Spatial Dimensions of Multi-Criteria Analysis

<p>Multi-criteria decision making (MCDM) has been introduced to GIS about 15 years ago. Decision rules that have been implemented in the GIS environment include weighted linear combination, analytical hierarchy process, ideal point analysis, concordance-discordance analysis, and ordered weighted averaging. The spatial dimensions of MCDM include spatially distributed decision-makers and decision alternatives, decision objectives relating to geographical objects, and a non-uniform weighting across space. However, few (if any) MCDM methods incorporate spatial relationships in the decision rule itself. This presentation suggests using geographical weighting to influence the calculation of aggregated suitability scores. Inverse distance-based weights are used to adapt the suitability of locations to their neighbours’ scores. This method was implemented in the thematic mapping package CommonGIS, and applied to a site selection problem to demonstrate the usefulness of geographically weighted MCDM. Through its interactive cartography, CommonGIS supports the application of geographical weighting in conjunction with other spatial dimensions of multi-criteria analysis.</p>

<p>Multi-criteria decision making (MCDM) has been introduced to GIS about 15 years ago. Decision rules that have been implemented in the GIS environment include weighted linear combination, analytical hierarchy process, ideal point analysis, concordance-discordance analysis, and ordered weighted averaging. The spatial dimensions of MCDM include spatially distributed decision-makers and decision alternatives, decision objectives relating to geographical objects, and a non-uniform weighting across space. However, few (if any) MCDM methods incorporate spatial relationships in the decision rule itself. This presentation suggests using geographical weighting to influence the calculation of aggregated suitability scores. Inverse distance-based weights are used to adapt the suitability of locations to their neighbours’ scores. This method was implemented in the thematic mapping package CommonGIS, and applied to a site selection problem to demonstrate the usefulness of geographically weighted MCDM. Through its interactive cartography, CommonGIS supports the application of geographical weighting in conjunction with other spatial dimensions of multi-criteria analysis. </p>

<p>Multi-criteria decision making (MCDM) has been introduced to GIS about 15 years ago. Decision rules that have been implemented in the GIS environment include weighted linear combination, analytical hierarchy process, ideal point analysis, concordance-discordance analysis, and ordered weighted averaging. The spatial dimensions of MCDM include spatially distributed decision-makers and decision alternatives, decision objectives relating to geographical objects, and a non-uniform weighting across space. However, few (if any) MCDM methods incorporate spatial relationships in the decision rule itself. This presentation suggests using geographical weighting to influence the calculation of aggregated suitability scores. Inverse distance-based weights are used to adapt the suitability of locations to their neighbours’ scores. This method was implemented in the thematic mapping package CommonGIS, and applied to a site selection problem to demonstrate the usefulness of geographically weighted MCDM. Through its interactive cartography, CommonGIS supports the application of geographical weighting in conjunction with other spatial dimensions of multi-criteria analysis.</p>

