Fuzzy MCDM based on the concept of α‐cut

In a fuzzy multiple criteria decision‐making (MCDM) problem, with a hierarchical structure of more than two levels and involving multiple decision‐makers (DMs), to find the exact membership functions of the final aggregation ratings of all feasible alternatives is almost impossible. Thus, ranking methods based on exact membership functions cannot be utilized to rank the feasible alternatives and complete the optimal selection. To resolve the above‐mentioned complexity and to incorporate assessments of all DMs' viewpoints, in this paper a fuzzy MCDM method with multiple DMs, based on the concepts of fuzzy set theory and α‐cut, is developed. This method incorporates a number of perspectives on how to approach the fuzzy MCDM problem with multiple DMs, as follows: (1) combining quantitative and qualitative criteria as well as negative and positive ones; (2) using the generalized means to develop the aggregation method of multiple DMs' opinions; (3) incorporating the risk attitude index β to convey the total risk attitude of all DMs by using the estimation data obtained at the data input stage; (4) employing the algebraic operations of fuzzy numbers based on the concept of α‐cut to calculate the final aggregation ratings and develop a matching ranking method for proposed fuzzy MCDM method with multiple DMs. Furthermore, we use this method to survey the site selection for free port zone (FPZ) in Taiwan as an empirical study to demonstrate the proposed fuzzy MCDM algorithm. The result of this empirical investigation shows that the port of Kaohsiung, the largest international port of Taiwan as well as the sixth container port in the world in 2004, is optimal for the Taiwan government in enacting the plan of FPZ. Copyright © 2005 John Wiley & Sons, Ltd.     

The Characteristic Objects Method: A New Distance‐based Approach to Multicriteria Decision‐making Problems

Multicriteria decision‐making (MCDM) methods are concerned with the ranking of alternatives based on expert judgements made using a number of criteria. In the MCDM field, the distance‐based approach is one popular method for obtaining a final ranking. The technique for order preference by similarity to the ideal solution (TOPSIS) is a commonly used example of this kind of MCDM method. The TOPSIS ranks the alternatives with respect to their geometric distance from the positive and negative ideal solutions. Unfortunately, two reference points are often insufficient, especially for nonlinear problems. As a consequence of this situation, the final result ranking is prone to errors, including the rank reversals phenomenon. This study proposes a new distance‐based MCDM method: the characteristic objects method. In this approach, the preferences of each alternative are obtained on the basis of the distance from the nearest characteristic objects and their values. For this purpose, we have determined the domain and Fuzzy number set for all the considered criteria. The characteristic objects are obtained as the combination of the crisp values of all the Fuzzy numbers. The preference values of all the characteristic object are determined on the basis of the tournament method and the principle of indifference. Finally, the Fuzzy model is constructed and is used to calculate preference values of the alternatives, making it a multicriteria model that is free of rank reversal. The numerical example is used to illustrate the efficiency of the proposed method with respect to results from the TOPSIS method. The characteristic objects method results are more realistic than the TOPSIS results. Copyright © 2014 John Wiley & Sons, Ltd.     

Group‐analytic network process for balancing stakeholder views on fire protection of steel‐framed buildings

To select appropriate fire protection options for buildings during their design stage, economic, safety, environmental, and societal criteria need to be accounted for. The divergent and sometimes conflictual desires from different fire design stakeholders involved in the process present a multicriteria decision problem. Design decision criteria and fire protection options can be interdependent, and so there is a need to manage these desires with an advanced decision analysis technique, thereby reducing uncertainties in the complex decision‐making process. The aim of this paper is to use the weighted/geometric mean method‐analytic network process (W/GMM‐ANP) to balance the opinions of fire design stakeholders extracted from 42 structured stakeholder interviews on selecting the most suitable fire protection option for buildings constructed of steel frames. Different categories of interdependent decision elements were developed from 22 design decision criteria and 5 proposed fire protection options to produce a network of decision clusters for multicriteria decision analysis. In the synthesis and ranking of fire protection options, the W/GMM‐ANP accounted for the multiple interdependencies of weighted and unweighted stakeholder desires and managed the complexity of the decision‐making problem. The technique is proposed for approaching suitable group decisions in structural fire design of steel‐framed buildings as well as other performance‐based engineering decision making that may involve multidisciplinary stakeholders.     

Instance‐based learning in dynamic decision making

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.     

