An Interactive Process Capability-Based Approach to Multi-Response Surface Optimization

The decision maker's preference information on the importance and interaction of decision criteria can be explicitly described by the probabilistic interaction indices in the framework of the capacity based multicriteria decision analysis. In this paper, we first investigate some properties of the probabilistic interaction indices of the empty set, and propose the maximum and minimum empty set interaction principles based capacity identification methods, which can be considered as the comprehensive interaction trend preference information oriented capacity identification methods. Then, by introducing the deviation variables, the goal constraints, as well as the goal objective function, we give a new and more flexible approach to representing the decision maker's explicit preference information on the kind and degree of the interaction of any given combination of decision criteria as well as on the degree of the importance of any decision criterion, and construct the nonempty set interaction indices based capacity identification method, which can be considered as the detailed explicit preference information oriented identification method. Finally, two illustrative examples are respectively given to show the feasibility and applicability of the two kinds of methods. In addition, the comparison analysis between these two kinds of methods and some existing capacity identification methods are also mentioned.

In this paper, we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANNs), the method is called the Interactive FFANN Procedure. In the procedure, the decision maker articulates preference information over representative samples from the nondominated set either by assigning preference “values” to the sample solutions or by making pairwise comparisons in a fashion similar to that in the Analytic Hierarchy Process. With this information, a FFANN is trained to represent the decision maker's preference structure. Then, using the FFANN, an optimization problem is solved to search for improved solutions. An example is given to illustrate the Interactive FFANN Procedure. Also, the procedure is compared computationally with the Tchebycheff Method (Steuer and Choo [Steuer, R. E., E.-U. Choo. 1983. An interactive weighted Tchebycheff procedure for multiple objective programming. Math. Programming 26(1) 326–344.]). The computational results indicate that the Interactive FFANN Procedure produces good solutions and is robust with regard to the neural network architecture.

In reliability allocation, certain reliability values are assigned to subsystems and components to achieve the required system reliability. One big challenge in solving such reliability‐based design problems is how to handle the uncertain preferences of a decision maker on multiple attributes of interest. In this paper, we propose a new ordered weighted averaging (OWA) method based on an analytic hierarchy process to address the decision maker's uncertain preferences in reliability allocation. In the proposed OWA operator, a bi‐objective mathematical programming model considering both maximal entropy and minimal variance is transformed into a single‐objective mathematical programming model using an ideal‐point method. The maximum entropy minimal variance OWA operator takes full advantage of available information and avoids overestimating the decision maker's preferences. A detailed computational procedure is presented to facilitate the implementation of the proposed method in practice. An illustrative example about the powertrain of fuel cell vehicles is provided to demonstrate the effectiveness of this method in handling multiple attributes with uncertain preferences in reliability allocation. Copyright © 2015 John Wiley & Sons, Ltd.

Data envelopment analysis (DEA) model has been applied for evaluating bank branches and recognizing efficient and inefficient branches can help bank managers to provide appropriate strategies to improve the inefficient branches' performance. Conventional DEA models are based on the ‘black box’ approach. However, the process of providing services in banks is made up of interactive and interdependent processes. Additionally, some managers tend to incorporate their preferences in evaluation process. In this paper, best worst method (BWM) is used for incorporating decision maker (DM) preferences in two‐stage DEA model. First, BWM model is applied to obtain the weights of inputs, intermediate measures and outputs based on DM's judgement. Second, generated weights are imposed on two‐stage DEA model as additional constraints and a novel bi‐objective BWM‐two stage DEA model is introduced. Finally, the proposed bi‐objective BWM‐two stage DEA model is solved using min‐max approach. To illustrate the capability of proposed model, 45 Agricultural Bank (Agribank) branches in West Azerbaijan province of Iran are evaluated. The branches' processes are considered as two stages ‘production’ and ‘profitability’ and efficiency of branches are calculated in each stage. According to the efficiencies of each sub‐stage, branches are divided to four groups and recommendations are made for each group.

Multiple response surface optimization (MRSO) aims to determine a setting of input variables that simultaneously optimizes multiple responses. The process capability function (PCF) approach is an attractive alternative to MRSO because it considers both the mean and variance of the responses systematically through the process capability index. In MRSO, the responses are often in conflict. Thus, the preference information of a decision maker (DM) on the tradeoffs among the responses should be considered. However, the existing PCF methods are based on an unrealistic assumption that all DM preference information is given in advance. In this article, a posterior preference articulation procedure based on the PCF approach, called P-PCF, is proposed. P-PCF first generates nondominated solutions using the ε-constraint method and then allows the DM to select his/her preferred cluster or final solution using the K-means clustering method. The proposed method is illustrated using a well-known MRSO problem.

