Elicitation of Preference Structure in Engineering Design

Engineering design processes, which inherently involve multiple, often conflicting criteria, can be broadly classified into synthesis and analysis processes. Multiple Criteria Decision Making addresses synthesis and analysis processes through multiple objective optimisation to generate sets of efficient design solutions (i.e. on Pareto surfaces) and multiple attribute decision making to analyse and select the most preferred design solution(s). MCDM, therefore, has been widely used in all fields of engineering design; for example it has been applied to such diverse areas as naval battle ships criteria analysis/selection and product appearance design. Given a list of design alternatives with multiple conflicting criteria, preferences often determine the final selection of a particular set of design alternative(s). Preferences may also be used to drive the design/design optimisation processes. Various methods have been proposed to model preference structure, for example simple weights, multiple attribute utility theory, pairwise comparison, etc. Preference structure is often non-linear, discontinuous and complex. An Artificial Neural Network (ANN) learning-based preference elicitation method is presented in this paper. ANNs efficiently model the non-linearity, complexity and discontinuity nature of any given preference structure. A case study is presented to illustrate the learning-based approach to preference structure elicitation.

A Decision-Making Model for Supplier Selection in Public Procurement

Public procurement is a multi-criteria decision-making (MCDM) problem that meets lexicographic preference to some extent. In this paper, a two-layer method that meets lexicographic preference selection was proposed to address the government procurement decision-making problem of multi experts in the fuzzy environment. The decision-making process of this model is divided into two steps. Firstly, attributes of multiple experts were collected according to their rights of speech, getting the comprehensive attribute preference order. Secondly, the program meeting lexicographic preference was decided based on the comprehensive attributes. Finally, the proposed model was applied to the firefighting cushion bidding case of Fire Station of A public security department. Results demonstrated that the proposed model is applicable to public procurement decision-making and can provide effective references to decision-making on government procurement

Multiple Attribute Decision Making Based on Cross-Evaluation with Uncertain Decision Parameters

Multiple attribute decision making (MADM) problem is one of the most common and popular research fields in the theory of decision science. A variety of methods have been proposed to deal with such problems. Nevertheless, many of them assumed that attribute weights are determined by different types of additional preference information which will result in subjective decision making. In order to solve such problems, in this paper, we propose a novel MADM approach based on cross-evaluation with uncertain parameters. Specifically, the proposed approach assumes that all attribute weights are uncertain. It can overcome the drawback in prior research that the alternatives’ ranking may be determined by a single attribute with an overestimated weight. In addition, the proposed method can also balance the mean and deviation of each alternative’s cross-evaluation score to guarantee the stability of evaluation. Then, this method is extended to a more generalized situation where the attribute values are also uncertain. Finally, we illustrate the applicability of the proposed method by revisiting two reported studies and by a case study on the selection of community service companies in the city of Hefei in China

Group consensus measurement in MADM with multiple preference formats

An approach is proposed for measuring the group consensus in multiple attribute decision-making (MADM) problems with experts’ various preference information on alternatives. In the approach, multiple decision-makers give their preference information on alternatives in different formats. The uniformities and aggregation process with fuzzy majority method are employed to obtain the social fuzzy preference relation on the alternatives. Accordingly, the ranking values of the alternatives are obtained based on the obtained individual expert’s fuzzy preference relation, and the social one. The group consensus can be measured based on the ranking values of the alternatives that are derived from the individual expert’s preference information and the social one. An example of selecting robots is presented as an illustration

New group decision making method in intuitionistic fuzzy setting based on TOPSIS

In multiple attribute group decision making, the weights of decision makers are very crucial to ranking results and have gained more and more attentions. A new approach to determining experts’ weights is proposed based on the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method in intuitionistic fuzzy setting. The weights determined by our method have two advantages: the evaluation value has a large weight if it is close to the positive ideal evaluation value and far from negative ideal evaluation values at the same time, otherwise it is assigned a small weight; experts have different weights for different attributes, which are more appropriate for real decision making problems since each expert has his/her own knowledge and expertise. The multiple attribute intuitionistic fuzzy group decision making algorithm has been proposed which is suitable for different situations about the attribute weight information, including the attribute weights are known exactly, partly known and unknown completely. A supplier selection problem and the evaluation of murals in a metro line are finally used to illustrate the feasibility, efficiency and practical advantages of the developed approaches

