Hierarchical Choice Modeling to Support Complex Systems Design

2012 ◽  
pp. 205-233 ◽  
Author(s):  
Wei Chen ◽  
Christopher Hoyle ◽  
Henk Jan Wassenaar
Keyword(s):  
Complex Systems ◽  
Systems Design ◽  
Choice Modeling

Supporting the Complexity of Inquiring Organizations

2011 ◽  
pp. 109-132
Author(s):  
Dianne J. Hall ◽  
Yi Guo
Keyword(s):  
Complex Systems ◽  
Multiagent System ◽  
Support Systems ◽  
Systems Design ◽  
Agent Technology ◽  
Technological Support ◽  
Organization Support

This chapter examines the issue of technological support for inquiring organizations and suggests that the complexity of these organizations is best supported by a technology of equal complexity—that is, by agent technology. Agents and the complex systems in which they are active are ideal for supporting not only the activity of Churchman’s inquirers but also those components necessary to ensure an effective environment. Accordingly, a multiagent system to support inquiring organizations is introduced. By explaining agent technology in simple terms and by defining inquirers and other components as agents working within a multiagent system, this chapter demystifies agent technology, enables researchers to grasp the complexity of inquiring organization support systems, and provides the foundation for inquiring organization support systems design.

Heuristic algorithm for optimal redundancy allocation in complex systems

2019 ◽  
Vol 25 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Sudhanshu Aggarwal
Keyword(s):  
Complex Systems ◽  
Heuristic Algorithm ◽  
Communication Systems ◽  
Heuristic Algorithms ◽  
Real Life ◽  
Optimal Solution ◽  
Systems Design ◽  
Computational Time ◽  
Coherent System ◽  
Content Type

PurposeThe purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.Design/methodology/approachThe algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.FindingsThe proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.Research limitations/implicationsIn general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.Practical implicationsIt is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.Social implicationsReliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.Originality/valueThe utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

Model-Based Approaches to Human-Automation Systems Design

2012 ◽  
Author(s):  
Greg A. Jamieson ◽  
Jonas Andersson ◽  
Ann Bisantz ◽  
Asaf Degani ◽  
Morten Lind
Keyword(s):  
Complex Systems ◽  
Interaction Design ◽  
Systems Design ◽  
Limited Range ◽  
Design Activity ◽  
Panel Session ◽  
Automation Systems ◽  
Explicit Consideration ◽  
Design Challenges

Human-automation interaction in complex systems is common, yet design for this interaction is often conducted without explicit consideration of the role of the human operator. Fortunately, there are a number of modeling frameworks proposed for supporting this design activity. However, the frameworks are often adapted from other purposes, usually applied to a limited range of problems, sometimes not fully described in the open literature, and rarely critically reviewed in a manner acceptable to proponents and critics alike. The present paper introduces a panel session wherein these proponents (and reportedly one or two critics) can engage one another on several agreed questions about such frameworks. The goal is to aid non-aligned practitioners in choosing between alternative frameworks for their human-automation interaction design challenges.

Applying Evolutionary Computing to Complex Systems Design

2007 ◽  
Vol 37 (5) ◽  
pp. 770-779 ◽  
Author(s):  
Alistair Sutcliffe ◽  
Wei-Chung Chang ◽  
Richard S. Neville
Keyword(s):  
Complex Systems ◽  
Systems Design ◽  
Evolutionary Computing

Maximizing Flexibility for Complex Systems Design to Compensate Lack-of-Knowledge Uncertainty

2019 ◽  
Vol 5 (4) ◽  
Author(s):  
Marco Daub ◽  
Fabian Duddeck
Keyword(s):  
Complex Systems ◽  
Complete Solution ◽  
Solution Space ◽  
Systems Design ◽  
Rigid Wall ◽  
Total System ◽  
Independent Manner ◽  
Design Variables ◽  
Knowledge Uncertainty ◽  
Lack Of Knowledge

Abstract The consideration of uncertainty is especially important for the design of complex systems. Because of high complexity, the total system is normally divided into subsystems, which are treated in a hierarchical and ideally independent manner. In recent publications, e.g., (Zimmermann, M., and von Hoessle, J. E., 2013, “Computing Solution Spaces for Robust Design,” Int. J. Numer. Methods Eng., 94(3), pp. 290–307; Fender, J., Duddeck, F., and Zimmermann, M., 2017, “Direct Computation of Solution Spaces,” Struct. Multidiscip. Optim., 55(5), pp. 1787–1796), a decoupling strategy is realized via first the identification of the complete solution space (solutions not violating any design constraints) and second via derivation of a subset, a so-called box-shaped solution space, which allows for decoupling and therefore independent development of subsystems. By analyzing types of uncertainties occurring in early design stages, it becomes clear that especially lack-of-knowledge uncertainty dominates. Often, there is missing knowledge about overall manufacturing tolerances like limitations in production or subsystems are not even completely defined. Furthermore, flexibility is required to handle new requirements and shifting preferences concerning single subsystems arising later in the development. Hence, a set-based approach using intervals for design variables (i.e., interaction quantities between subsystems and the total system) is useful. Because in the published approaches, no uncertainty consideration was taken into account for the computation of these intervals, they can possibly have inappropriate size, i.e., being too narrow. The work presented here proposes to include these uncertainties related to design variables. This allows now to consider lack-of-knowledge uncertainty specific for early phase developments in the framework of complex systems design. An example taken from a standard crash load case (frontal impact against a rigid wall) illustrates the proposed methodology.

Sign in / Sign up

Export Citation Format

Share Document