The contributions of multi‐criteria decision making to the development of decision support systems subspecialties: an empirical investigation

This study focuses on examining the structure of decision support systems (DSS) research, with a particular emphasis on assessing the contributions of multi‐criteria decision making (MCDM) to the development of each of the DSS subspecialty areas. This study traces how concepts and findings by researchers in the MCDM area have been picked up by DSS researchers to be applied, extended and refined in the development of DSS research subspecialties. In doing so, factor analysis is applied to an author cocitation frequency matrix derived from a large database file of comprehensive DSS literature. This study concludes that researchers in the MCDM area have made crucial contributions to the development of DSS research subspecialties. Copyright © 1999 John Wiley & Sons, Ltd.     

Evolution of multi‐criteria analysis: a scientometric analysis

The field of multi‐criteria analysis has known very important developments during the last 20 years, if one considers the very large amount of activities that it has generated and continues to generate (papers in journals, a dedicated journal, books, meetings, applications, …). Even if multi‐criteria analysis has reached some maturity, its future is subject to questions and debate among its researchers: what is the evolution of the field? What is its structure? Is it integrating new topics? In order to answer such questions at least partially, we have studied multi‐criteria analysis literature by means of the method of associated words (scientometric analysis) and the software ‘Leximappe’. All the abstracts of the papers which are relative to multi‐criteria analysis and are included in the bibliographic ABI‐INFORM database from 1985 to 1996 have been analysed. In this paper, we will present some results of this study. Copyright © 1999 John Wiley & Sons, Ltd.     

Trade‐off ranking method for multi‐criteria decision analysis

Multi‐criteria decision analysis presumes trade‐off between different criteria. As a result, the optimal solution is not unique and can be represented by the Pareto frontier in the objective space. Each Pareto solution is a compromise between different objectives. Despite a limited number of Pareto optimal solutions, the decision‐maker eventually has to choose only one option. Such a choice has to be made with the use of additional preferences not included in the original formulation of the optimization problem. The paper represents a new approach to an automatic ranking that can help the decision‐maker. In contrast to the other methodologies, the proposed method is based on the minimization of trade‐off between different Pareto solutions. To be realized, the approach presumes the existence of a well‐distributed Pareto set representing the entire Pareto frontier. In the paper, such a set is generated with the use of the directed search domain algorithm. The method is applied to a number of test cases and compared against two existing alternative approaches.     

Improving an optimization‐based framework for sensitivity analysis in multi‐criteria decision‐making

The framework for sensitivity analysis in discrete multi‐criteria decision analysis developed by Rios Insua and French allows simultaneous variation of all parameters and applies to many paradigms for decision analysis. However, its computational load may inhibit use, particularly in the context of a decision conference where results are required in near real time. In order to improve on the current algorithm and its implementation, we investigate, on the one hand, an opportunistic approach aimed at reducing the number of optimization problems solved in the original framework and, on the other, an alternative framework based on distance analysis. Computational results on linear and bilinear models are reported. Copyright © 2001 John Wiley & Sons, Ltd.     

Weight approximations in multi‐attribute decision models

The use of surrogate weights based on rankings has been proposed as a method for avoiding difficulties associated with the elicitation of weights in multi‐attribute decision analysis. When the simple multiattribute rating technique using swings (SMARTS) method is being employed it has been suggested that rank order centroid (ROC) weights are the best surrogate weights to use. This study shows that ROC weights are appropriate to use as a substitute for original weights that are constrained to sum to a fixed total (usually 1 or 100) as used in the point allocation method. If, however, the original weights are determined without any initial restrictions, as in the direct rating method, and are then normalized, which is the common procedure in SMARTS analysis, then the ROC weights do not provide the best approximations to the original weights. This paper shows how to obtain rank order distribution (ROD) weights that provide a better approximation than the ROC approach to unrestricted original weights. The paper also shows that, as the number of attributes in a decision problem increases, the ROD weights approximate to the more easily calculated rank sum weights. Copyright © 2003 John Wiley & Sons, Ltd.     