A method based on TOPSIS and distance measures for hesitant fuzzy multiple attribute decision making

The aim of this paper is to provide a methodology to hesitant fuzzy multiple attribute decision making using technique for order preference by similarity to ideal solution (TOPSIS) and distance measures. Firstly, the inadequacies of the existing hesitant fuzzy TOPSIS method are analyzed in detail. Then, based on the developed hesitant fuzzy ordered weighted averaging weighted aver-aging distance (HFOWAWAD) measure, a modified hesitant fuzzy TOPSIS, called HFOWAWAD-TOPSIS is introduced for hesitant fuzzy multiple attribute decision making problems. Moreover, the advantages and some special cases of the HFOWAWAD-TOPSIS are presented. Finally, a numerical example about energy policy selection is provided to illustrate the practicality and feasibility of the developed approach

SISTEM PENDUKUNG KEPUTUSAN UNTUK MEMILIH USAHA WARALABA MAKANAN MENGGUNAKAN METODE TOPSIS

Large number of franchises that will be selected as well as indicators of many criteria, it is necessary to build a decision support system that will help decide which franchise to choose. The model used in the decision support system is a Multiple Attribute Decision Making (MADM) and to perform calculations on the case MADM method in finding the best alternative based on the criteria specified use traditional methods TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to perform the calculations. TOPSIS method is chosen as the method is based on the concept that the best alternative was chosen not only has the shortest distance from the positive ideal solution, but also has the longest distance from the negative ideal solution

Essays on attribute inattention choice behavior

Doctor of PhilosophyDepartment of Agricultural EconomicsGlynn T. TonsorThe main objective of this dissertation is to explore attribute non-attendance choice in food consumption research under the discrete choice framework. The standard choice analysis based on random utility maximization assumes that an agent evaluates every attribute of alternatives and selects his or her most preferred option that maximizes utility in a given choice situation. However, recent empirical evidence reveals that decision makers may ignore a certain attribute presented in a choice set. My dissertation research investigates inattention choice behaviors using stated and revealed preferences data. The first essay, “Out-of-sample Validity of Random Response Share Approach”, applied the Random Response Share (RRS) approach that was proposed by Malone and Lusk (2018) for investigating inattention choice in choice experiments. The aim of the RRS approach is to identify and purge inattention observations in analysis. We applied the RRS and assessed the out-of-sample predictive performance of the RRS using 60 months of choice experiment data from 61,592 U.S households. Our results show that the RRS is not a dominant strategy to the conventional multinomial logit model in terms of out-of-sample forecasting accuracy. However, the RRS could be a way to deal with attribute nonattendance when also considering the socio-economic characteristics of respondents because it is not harmful compared to the predictive accuracy of the traditional multinomial logit model. In the second essay, “Incorporating Choice Heuristics in Analysis of Decision Making”, we investigated consumers’ heuristic choices when purchasing hotdog sausage products. This study applied the IRI marketing data set into the latent class structure of the discrete choice models to explore choice heuristics based on different attribute processing at the level of the household. The main contribution of this study is to incorporate attribute inattention into discrete choice model using actual market data, instead of stated choice data. The estimation results based on multiple models reveal that marginal utilities and willingness to pay estimates for attributes of hotdog products are sensitive to model selection. Our empirical analysis suggests that accounting for heterogeneous decision rules could provide better model fit. Thus, researchers need to consider the heterogeneous decision rules as an alternative to the classic assumption that all attributes are considered in choice situations by decision makers to better understand consumers' choices and provide more accurate policy implications. To sum up, the traditional assumption of full attribute consideration may be strong and restrictive to reflect consumer decision making rules. Recent studies are attempting to relax this assumption and reflect real choice environments. Considering ANA-based choice behaviors may help improve understanding of consumer preference through better analysis of decision making. I hope that this dissertation on attribute inattention choices will be a steppingstone to additional research in the field of discrete choice analysis