n modern aircraft design, increased attention is being paid to the conceptual and preliminary design phases so as to increase the odds of creating a design that will ultimately be successful at the completion of the design process. Since aerospace systems are complex systems with interacting disciplines and technologies, the decision makers dealing with such design problems are involved in balancing multiple, potentially conflicting attributes/criteria, transforming a large amount of customer supplied guidelines into a solidly defined set of requirement definitions. As a result, the criteria have to be all simultaneously taken into account and a compromise essentially becomes part of the decision making process. Various methods and techniques are available to deal with such sort of multi-criteria decision making (MCDM) problems. In the 1970’s, Saaty proposed the Analytic Hierarchy Process (AHP), which facilitates the MCDM problems that have a hierarchical structure of attributes by reducing complex decisions to a series of pair-wise comparisons. In this method, the preference information is elicited as the pair-wise comparisons between attributes or alternatives and treated using the eigenvector method. The other straightforward method to handle the MCDM problem is the Overall Evaluation Criterion (OEC) technique, presented in Ref 3. The OEC is a single metric and is obtained by summing multiple non-dimensional attribute metrics normalized by the metric values of a relevant baseline. Another commonly used MCDM technique is the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). The “best” solution chosen by TOPSIS is the alternative that is the closest to the positive ideal solution and the furthest from the negative ideal solution. The separation between each alternative solution and the ideal solution, which is determined by the weighted criteria, is rather sensitive to criterion weights, so typically several weighting scenarios are investigated to determine the final solution. Among these developed MCDM methods, different methods have different underlying assumptions, information requirements, analysis models, and decision rules that are designed for solving a certain class of decision making problems. This implies that it is critical to use the most appropriate method to solve the problem under consideration since the use of unsuitable method always leads to misleading design decisions. Consequently, bad design decisions will result in big loss to the society, such as property damage or personal injury. Thus, it is necessary to review the existing MCDM methods, discuss in depth their advantages, disadvantages, applicability, computational complexity, etc. in order to make right decision when choosing the right method for the given problem. In this paper a hybrid MCDM method is developed to deal with the problem under consideration. Relative weights of the evaluation criteria are elicited by using the eigenvector method to describe the decision maker’s preference information. The TOPSIS method is used to analyze the qualitative and quantitative data of input parameters and find the solution to the given problem. An aircraft technology selection problem is conducted as a proof of implementation to demonstrate the functionality and effectiveness of the proposed methodology.

In recent years, Dempster–Shafer (D–S) theory has been widely used in multi-criteria decision-making (MCDM) problems due to its excellent performance in dealing with discrete ambiguous decision alternative (DA) evaluations. In the general framework of D–S-theory-based MCDM problems, the preference of the DAs for each criterion is regarded as a mass function over the set of DAs based on subjective evaluations. Moreover, the multi-criteria preference aggregation is based on Dempster’s combination rule. Unfortunately, this an idea faces two difficulties in real-world applications: (i) D–S theory can only deal with discrete uncertain evaluations, but is powerless in the face of continuous uncertain evaluations. (ii) The generation of the mass function for each criterion relies on the empirical judgments of experts, making it time-consuming and laborious in terms of the MCDM problem for large-scale DAs. To the best of our knowledge, these two difficulties cannot be addressed with existing D–S-theory-based MCDM methods. To this end, this paper proposes a clustering MCDM method combining D–S theory with the analytic hierarchy process (AHP) and the Silhouette coefficient. By employing the probability distribution and the D–S theory to represent discrete and continuous ambiguous evaluations, respectively, determining the focal element set for the mass function of each criterion through the clustering method, assigning the mass values of each criterion through the AHP method, and aggregating preferences according to Dempster’s combination rule, we show that our method can indeed address these two difficulties in MCDM problems. Finally, an example is given and comparative analyses with related methods are conducted to illustrate our method’s rationality, effectiveness, and efficiency.

PurposeThe purpose of this paper is to perform a systematic review on the application of different multi-criteria decision-making (MCDM) methods in solving the site selection problem across multiple problem domains. The domains are energy generation, logistics, public services and retail facilities. This study aims to answer the following research questions: Which evaluating criteria were used for each site selection problem domain? Which MCDM methods were frequently applied in a particular site selection problem domain?Design/methodology/approachThe goals of the systematic review were to identify the evaluating criteria as well as the MCDM method used for each problem domain. A total of 81 recent papers (2014–2018) including 32 papers published in conference proceedings and 49 journal articles from various databases including IEEE Xplore, PubMed, Springer, Taylor and Francis as well as ScienceDirect were evaluated.FindingsThis study has shown that site selection for energy generation facilities is the most active site selection problem domain, and that the analytic hierarchy process (AHP) method is the most commonly used MCDM method for site selection. For energy generation, the criteria which were most used were geographical elements, land use, cost and environmental impact. For logistics, frequently used criteria were geographical elements and distance, while for public services population density, supply and demand, geographical layout and cost were the criteria most used. Criteria useful for retail facilities were the size (space) of the store, demographics of the site, the site characteristics and rental of the site (cost).Research limitations/implicationsThis study is limited to reviewing papers which were published in the years 2014–2018 only, and only covers the domains of energy generation, logistics, public services and retail facilities.Practical implicationsMCDM is a viable tool to be used for solving the site selection problem across the domains of energy generation, logistics, public services and retail facilities. The usage of MCDM continues to be relevant as a complement to machine learning, even as data originating from embedded IoT devices in built environments becomes increasingly Big Data like.Originality/valuePrevious systematic review studies for MDCM and built environments have either focused on studying the MCDM techniques itself, or have focused on the application of MCDM for site selection in a single problem domain. In this study, a critical review of MCDM techniques used for site selection as well as the critical criteria used during the MCDM process of site selection was performed on four different built environment domains.