A parameter analysis method for the weight‐set to satisfy preference orders of alternatives in additive multi‐criteria value models

In the context of the additive multi‐criteria value model, this paper investigates how the set of criteria weights (weight‐set hereafter) can be determined according to the preference orders of alternatives given by the decision maker. A construction method is proposed for the weight‐set for different intervals of β, where β is a differential amount of value between the preference information on two alternatives. The results of this paper are important for sensitivity analysis in multi‐criteria decision making (MCDM) problems and multi‐criteria group decision analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

An evaluation of multi‐criteria methods in integrated assessment of climate policy

Those who conduct integrated assessments (IAs) are aware of the need to explicitly consider multiple criteria and uncertainties when evaluating policies for preventing global warming. MCDM methods are potentially useful for understanding tradeoffs and evaluating risks associated with climate policy alternatives. A difficulty facing potential MCDM users is the wide range of different techniques that have been proposed, each with distinct advantages. Methods differ in terms of validity, ease of use, and appropriateness to the problem. Alternative methods also can yield strikingly different rankings of alternatives. A workshop was held in which climate change experts and policy makers evaluated the usefulness of MCDM for IA. Participants applied several methods in the context of a hypothetical greenhouse gas policy decision. Methods compared include value and utility functions, goal programming, ELECTRE, fuzzy sets, stochastic dominance, min max regret, and several weight selection methods. Ranges, rather than point estimates, were provided for some questions to incorporate imprecision regarding weights. Additionally, several visualization methods for both deterministic and uncertain cases were used and evaluated. Analysis of method results and participant feedback through questionnaires and discussion provide the basis for conclusions regarding the use of MCDM methods for climate change policy and IA analyses. Hypotheses are examined concerning predictive and convergent validity of methods, existence of splitting bias among experts, perceived ability of methods to aid decision‐making, and whether expressing imprecision can change ranking results. Because participants gained from viewing a problem from several perspectives and results from different methods often significantly differed, it appears worthwhile to apply several MCDM methods to increase user confidence and insight. The participants themselves recommended such multimethod approaches for policymaking. Yet they preferred the freedom of unaided decision‐making most of all, challenging the MCDM community to create transparent methods that permit maximum user control. Copyright © 2002 John Wiley & Sons, Ltd.     

On the advancement and software support of decision‐making in focused ultrasound therapy

Focused ultrasound therapy (FUS) is a modern and promising way for minimally invasive cancer treatment. Recent advances in treatment technology, bio‐physical models, and numerical simulation methods have given rise to a significant curative potential. However, clinical routine of FUS still features classical planning approaches, which widely fail in exploiting this potential. The structure of FUS planning problems strongly suggests interactive multi‐criteria decision‐making concepts in order to improve treatment quality. This research work introduces an multi‐criteria decision‐making approach to FUS planning and explains how to bridge the conceptual gap between the clinical state of the art and this new planning paradigm.     

Reduction of pairwise comparisons in decision making via a duality approach

Although pairwise comparisons have been seen by many as an effective and intuitive way for eliciting qualitative data for multi‐criteria decision making problems, a major drawback is that the number of the required comparisons increases quadratically with the number of the entities to be compared. Thus, often even data for medium size decision problems may be impractical to be elicited via pairwise comparisons. The more the comparisons are, the higher is the likelihood that the decision maker will introduce erroneous data. This paper introduces a dual formulation to a given multi‐criteria decision making problem, which can significantly alleviate the previous problems. Some theoretical results establish that this is possible when the number of alternatives is greater than the number of decision criteria plus one. Copyright © 1999 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.     

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.     

Production planning of Pakistan Tobacco Company (PTC) using quantitative and multiple‐criteria decision analysis—A case in‐point

The main purpose of this paper is to improve the production planning of Pakistan Tobacco Company by selecting the most preferred brand and subsequently generating maximum profit from it. As the company produces a variety of products, the technique of multi criteria decision making is used to select the most preferred brand. To generate the maximum output from the preferred brand, different methods of qualitative managerial analysis are used, which include decision analysis to decide “why and where” the manufacturing should be carried out, transportation model to minimize the logistics cost while meeting the demand, and linear programming technique to maximize the profit generated in 2014–2015. The result obtained from analytical hierarchy process shows that the most preferred brand of the company with respect to price, quality, and comfort is John Player Gold Leaf. The decision analysis explains that this brand should be manufactured in the Jhelum factory of the company as it is more cost‐effective to produce and there is a high availability of resources. Transportation model minimizes the logistics cost of this brand from the 2 factories while meeting the demand at each provinces, central warehouse. Linear programming contributes in generating a profit of 32.738 billion PKR with an amount of 0.35 million PKR, which is more than that of the current profit of the company in the year. These results will allow the top management of the company to take corrective decisions well in time, gain a core competency in cost reduction, and make the supply chain process more efficient.     