The Selection of Multiattribute Decision Making Methods for Scholarship Student Selection

Selecting scholarship students from a number of competing candidates is a complex decision making process, in which multiple selection criteria have to be considered simultaneously. Multiattribute decision making (MADM) has proven to be an effective approach for ranking or selecting one or more alternatives from a finite number of alternatives with respect to multiple, usually conflicting criteria. This paper formulates the scholarship student selection process as an MADM problem, and presents suitable compensatory methods for solving the problem. A new empirical validity procedure is developed to deal with the inconsistent ranking problem caused by different MADM methods. The procedure aims at selecting a ranking outcome which has a minimum expected value loss, when true attribute weights are not known. An empirical study of a scholarship student selection problem in an Australian university is conducted to illustrate how the selection procedure works.     

Strategic hybrid approach for selecting suppliers of high‐density polyethylene

Supplier selection is an important process for companies in the plastic sector due to its influence on firm performance and competitiveness. For a proper selection, a number of criteria from different aspects need to be considered by decision makers. Yet, as in different fields, because there are numerous criteria and alternatives to be considered in the plastic industry, choosing an appropriate multicriteria decision‐making approach has become a critical step for selecting suppliers. Therefore, the aim of this research is to define the most suitable supplier of high‐density polyethylene through the integration of powerful multicriteria decision‐making methods. For this purpose, the fuzzy analytic hierarchy process (FAHP) is initially applied to define initial weights of factors and subfactors under uncertainty, followed by the use of decision‐making trial and evaluation laboratory (DEMATEL) to evaluate interrelations between the elements of the hierarchy. Then, after combining FAHP and DEMATEL to calculate the final contributions of both factors and subfactors on the basis of interdependence, the technique for order of preference by similarity to ideal solution is used to assess the supplier alternatives. In addition, this paper also explores the differences between the judgments of decision makers for both AHP and DEMATEL methods. To do these, a case study is presented to demonstrate the validity of the proposed approach.     

Computing Pareto‐optimal Predictor Composites for Complex Selection Decisions

A complex selection situation encompasses vacancies for several different positions and applicants that apply simultaneously for one or several of these positions. This article presents an analytic method for estimating the expected selection quality, as well as the adverse impact ratio of these complex selections, when the decisions are based on a single predictor composite score. In addition, the method is integrated within a broader decision‐making framework for designing complex selection decisions that show a Pareto‐optimal balance between the selection quality and diversity goals. Finally, the decision aid is used to demonstrate the importance of applying the appropriate selection format (either the simple or the complex format) when exploring the front of Pareto‐optimal outcomes of planned selections.     

A Multicriteria Decision‐Making Application to the Conceptual Design of Mechanical Components

Early design is crucial for the success of the final product. In the conceptual design phase, several constraints, criteria, objectives and disciplines have to be considered. To this aim, multidisciplinary optimization has proven effective for the solution of engineering design problems, even in the industrial every‐day practice, to improve and simplify the work of designers in a successful quest of the best compromise solution. In this paper, a multicriteria decision‐making (MCDM)‐based design platform for early optimal design of industrial components is proposed. In a group decision‐making context, the selection of the most suitable component among several possible layouts is performed by means of a group Fuzzy Technique for Order of Preference by Similarity to Ideal Solution approach. Hence, a multi‐objective optimization is performed on the selected component by applying a multi‐objective particle swarm optimization for finding optimal component dimensions. An industrial case study is presented for showing the efficiency of the multicriteria decision‐making‐based design platform, regarding an innovative and low‐cost solution to increase the duration of heel tips in women's shoes. Copyright © 2016 John Wiley & Sons, Ltd.     

Robustness in the Pareto‐solutions for the multi‐criteria minisum location problem

In this paper, a new trend is introduced into the field of multi‐criteria location problems. We combine the robustness approach using the minmax regret criterion together with Pareto‐optimality. We consider the multi‐criteria squared Euclidean minisum location problem which consists of simultaneously minimizing a number of weighted sum‐distance functions and the set of Pareto‐optimal locations as its solution concept. The Pareto‐optimal solutions for the set of robust locations with respect to the original weighted sum‐distance functions is completely characterized. These Pareto‐optimal solutions have both the properties of stability and non‐domination which are required in robust and multi‐criteria programming. Copyright © 2001 John Wiley & Sons, Ltd.     

Model and interactive algorithm of bi‐level multi‐objective decision‐making with multiple interconnected decision makers

Furthering a prior research on two‐person bi‐level multi‐objective decision‐making problems of the leader‐follower Stackelberg game, we present an extended model of bi‐level multi‐objective decision‐making with multiple interconnected decision makers at the lower level. In the model, the upper level decision maker acts as a leader and the lower level decision makers behave as the followers, and inter‐connections and interactions exist among these followers in decision‐making scenarios. Following the rules of leader‐follower Stackelberg game, we develop an interactive algorithm of the model for solving multi‐objective decision‐making problems and reflecting the interactive natures among the decision makers. Finally, the authors exemplify the model and algorithm, and draw a conclusion on points of contributions and the significance of this study in decision‐making and support. Copyright © 2001 John Wiley & Sons, Ltd.     

