IP‐MRSO: An iterative posterior preference articulation method to multiple response surface optimization

One of the most important issues in multiple response surface optimization (MRSO) is obtaining a satisfactory “compromise” solution considering a decision maker (DM)'s preference information on the tradeoffs among multiple responses. A promising alternative to incorporate the DM's preference information into the problem is the posterior preference articulation approach, which first generates all (or most) of the nondominated solutions and then makes the DM select the best one from the set of nondominated solutions a posteriori. However, it has an inefficiency problem in that it generates an excessive number of nondominated solutions in which almost all are not used for the DM's selection. This paper proposes a new posterior method called “IP‐MRSO” to overcome the limitation of the existing posterior method. The proposed IP‐MRSO is illustrated through a well‐known MRSO case problem.     

A multiple criteria decision making approach for the assembly line balancing problem

In practice, measuring total profit for a given assembly line balancing (ALB) problem is an involved process that is sometimes impossible because of much uncertainty and unavailability of data. In this paper, ALB is formulated as a multiple criteria problem where several easily quantifiable criteria (objectives) and constraints are defined. Objective functions include number of stations, cycle time, and operations cost, all to be minimized. After a discussion of applications and an overview of multiple criteria decision making (MCDM) approaches for ALB, the MCDM-ALB problem is formulated. Basic definitions and properties of MCDM for ALB are outlined and then an interactive MCDM approach is developed for solving the MCDM-ALB problem. To solve the problem, the decision maker (DM) interactively responds to paired comparisons of multicriteria alternatives. Through a limited number of interactions with the DM, the most preferred alternative is obtained. Many unexplored alternatives are eliminated by using a one-dimensional multiple criteria search. To present the DM's preference, we use the most flexible and general class of utility functions; namely, either quasi-concave or quasi-convex utility functions. An example is solved and computational experiments are reported. It is demonstrated that the bicriteria ALB, cycle time versus number of stations, can be easily solved by using the developed procedure. For the case that there are different criteria, an improved goal programming is developed to solve the MCDM-ALB problem. The motivation for development of the method, based on a case study of a lamp-making plant of the General Electric Company, is discussed.     

An expert system using priorities for solving multiple-criteria facility layout problems

In this paper we develop an expert system for multiple-criteria facility layout problems. The facility layout problem is identified as an ill-structured problem; our approach for solving it is based on expert systems and multiple-criteria decision making (MCDM). The expert system interacts with the decision maker (DM), and reflects the DM's preferences in the selection of rules and priorities. The inference engine is a forward-chaining reasoning procedure which is discussed in detail. The approach consists of two parts: (a) construction of a layout based on a set of rules and restrictions, and (b) improvement of the layout based on interaction with decision maker. The MCDM expert system approach considers and incorporates the multiple criteria in these two parts as follows. In (a) it uses priorities on the selection of rules, adjacency of departments, and departments for construction purposes. In (b) it uses different objectives such as materials handling cost, flexibility, and materials handling time for paired comparison of generated layouts for improvement purposes. Some experiments with the developed computer package are reported and an example is solved.     

OPTIMAL OPERATION OF MULTIQUALITY WATER SUPPLY SYSTEMS-III: THE Q-C-H MODEL

A new technique for optimal operation of multiquality water supply systems is proposed. The technique, which is known as a Q-C-H (flow-quality-head) model, combines previously developed Sow-quality (Q-C) and flow-head (Q-H) models for optimal operation of water supply systems. The decision variables in the model are the operation of treatment plants, pumps and valves. The model minimizes the cost of water at sources, treatment, energy, and loss of agricultural yield when water quality is low. The model uses an iterative modified projected gradient method combined with the Complex method. As in the Q-C and Q-H models, the solution method is based on decomposition, dis-aggregation/aggregation approach, involving internal and external optimization. The decision variables of the external model are the flows in the loops of the network and the removal ratios at the treatment plants. The operation of the pumps and valves are the decision variables of the internal model. The method is demonstrated by application to an example problem.     

Visualization support to better comprehend and improve decision tree classification modelling process: a survey and appraisal

Data mining (DM) can be defined as the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data. Modelling is the crucial step where DM algorithms are applied in order to extract data patterns. In order for domain experts, who play significant roles in DM process, to make the most efficient and effective use of DM tools, these tools must incorporate appropriate visualization to facilitate the process of modelling. Yet, unfortunately, study of how visualization should be designed, particularly what components should be included and how to present them, has been rather limited. This paper surveys the current state of art in application of visualization techniques to better comprehend and improve the decision trees modelling process in three modes: visualization of tree models, visualization of model evaluation and visual interactive tree construction. A number of issues that have been overlooked and areas that need to be improved are identified through reviewing a collection of related research and examining six current DM softwares in terms of their design of a few important features in each mode of the visualization support to decision trees classification modelling. Although this article focuses on decision trees classification modelling, guidelines derived from this study can be beneficial to other modelling techniques as well. At the end of the paper, a desirable design of visualization support to DM modelling is proposed with a conceptual model.     

