-based approach with a mutual influencing, many-issue, one-to-many-party, compensatory negotiation model is proposed. In the model, the activity agents possess various negotiation tactics and strategies formed by respective self-interested owner's subjective preference, aim to find the contracts of schedule adjustment mutually acceptable to respective participant's acquaintance while encountering conflicts over rescheduling settlement. In order to find the fitting negotiation strategies that are optimally adapted for each activity agent, an evolutionary computation approach that encodes the parameters of tactics and strategies of an agent as genes in GAs is also addressed. In the final, a prototype with a case discussed in researches is evaluated to validate the feasibility and applicability of the model, and some characteristics and future works are also exhibited.  相似文献   

10.
The potential of Multidisciplinary Design Optimization (MDO) is not sufficiently exploited in current building design practice. I argue that this field of engineering requires a special setup of the optimization model that considers the uniqueness of buildings, and allows the designer to interact with the optimization in order to assess qualities of aesthetics, expression, and building function. For this reason, the approach applies a performance optimization based on resource consumption extended by preference criteria. Furthermore, building design-specific components serve for the decomposition and an interactive way of working. The component scheme follows the Industry Foundation Classes (IFC) as a common Building Information Model (BIM) standard in order to allow a seamless integration into an interactive CAD working process in the future. A representative case study dealing with a frame-based hall design serves to illustrate these considerations. An N-Square diagram or Design Structure Matrix (DSM) represents the system of components with the disciplinary dependencies and workflow of the analysis. The application of a Multiobjective Genetic Algorithm (MOGA) leads to demonstrable results.  相似文献   

11.
There are many applications in aeronautical/aerospace engineering where some values of the design parameters/states cannot be provided or determined accurately. These values can be related to the geometry (wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi-Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the first case considers robust multi-objective (single-disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero-structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design.  相似文献   

12.
Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g. the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.). This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance. Copyright © 1999 John Wiley & Sons, Ltd.  相似文献   

13.
基于Pareto的多目标进化免疫算法   总被引:2,自引:1,他引:1  
提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中;同时引入Parzen 窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动态更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分布情况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比,PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。  相似文献   

14.
The area of Multiparametric Optimization (MPO) solves problems that contain unknown problem data represented by parameters. The solutions map parameter values to optimal design and objective function values. In this paper, for the first time, MPO techniques are applied to improve and advance Multidisciplinary Design Optimization (MDO) to solve engineering problems with parameters. A multiparametric subgradient algorithm is proposed and applied to two MDO methods: Analytical Target Cascading (ATC) and Network Target Coordination (NTC). Numerical results on test problems show the proposed parametric ATC and NTC methods effectively solve parametric MDO problems and provide useful insights to designers. In addition, a novel Two-Stage ATC method is proposed to solve nonparametric MDO problems. In this new approach elements of the subproblems are treated as parameters and optimal design functions are constructed for each one. When the ATC loop is engaged, steps involving the lengthy optimization of subproblems are replaced with simple function evaluations.  相似文献   

15.
Optimal design of launch vehicles is a complex problem which requires the use of specific techniques called Multidisciplinary Design Optimization (MDO) methods. MDO methodologies are applied in various domains and are an interesting strategy to solve such an optimization problem. This paper surveys the different MDO methods and their applications to launch vehicle design. The paper is focused on the analysis of the launch vehicle design problem and brings out the advantages and the drawbacks of the main MDO methods in this specific problem. Some characteristics such as the robustness, the calculation costs, the flexibility, the convergence speed or the implementation difficulty are considered in order to determine the methods which are the most appropriate in the launch vehicle design framework. From this analysis, several ways of improvement of the MDO methods are proposed to take into account the specificities of the launch vehicle design problem in order to improve the efficiency of the optimization process.  相似文献   

16.
This paper addresses the integration issues at the preliminary design stage in order to support analysis and decision-making while considering a design from the viewpoint of different disciplines. The paper describes a research project for investigating and designing a framework for intelligent linkage between design drawings and other information system environments, providing access to both external databases and design methods at the preliminary design stage. Accessing such information at this stage will allow designers to carry out the rapid evaluation of design alternatives, analysis and decision-making in a multi-disciplinary, multi-agent design environment. The objectives of the research are outlined, the methodology is discussed and the first application results are demonstrated.  相似文献   