Triple Bottomline Many‐Objective‐Based Decision Making for a Land Use Management Problem

A land use many‐objective optimization problem for a 1500‐ha farm with 315 paddocks was formulated with 14 objectives (maximizing sawlog production, pulpwood production, milksolids, beef, sheep meat, wool, carbon sequestration, water production, income and Earnings Before Interest and Tax; and minimizing costs, nitrate leaching, phosphorus loss and sedimentation). This was solved using a modified Reference‐point‐based Non‐dominated Sorting Genetic Algorithm II augmented by simulated epigenetic operations. The search space had complex variable interactions and was based on economic data and several interoperating simulation models. The solution was an approximation of a Hyperspace Pareto Frontier (HPF), where each non‐dominated trade‐off point represented a set of land‐use management actions taken within a 10‐year period and their related management options, spanning a planning period of 50 years. A trade‐off analysis was achieved using Hyper‐Radial Visualization (HRV) by collapsing the HPF into a 2‐D visualization capability through an interactive virtual reality (VR)‐based method, thereby facilitating intuitive selection of a sound compromise solution dictated by the decision makers' preferences under uncertainty conditions. Four scenarios of the HRV were considered emphasizing economic, sedimentation and nitrate leaching aspects—giving rise to a triple bottomline (i.e. the economic, environmental and social complex, where the social aspect is represented by the preferences of the various stakeholders). Highlights of the proposed approach are the development of an innovative epigenetics‐based multi‐objective optimizer, uncertainty incorporation in the search space data and decision making on a multi‐dimensional space through a VR‐simulation‐based visual steering process controlled at its core by a multi‐criterion decision making‐based process. This approach has widespread applicability to many other ‘wicked’ societal problem‐solving tasks. Copyright © 2014 John Wiley & Sons, Ltd.     

A Hierarchical Multi‐Attribute System Approach to Personnel Selection

In this paper, an approach to decision making in managing human resources that integrates multi‐attribute decision making techniques with expert systems is described. The approach is based on the explicit articulation of qualitative decision knowledge which is represented by a tree of attributes and decision rules. The decision making process is supported by DEXi, a specialized expert system shell for interactive construction of the knowledge base, evaluation of options, and explanation of the results. Practical use of the shell is illustrated by an application in the field of personnel selection for a top manager position.     

Early‐Entrant Advantage,Word‐of‐Mouth Communication,Brand Similarity,and the Consumer Decision‐Making Process

To better understand the competitive dynamics between an early and a later entrant, in this study we examined the extent to which word of mouth (WOM) regarding the later entrant and the later entrant's similarity to the early entrant influences the consumer decision process. We hypothesized that the influence of WOM and similarity depends on the nature of the decision‐making task, which is theorized as either a stimuli‐based or memory‐based task. A 3‐stage, sequential‐logit model with 2 focal brands (the early and later entrants) was developed to test the influence of independent measures on the likelihood of (a) retrieval; (b) consideration, given retrieval; and (c) choice, given consideration for both the early entrant and the focal follower. Data from 2 experiments provides support for the multistage conceptualization of the consumer decision process and demonstrates that the effects of WOM communication and similarity depend on the nature of the decision‐making task.     

When past meets present: the relationship between website‐induced nostalgia and well‐being

The present research examined whether social media websites increase feelings of nostalgia, and whether this nostalgic reverie promotes psychological and social health. Specifically, in comparison to control conditions, participants exposed to the websites Dear Old Love and Dear Photograph reported greater feelings of nostalgia (Studies 1–3), positive affect (Studies 1 and 3), life satisfaction (Study 1), and relationship need satisfaction (Study 2). Further, mediational analyses revealed that increased thoughts of nostalgia heightened subjective well‐being and social connectedness. Study 3 showed that the relationship between nostalgia and positive affect was specific to the Dear Photograph website and did not generalize to any website focused on close relationships. The implications of this research for nostalgia, internet use, and well‐being are further discussed.     