An extensive simplex method for mapping global feasibility

Understanding the global feasibility of engineering decision-making problems is fundamental to the synthesis of rational engineering decisions. An Extensive Simplex Method is presented to solve the global feasibility for a linear decision model relating multiple decision variables to multiple performance measures, and constrained by corresponding limits. The developed algorithm effectively traverses all extreme points in the feasible space and establishes the graph structure reflecting the active constraints and their connectivity. The algorithm demarcates basic and nonbasic variables at each extreme point, which is exploited to traverse the active constraints and merge the degenerate extreme points. Finally, a random model generator is presented with the capability to control the matrix sparseness and the model degeneracy for an arbitrary number of decision variables and performance measures. The results indicate that all these model properties are significant factors which affect the total number of extreme points, their connected graph, and the global feasibility.     

Pareto navigator for interactive nonlinear multiobjective optimization

We describe a new interactive learning-oriented method called Pareto navigator for nonlinear multiobjective optimization. In the method, first a polyhedral approximation of the Pareto optimal set is formed in the objective function space using a relatively small set of Pareto optimal solutions representing the Pareto optimal set. Then the decision maker can navigate around the polyhedral approximation and direct the search for promising regions where the most preferred solution could be located. In this way, the decision maker can learn about the interdependencies between the conflicting objectives and possibly adjust one’s preferences. Once an interesting region has been identified, the polyhedral approximation can be made more accurate in that region or the decision maker can ask for the closest counterpart in the actual Pareto optimal set. If desired, (s)he can continue with another interactive method from the solution obtained. Pareto navigator can be seen as a nonlinear extension of the linear Pareto race method. After the representative set of Pareto optimal solutions has been generated, Pareto navigator is computationally efficient because the computations are performed in the polyhedral approximation and for that reason function evaluations of the actual objective functions are not needed. Thus, the method is well suited especially for problems with computationally costly functions. Furthermore, thanks to the visualization technique used, the method is applicable also for problems with three or more objective functions, and in fact it is best suited for such problems. After introducing the method in more detail, we illustrate it and the underlying ideas with an example.     

A linguistic risk prioritization approach for failure mode and effects analysis: A case study of medical product development

Failure mode and effects analysis (FMEA) is a widely used risk management technique for identifying the potential failures from a system, design, or process and determining the most serious ones for risk reduction. Nonetheless, the traditional FMEA method has been criticized for having many deficiencies. Further, in the real world, FMEA team members are usually bounded rationality, and thus, their psychological behaviors should be considered. In response, this study presents a novel risk priority model for FMEA by using interval two‐tuple linguistic variables and an integrated multicriteria decision‐making (MCDM) method. The interval two‐tuple linguistic variables are used to capture FMEA team members' diverse assessments on the risk of failure modes and the weights of risk factors. An integrated MCDM method based on regret theory and TODIM (an acronym in Portuguese for interactive MCDM) is developed to prioritize failure modes taking experts' psychological behaviors into account. Finally, an illustrative example regarding medical product development is included to verify the feasibility and effectiveness of the proposed FMEA. By comparing with other existing methods, the proposed linguistic FMEA approach is shown to be more advantageous in ranking failure modes under the uncertain and complex environment.     

Classifier-guided sampling for discrete variable,discontinuous design space exploration: Convergence and computational performance

A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS method uses a Bayesian network classifier for predicting the performance of new designs based on a set of known observations or training points. Unlike most metamodelling techniques, however, the classifier assigns a categorical class label to a new design, rather than predicting the resulting response in continuous space, and thereby accommodates non-differentiable and discontinuous functions of discrete or categorical variables. The CGS method uses these classifiers to guide a population-based sampling process towards combinations of discrete and/or continuous variable values with a high probability of yielding preferred performance. Accordingly, the CGS method is appropriate for discrete/discontinuous design problems that are ill suited for conventional metamodelling techniques and too computationally expensive to be solved by population-based algorithms alone. The rates of convergence and computational properties of the CGS method are investigated when applied to a set of discrete variable optimization problems. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, compared with genetic algorithms.     

