A dual-interval vertex analysis method and its application to environmental decision making under uncertainty

In this study, a dual-interval vertex analysis (DIVA) method is developed, through incorporating the vertex method within an interval-parameter programming framework. The developed DIVA method can tackle uncertainties presented as dual intervals that exist in the objective function and the left- and right-hand sides of the modeling constraints. An interactive algorithm and a vertex analysis approach are proposed for solving the DIVA model. Solutions under an associated [alpha]-cut level can be generated by solving a series of deterministic submodels. They can help quantify relationships between the objective function value and the membership grade, which is meaningful for supporting in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system optimality and reliability. A management problem in terms of regional air pollution control is studied to illustrate applicability of the proposed approach. The results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers to identify desired pollution-abatement strategies with minimized costs and maximized environmental efficiencies.Air quality Decision making Dual interval Environment Fuzzy programming Optimization Vertex analysis Uncertainty

Uncertain design optimization of automobile structures: A survey

In real life, there are a lot of uncertainties in engineering structure design, and the potential uncertainties will have an important impact on the structural performance responses. Therefore, it is of great significance to consider the uncertainty in the initial stage of structural design to improve product performance. The consensus can be reached that the mechanical structure obtained by the reliability and robustness design optimization method considering uncertainty not only has low failure risk but also has highly stable performance. As a large mechanical system, the uncertainty design optimization of key vehicle structural performances is particularly important. This survey mainly discusses the current situation of the uncertain design optimization framework of automobile structures, and successively summarizes the uncertain design optimization of key automobile structures, uncertainty analysis methods, and multi-objective iterative optimization models. The uncertainty analysis method in the design optimization framework needs to consider the existing limited knowledge and limited test data. The importance of the interval model as a non-probabilistic model in the uncertainty analysis and optimization process is discussed. However, it should be noted that the interval model ignores the actual uncertainty distribution rule, which makes the design scheme still have some limitations. With the further improvement of design requirements, the efficiency, accuracy, and calculation cost of the entire design optimization framework of automobile structures need to be further improved iteratively. This survey will provide useful theoretical guidance for engineers and researchers in the automotive engineering field at the early stage of product development

Sustainable design of complex industrial and energy systems under uncertainty

Depletion of natural resources, environmental pressure, economic globalization, etc., demand seriously industrial organizations to ensure that their manufacturing be sustainable. On the other hand, the efforts of pursing sustainability also give raise to potential opportunities for improvements and collaborations among various types of industries. Owing to inherent complexity and uncertainty, however, sustainability problems of industrial and energy systems are always very difficult to deal with, which has made industrial practice mostly experience based. For existing research efforts on the study of industrial sustainability, although systems approaches have been applied in dealing with the challenge of system complexity, most of them are still lack in the ability of handling inherent uncertainty. To overcome this limit, there is a research need to develop a new generation of systems approaches by integrating techniques and methods for handling various types of uncertainties. To achieve this objective, this research introduced series of holistic methodologies for sustainable design and decision-making of industrial and energy systems. The introduced methodologies are developed in a systems point of view with the functional components involved in, namely, modeling, assessment, analysis, and decision-making. For different methodologies, the interval-parameter-based, fuzzy-logic-based, and Monte Carlo based methods are selected and applied respectively for handling various types of uncertainties involved, and the optimality of solutions is guaranteed by thorough search or system optimization. The proposed methods are generally applicable for any types of industrial systems, and their efficacy had been successfully demonstrated by the given case studies. Beyond that, a computational tool was designed, which provides functions on the industrial sustainability assessment and decision-making through several convenient and interactive steps of computer operation. This computational tool should be able to greatly facilitate the academic and industrial practices on the study of sustainability problems, and it is the first one available to the public