Consider a finite set of alternatives under risk which have multiple attributes. MARPI is an interactive computer-based procedure to find an efficient choice in the sense of linear expected utility. The choice is based on incomplete information about the decision maker's preferences which is elicited and processed in a sequential way. The information includes qualitative properties of the multivariate utility function such as monotonicity, risk aversion, and separability. Further, in case of an additively separable utility function, bounds on the scaling constants are elicited, and preferences (not necessarily indifferences) between sure amounts and lotteries are asked from the decision maker. The lotteries are Bernoulli lotteries generated by MARPI using special strategies. At every stage of the procedure the efficient set of alternatives is determined with respect to the information elicited so far.

Multi-layer interaction preference based multi-objective evolutionary algorithm through decomposition

To address problems with multiple conflicting objective functions while incorporating preference information from a decision maker (a domain expert), interactive evolutionary multiobjective optimization methods have been proposed and widely adopted. To systematically assess the performance of these methods, it is desirable to have indicators that not only rely on the Pareto dominance relation, where solutions with better objective function values are preferred, but also take the decision maker's preferences into account. In this way, indicator values can meaningfully reflect the decision maker's preferences and the quality of the solutions obtained. In this paper, we propose the first performance indicator that incorporates preference information in the form of two reference points, specifically, an aspiration point, consisting of aspiration levels that represent the decision maker's full satisfaction, and a reservation point, consisting of reservation levels that represent dissatisfaction. The new indicator, termed D-PHI, translates information about reservation and aspiration points into a transformed hypervolume measure that assesses the performance of a solution set. We introduce D-PHI with an extended set of guidelines for preference-based performance evaluation, discuss its theoretical properties, and demonstrate its behavior through empirical examples. Moreover, we define a complementary indicator (CI) based on a so-called achievement scalarizing function. CI offers additional insight into whether aspiration and reservation levels have been achieved, and its usefulness is demonstrated through examples.

Previous research has documented the lack of clear relationships between the value of information and characteristics of the decision problem. This paper shows that the intensity of the decision maker's preference toward the prior choice alternatives can be regarded as the primary determinant of information value. The value of an information source, measured in utility units, is maximal when the decision maker is indifferent vis-à-vis the prior alternatives, and it is lower as the preference for one alternative over the others gets stronger. This holds under quite general conditions, which are made explicit, and for any decision maker's utility function, wealth, and background risks. The maximum buying price of information follows the same pattern for linear utility and, with a restriction, exponential utility, and this may hold approximately for other types of utility functions. The result provides a general, concise, and intuitive explanation for seemingly ill-behaved variations in the value of information. It indicates that nonmonotonic, quasi-concave relationships should generally be expected between value of information and parameters of the decision problem.

We assume a multi-attribute decision making problem under uncertainty with partial information on the decision maker's preferences, by a vector utility function with two components and imprecision over their scaling constants. We propose an approximation set whose determination may be easier than the one of the utility efficient set and we consider an interactive procedure which uses such approximation to decision aid. We study some nesting and convergence properties based on the interactive reduction of the approximation set. Finally, we illustrate the procedure with a numerical example.

Preference modelling by estimating local utility functions for multiobjective optimization

This paper uses the fuzzy goal programming approach with different kinds of membership functions like linear, exponential, parabolic, hyperbolic and quadratic membership; for solving the multi-objective sustainable supply chain (MOSSC) problem under the intuitionistic fuzzy environment. The primary objective is to maximise the minimum value of the membership function so the preferred compromise solution can achieve to the MOSSC problem. By attaining this preferred compromise solution, the optimum order quantity allocation to each supplier can also be determined. Additionally, a situation even has been considered where the decision maker tries to control the optimum order quantity and search for another satisfactory solution, for this, he uses the membership function. A case study of the sustainable supply chain has been used to show the usefulness of the proposed work.

We consider the multiattribute decision making problem under risk with imprecise information on the decision maker's preferences, modelled by means of a vector utility function. We propose an interactive decision aid approach, which uses an idea of approximation to the utility efficient set and qualitative comparisons for the decision maker, to overcome the possible difficulty in generate it. An application to university selection illustrates the procedure.

Explainable Interactive Evolutionary Multiobjective Optimization

A novel interactive preferential evolutionary method for controller tuning in chemical processes