Multiresponse optimization of a multistage manufacturing process using a patient rule induction method

Most manufacturing industries produce products through a series of sequential stages, known as a multistage process. In a multistage process, each stage affects the stage that follows, and the process often has multiple response variables. In this paper, we suggest a new procedure for optimizing a multistage process with multiple response variables. Our method searches for an optimal setting of input variables directly from operational data according to a patient rule induction method (PRIM) to maximize a desirability function, to which multiple response variables are converted. The proposed method is explained by a step-by-step procedure using a steel manufacturing process as an example. The results of the steel manufacturing process optimization show that the proposed method finds the optimal settings of input variables and outperforms the other PRIM-based methods.     

房车造型设计关系模型研究

为了提高房车造型与用户预期的匹配度,降低设计成本,运用感性工程学思想,将产品造型和用户感性评价转变为两个可测的变量,并使用多元回归分析法在两者之间建立起一个数学表达式模型,提出了使用圆柱面坐标系建立房车造型要素认知空间的方法。这种方法是对感性工程学思想手段的改良和创新,能为决策者预测市场对房车的反应并辅助设计师在概念生成阶段的造型构思。     

Yield surface simulation for partially recrystallized aluminum polycrystals on the basis of spatially discrete data

The paper presents simulations of the yield surface evolution of plastically deformed aluminum polycrystals during recrystallization. The yield surfaces are calculated using a viscoplastic Taylor–Bishop–Hill strain rate polycrystal homogenization method. The input data for the yield surface calculations are the crystal orientations, their volume fractions, and their shear stresses. While the crystal orientations determine the kinematic portion of the yield surface the threshold shear stress of each individual orientation determines the kinetic portion of the yield surface. The input data for the homogenization calculations are generated through a spatially discrete simulation, where crystal deformation and primary static partial recrystallization are simulated by coupling a viscoplastic crystal plasticity finite element model with a cellular automaton. The crystal plasticity finite element model accounts for crystallographic slip and for crystal rotation during plastic deformation using space and time as independent variables and the crystal orientation and the accumulated slip as dependent variables. The cellular automaton uses a switching rule which is formulated as a probabilistic analogue of Turnbull's rate equation for the motion of grain boundaries. The actual decision about a switching event is made using a simple-sampling Monte Carlo step. The automaton uses space and time as independent variables and the crystal orientation and a stored energy measure as dependent variables. The kinetics produced by the switching algorithm are scaled through grain boundary mobility and driving force data. The crystallographic texture and the orientation-dependent resistance to shear are for each interpolation point extracted after each time step during recrystallization. The data serve as input for the calculation of discrete yield surfaces.     

Integrated model for supplier selection and negotiation in a make-to-order environment

The paper deals with the development of an integrated supplier selection and negotiation process for multiple parts/materials procurement. The main objective is to integrate decisions in the internal supply chain of a make-to-order manufacturer. Two main decisions during the negotiation process are considered: (1) the manufacturing planning decision responsible for determining the production schedule and fabrication lot size and (2) the supplier selection decision concerning which suppliers are selected for company business and the order volume allocated to each selected supplier. The model is designed to support the negotiation process by generating a set of effective alternatives in each negotiation period. Its structure is multi-objective and non-linear. The combination of the interactive weighted Tchebycheff method and Benders decomposition method is applied to generate a set of effective alternatives to support the decision-maker in each negotiation period.     

The Design of Economic Acceptance Sampling Plans for a Mixture of Variables and Attributes

A method is developed for determining economical acceptance sampling plans where the characteristics of interest are a mixture of variables and attributes. The method uses a model which has been developed to represent the total expected cost per lot of exercising acceptance sampling. An optimum plan is found by minimizing the expected cost model with respect to the decision variables which are the sample sizes and control limits on the sample means for variables and the sample sizes and acceptance numbers for attributes. Optimization is accomplished using the pattern search.     

Identifying stakeholders’ roles and relevant project documents for 4D-based collaborative decision making

To fully understand and effectively implement 4D building information modelling (BIM) models and methods, we need to develop a precise knowledge of which project digital documents should be used and how they influence the decision-making (DM) process. This article studies the convergence between uses of 4D BIM and digital project documents. We hypothesize that a clear visualization of the construction simulation through a 4D model is a useful source of information and a support for DM at collaborative meetings. Through this research, we continue to progress toward a new 4D-based collective decision device, so these elements will contribute to propose 4D BIM as DM support on architecture engineering construction (AEC) projects. Further, the present research will be complemented by results from questionnaires given at a later research stage. The article presents a brief review of BIM context to consider ways of fostering the implementation of all 4D BIM uses (not only visualization). It then introduces a proposition for 4D BIM uses implementation by the project development phase. It concludes by summarizing stakeholders’ roles and documents relevant to 4D BIM uses.     