17.
Multidisciplinary design optimization (MDO) has become essential for solving the complex engineering design problems. The most common approach is to “divide and conquer” the MDO problem, that is, to decompose the complex problem into several sub-problems and to collect the local solutions to give a new design point for the original problem. In 1990s, researchers have developed some decomposition strategies to find or synthesize the optimal model of the optimization structure in order to evenly distribute the computational workloads to multiple processors. Several MDO methods, such as Collaborative Optimization (CO) and Analytical Target Cascading (ATC), were then developed to solve the decomposed sub-problems and coordinate the coupling variables among them to find the optimal solution. However, both the synthesis of the decomposition structure and the coordination of the coupling variables require additional function evaluations, in terms of evaluating the functional dependency between each sub-problem and determining the proper weighting coefficients between each coupling functions respectively. In this paper, a new divide-and-conquer strategy, Gradient-based Transformation Method (GTM), is proposed to overcome the challenges in structure synthesis and variable coordination. The proposed method first decomposes the MDO problem into several sub-systems and distributes one constraint from the original problem to each sub-system without evaluating the dependency between each sub-system. Each sub-system is then transformed to the single-variate coordinate along the gradient direction of the constraint. The total function evaluations equal the number of constraints times the number of variables plus one in every iteration. Due to the monotonicity characteristics of the transformed sub-problems, they are efficiently solved by Monotonicity Analyses without any additional function evaluations. Two coordination principles are proposed to determine the significances of the responses based on the feasibility and activity conditions of every sub-problem and to find the new design point at the average point of the most significant responses. The coordination principles are capable of finding the optimal solution in the convex feasible space bounded by the linearized sub-system constraints without additional function evaluations. The optimization processes continue until the convergence criterion is satisfied. The numerical examples show that the proposed methodology is capable of effectively and efficiently finding the optimal solutions of MDO problems.  相似文献   

18.
贺群  程格  安军辉  戴光明  彭雷 《计算机科学》2012,39(103):489-492
为了克服部分多目标进化算法中容易出现退化与早熟,造成收敛速度过慢的不足,结合精英保留策略、基于近部规则的环境选择以及免疫克隆算法中的比例克隆等思想,提出一种基于Pareto的多目标克隆进化算法NPCA(Non-dominated Pareto Clonal Algorithm)。通过部分多目标优化测试函数ZDT和DTLZ对算法进行了性能测试,验证了该算法能获得分布更加均匀的Parcto前沿,解的收敛性明显优于典型的多目标进化算法。  相似文献   

19.
Simultaneously running multiple projects are quite common in industries. These projects require local (always available to the concerned project) and global (shared among the projects) resources that are available in limited quantity. The limited availability of the global resources coupled with compelling schedule requirements at different projects leads to resource conflicts among projects. Effectively resolving these resource conflicts is a challenging task for practicing managers. This paper proposes a novel distributed multi-agent system using auctions based negotiation (DMAS/ABN) approach for resolving the resource conflicts and allocating multiple different types of shared resources amongst multiple competing projects. The existing multi-agent system (MAS) using auction makes use of exact methods (e.g. dynamic programming relaxation) for solving winner determination problem to resolve resource conflicts and allocation of single unit of only one type of shared resource. Consequently these methods fail to converge for some multi-project instances and unsuitable for real life large problems. In this paper the multi-unit combinatorial auction is proposed and winner determination problem is solved by efficient new heuristic.The proposed approach can solve complex large-sized multi-project instances without any limiting assumptions regarding the number of activities, shared resources or the number of projects. Additionally our approach further allows to random project release-time of projects which arrives dynamically over the planning horizon. The DMAS/ABN is tested on standard set of 140 problem instances. The results obtained are benchmarked against the three state-of-the-art decentralized algorithms and two existing centralized methods. For 82 of 140 instances DMAS/ABN found new best solutions with respect to average project delay (APD) and produced schedules on an average 16.79% (with maximum 57.09%) lower APD than all the five methods for solving the same class of problems.  相似文献   

20.
Assumptions form the base on which the design is founded. Design starts by taking a concept based on assumptions of what may be wanted and how those needs may be satisfied. Later, this concept is decomposed into sub-problems that, in turn, may be decomposed in smaller sub-problems. This decomposition is based on a set of assumptions of how the sub-problems interact and how they may be solved. In addition, this decomposition requires the participation of multiple actors from multiple disciplines who must coordinate and communicate their assumptions on how to solve the sub-problems. Capturing the assumptions as they are presented, and making them available to the remaining phases of the design process would enhance coordination, integration and negotiation during the process. However, there are some problems with the capture and use of design assumptions. In current practice, certain kinds of vital information—usually unstructured and informal, often having to do with why certain actions are taken (design rationale and intent)—are often lost in large projects. Usually the information that is related to the relationship between the parts of an artifact and the context in which they are designed to work is lost. To solve these problems of capturing design assumptions, this paper recommends the use of the Design-Rationale-Intent Model (DRIM), a model that attaches the design rationale behind an artifact to the hierarchical decomposition of that artifact.  相似文献   

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