Identifying and prioritizing factors influencing the selection of the top suppliers of e‐procurement using FDEMATEL and FANP

The aim of this study is to identify, rank, and determine the importance of factors influencing the selection of the top supplier of electronic procurement in organizations using a hybrid approach. This is a case study of Irancell Telecommunication Service Company in which fuzzy decision‐making trial and evaluation (FDEMATEL) and fuzzy analytical network process (FANP) were used. FDEMATEL was employed to determine the interaction and interrelationships among the identified criteria and subcriteria, while utilizing FANP to calculate the weights of the criteria and their relevant subcriteria to determine their priorities. Therefore, the research literature was first reviewed to identify the factors influencing the selection of suppliers. For this purpose, the experts on communication networks were interviewed to select 16 final factors as the most important subcriteria that were then categorized as 5 groups (technology, environmental, services, organizational, and commercial) on which the study was based. After extracting the criteria for ranking the suppliers, codifying evaluation questionnaire, and converting the opinions in the Likert scale, 16 criteria were finally chosen with scores above the mean. FDEMATEL was used to determine the weights of subcriteria. In the final step, FANP was employed for prioritization. Results indicate that the organizational criterion is the most influential factor; however, the criterion services is the most permeable factor. The greatest weights were obtained from production capacity and provisions and geographical situation which were weighted 0.097 and 0.039, respectively. They are also regarded as the most and least important criteria for the selection of the top supplier of e‐procurement by the experts.     

The role of personality traits,core self‐evaluation,and emotional intelligence in career decision‐making difficulties

This study examines the role of personality traits, core self‐evaluation, and emotional intelligence (EI) in career decision‐making difficulties. Italian university students (N= 232) responded to questions on the Big Five Questionnaire, Core Self‐Evaluation Scale, Bar‐On Emotional Quotient Inventory, and Career Decision‐Making Difficulties Questionnaire. It was found that EI adds significant incremental variance compared with personality traits and core self‐evaluation in predicting career decision‐making difficulties. The results draw attention to the unique role of EI in career decision‐making difficulties, offering new research opportunities and intervention possibilities.     

Integrating Fast and Frugal Heuristics with a Model of Memory‐based Cue Generation

Although the fast and frugal heuristics have been studied extensively, relatively little attention has been paid to how cues are generated to be used within the heuristics. The goal of this paper is to propose and test a memory‐based model of how cues are generated and used in cue‐based inferences. The current study advances theory by integrating the fast and frugal heuristics with HyGene, a memory‐based model of how decision makers generate and evaluate hypotheses. Using archival data in which memory retrieval variables were not directly manipulated, we demonstrate that participants' cue selection behavior is consistent with memory‐based retrieval. Further, by directly manipulating memory retrieval within a cue‐based stock‐forecasting task, we demonstrate that memory processes underlie cue use. Participants' cue use varied depending on the relationship between cue validity and the frequency with which the cues were seen during learning. The HyGene model provided the best account of the empirical data, providing further evidence for the critical role of memory in judgment and decision making. Copyright © 2018 John Wiley & Sons, Ltd.     

Perfectionism and career decision‐making self‐efficacy

This study investigated the relationship between perfectionism and career decision‐making self‐efficacy. Participants completed the Almost Perfect Scale—Revised (R. B. Slaney, K. G. Rice, M. Mobley, J. Trippi, & J. S. Ashby, 2001) and the Career Decision‐Making Self‐Efficacy—Short Form (N. E. Betz, K. L. Klein, & K. M. Taylor, 1996). Adaptive perfectionists had higher levels of career decision‐making self‐efficacy than did maladaptive perfectionists and nonperfectionists. There was no difference between maladaptive perfectionists and nonperfectionists in career decision‐making self‐efficacy. Findings add to a growing body of research that suggests perfectionism has adaptive and maladaptive components. Implications for counseling and limitations are discussed.     

Simulation of Preference Information in an Interactive Reference Point‐Based Method for the Bi‐Objective Inventory Routing Problem

In the research area of multiple criteria decision making, very few publications exist that explicitly design the simulation of a decision maker (DM) in an interactive approach. For this reason, we outline some methods widely used in the literature to identify common assumptions of simulating the DM's responses and the required input preference information. Our paper aims at covering the identified gap by introducing experimental concepts. Such concepts are used for theoretical analyses of a combined search‐and‐decision‐making procedure. Simulating the DM is a fruitful idea because the algorithm can be tested without integrating a human decision maker. Finally, we conduct experiments based on the proposed settings for a multiobjective inventory routing problem, which is a relevant and challenging logistic problem. Copyright © 2014 John Wiley & Sons, Ltd.     

Goal‐Proximity Decision‐Making

Reinforcement learning (RL) models of decision‐making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal‐option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal‐Proximity Decision‐making (GPD), is implemented within the ACT‐R cognitive framework. GPD is found to be more efficient than RL in three maze‐navigation simulations. GPD advantages over RL seem to grow as task difficulty is increased. An experiment is presented where participants are asked to make choices in the absence of prior reward. GPD captures human performance in this experiment better than RL.