Generating consensus priority interval vectors for group decision‐making in the AHP

Many practical and important decision‐making problems are complicated by at least two factors: (1) the qualitative/subjective nature of some criteria often results in uncertainty in the individual ratings; and (2) group decision‐making is involved and some means of aggregating individual ratings is required. Traditionally, both individual and group priorities have been represented as point estimates, but this approach presents severe limitations for accommodating imprecision in the decision‐making process. This paper examines the group decision‐making problem in the context where priorities are represented as numeric intervals. A set of techniques that could be used at some of the phases of an analytic hierarchy process (AHP)‐based group decision‐making process, which has the objective of generating a ‘consensus’ priority that represents the group's opinion with regards to the relative importance of a set of N objects (e.g. criteria, alternatives), is presented. Copyright © 2000 John Wiley & Sons, Ltd.     

Differences in multiple‐target visual search performance between non‐professional and professional searchers due to decision‐making criteria

Professional visual searches, such as those conducted by airport security personnel, often demand highly accurate performance. As many factors can hinder accuracy, it is critical to understand the potential influences. Here, we examined how explicit decision‐making criteria might affect multiple‐target search performance. Non‐professional searchers (college undergraduates) and professional searchers (airport security officers) classified trials as ‘safe’ or ‘dangerous’, in one of two conditions. Those in the ‘one = dangerous’ condition classified trials as dangerous if they found one or two targets, and those in the ‘one = safe’ condition only classified trials as dangerous if they found two targets. The data suggest an important role of context that may be mediated by experience; non‐professional searchers were more likely to miss a second target in the one = dangerous condition (i.e., when finding a second found target did not change the classification), whereas professional searchers were more likely to miss a second in the one = safe condition.     

Two new cases of rank reversals when the AHP and some of its additive variants are used that do not occur with the multiplicative AHP

Many researchers have long observed some cases in which certain ranking irregularities can occur when the original analytic hierarchy process (AHP), or some of its variants, are used. This paper presents two new categories of ranking irregularities which defy common intuition. These ranking irregularities occur when one decomposes a decision problem into a set of smaller problems each defined on two alternatives and the same criteria as the original problem. These irregularities are possible when the original AHP, or some of its additive variants, are used. Computational experiments on random test problems and an examination of some real‐life case studies suggest that these ranking irregularities are dramatically likely to occur. This paper also proves that these ranking irregularities are not possible when a multiplicative variant of the AHP is used. Copyright © 2001 John Wiley & Sons, Ltd.     

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.     

Rough approximation of pairwise comparisons described by multi‐attribute stochastic dominance

Given a finite set A of actions evaluated by a set of attributes, preferential information is considered in the form of a pairwise comparison table including pairs of actions from subset B⊂A described by stochastic dominance relations on particular attributes and a total order on the decision attribute. Using a rough sets approach for the analysis of the subset of preference relations, a set of decision rules is obtained, and these are applied to a set A\B of potential actions. The rough sets approach of looking for the reduction of the set of attributes gives us the possibility of operating on a multi‐attribute stochastic dominance for a reduced number of attributes. Copyright © 1999 John Wiley & Sons, Ltd.     

A Survey on Development and Applications of the Multi‐criteria Decision Making Method MULTIMOORA

This paper focuses on the MULTIMOORA method. Specifically, it discusses its development as well as extensions alongside with an overview of their applications. Indeed, the MULTIMOORA is a relatively new multi‐criteria decision making method consisting of the three parts. Therefore, it is specific with peculiarities of both complete and partial aggregation techniques. The MULTIMOORA method consists of the three parts, namely the Ratio System, the Reference Point, and the Full Multiplicative Form. The paper discussed the extensions of MULTIMOORA into the fuzzy environment and group decision making. The carried out survey identified the two major areas of application of MOORA and MULTIMOORA, namely engineering decision support for technological development and economic researches. The economic researches can further be classified into those related to micro‐level or macro‐level analyses. Copyright © 2013 John Wiley & Sons, Ltd.     

Attribute‐based differentiation of alternatives

An intermediate step is introduced to the dialogue decision process for decision analysis. Alternatives are refined after they have been generated within a strategy table but before they are subject to more detailed evaluation. Two or more judges create a subjective mapping from alternatives to attributes that will later be mapped to criteria. In strategy tables, each of the alternative strategies consists of a coherent set of choices made across several decisions that are to be coordinated. These strategic alternatives are modified so as to increase their differentiation in the attribute space, rather than in the decision space alone. When criteria weights are unknown, the best alternative from the modified set may be superior to the best alternative from the original set. Furthermore, analysis of the resulting alternatives may yield a better mapping of the value response surface for the action space, in the sense that this mapping leads to eventual construction of a higher value alternative. Results are reported for a consulting engagement incorporating the proposed step. Copyright © 2003 John Wiley & Sons, Ltd.