An integrated probabilistic risk analysis decision support methodology for systems with multiple state variables

Probabilistic risk analysis (PRA) methods have been proven to be valuable in risk and reliability analysis. However, a weak link seems to exist between methods for analysing risks and those for making rational decisions. The integrated decision support system (IDSS) methodology presented in this paper attempts to address this issue in a practical manner. In consists of three phases: a PRA phase, a risk sensitivity analysis (SA) phase and an optimisation phase, which are implemented through an integrated computer software system. In the risk analysis phase the problem is analysed by the Boolean representation method (BRM), a PRA method that can deal with systems with multiple state variables and feedback loops. In the second phase the results obtained from the BRM are utilised directly to perform importance and risk SA. In the third phase, the problem is formulated as a multiple objective decision making problem in the form of multiple objective reliability optimisation. An industrial example is included. The resultant solutions of a five objective reliability optimisation are presented, on the basis of which rational decision making can be explored.     

小样本下结构系统可靠度分析方法的研究

结构随机变量的空间分为可靠空间和失效空间。采用Monte Carlo法中的自适应抽样技巧可以较高效率地抽取出失效空间中的样本点。然后统计出可靠空间和失效空间的分布参数。最终采用判别分析的思想重构出结构的功能函数。由此提出一种小样本下结构系统可靠度分析的新方法。该方法仅以样本点的状态即可靠或失效来重构功能函数,具有广泛性。数值算例表明该方法在样本数量不多的情况下仍具有较高的精度。     

A MULTIOBJECTIVE INTERACTIVE SEQUENTIAL HYBRID OPTIMIZATION TECHNIQUE FOR DESIGN DECISION MAKING

The paper proposes an interactive method for obtaining a solution to a multiple objective design decision making problem. The focus is on generating Pareto solutions including those that are in the non-convex region, and are desirable to obtain in an engineering design context. After the generation of a small subset of the Pareto solutions, the designer's feedback is elicited in order to eliminate part of the subset. The process is repeated until il iteratively narrows down the Pareto solution set to a size small enough so that the designer is able to easily select a final solution. The advantage of this approach is that the designer can view a few sample points from the Pareto set before zooming into the region preferred and without expending computation time in generating a complete Pareto set. The process has been demonstrated with the help of an example, the design of a fleet of ships, that has mixed-discrete variables and hence a genetic algorithm is used as the optimizer.     

Feature extraction and classification models for high‐dimensional profile data

As manufacturing transitions to real‐time sensing, it becomes more important to handle multiple, high‐dimensional (non‐stationary) time series that generate thousands of measurements for each batch. Predictive models are often challenged by such high‐dimensional data and it is important to reduce the dimensionality for better performance. With thousands of measurements, even wavelet coefficients do not reduce the dimensionality sufficiently. We propose a two‐stage method that uses energy statistics from a discrete wavelet transform to identify process variables and appropriate resolutions of wavelet coefficients in an initial (screening) model. Variable importance scores from a modern random forest classifier are exploited in this stage. Coefficients that correspond to the identified variables and resolutions are then selected for a second‐stage predictive model. The approach is shown to provide good performance, along with interpretable results, in an example where multiple time series are used to indicate the need for preventive maintenance. In general, the two‐stage approach can handle high dimensionality and still provide interpretable features linked to the relevant process variables and wavelet resolutions that can be used for further analysis. Copyright © 2011 John Wiley & Sons, Ltd.     

Multi-criteria decision aid approach for the selection of the best compromise management scheme for ELVs: the case of Cyprus

Each alternative scheme for treating a vehicle at its end of life has its own consequences from a social, environmental, economic and technical point of view. Furthermore, the criteria used to determine these consequences are often contradictory and not equally important. In the presence of multiple conflicting criteria, an optimal alternative scheme never exists. A multiple-criteria decision aid (MCDA) method to aid the Decision Maker (DM) in selecting the best compromise scheme for the management of End-of-Life Vehicles (ELVs) is presented in this paper. The constitution of a set of alternatives schemes, the selection of a list of relevant criteria to evaluate these alternative schemes and the choice of an appropriate management system are also analyzed in this framework. The proposed procedure relies on the PROMETHEE method which belongs to the well-known family of multiple criteria outranking methods. For this purpose, level, linear and Gaussian functions are used as preference functions.