Fuzzy multicriteria decision making (MCDM) has been widely used in ranking a finite number of decision alternatives characterised by fuzzy assessments with respect to multiple evaluation criteria. The MCDM methods suitable for solving a given decision problem usually differ in their normalisation process and aggregation process for handling the performance ratings of the decision alternatives and the weights of the evaluation criteria. The overall preference of a decision alternative is obtained by aggregating the criteria weights and the performance ratings of the alternatives, on which the ranking is based. Due to their structural differences, these methods often produce inconsistent ranking results for the same fuzzy MCDM problem. To address this issue, this study develops a novel approach for the development and validation of fuzzy MCDM models. The approach incorporates three normalisation methods, three aggregation methods, and a α-cut based defuzzification method to develop fuzzy MCDM models. The α-cut based defuzzification method allows the decision maker’s attitude on fuzzy assessments to be incorporated into the decision making process. To examine the validity of the fuzzy MCDM models available for a given decision problem, a new validation process is developed based on the fuzzy clustering technique to assist in selecting a valid outcome from the inconsistent ranking results produced by these models. To demonstrate the effectiveness of the fuzzy MCDM model development and validation approach, three practical applications under various decision contexts are conducted. The first application is about the airport performance evaluation problem. This study selects 12 Asia-Pacific major international airports as the decision alternatives of the evaluation problem and identifies 19 quantitative and qualitative evaluation criteria under the airport operator, passenger, and airline dimensions. Based on three normalisation methods and two aggregation methods, six fuzzy MCDM models are developed which produce inconsistent ranking results for the evaluation problem. The ranking validity of the six models is examined by the validation process using fuzzy clustering and the most valid model is selected. The second application is concerned with the scrap metal buyer selection problem. This study considers five recycling companies in southern China as the decision alternatives of the buyer selection problem and identifies four qualitative selection criteria under the economic and environmental dimensions. Based on three normalisation methods and three aggregation methods, seven fuzzy MCDM models are developed which produce inconsistent ranking results for the selection problem. The ranking validity of the seven models is examined by the validation process using fuzzy clustering and the most valid model is selected. The third application deals with the non-ferrous scrap metal supplier selection problem. This study considers 15 scrap metal suppliers as the decision alternatives of the supplier selection problem and identifies five quantitative and qualitative selection criteria for a non-ferrous scrap metal buyer. Based on three normalisation methods and three aggregation methods, seven fuzzy MCDM models are developed which produce inconsistent ranking results for the selection problem. The ranking validity of the seven models is examined by the validation process using fuzzy clustering and the most valid model is selected. With the development of the approach and the three empirical applications, this study makes significant methodological and practical contributions. The approach addresses the validity issue of the cardinal rankings generated by different fuzzy MCDM models. In practical applications, the subjective attitude of the decision maker is effectively incorporated into the decision making process. With its simplicity in both concept and computation, the approach has a general applicability for solving general MCDM problems, and is particularly suited to decision situations where the ranking results produced by different fuzzy MCDM models differ significantly.

BACKGROUND: In the context of the progressive movement towards patientcentred care, patient-specific decision support is an important focus of interest. Many diagnostic and treatment patient decision aids (PDAs) are now available to help patients make informed choice decisions. An increasing number of these are software-based, with some available online. Multi-Criteria Decision Analysis (MCDA) is a potentially useful technique on which to base a software-assisted PDA, especially when the decision is complex - as is the case in choosing the best treatment for non-small cell lung cancer – but it has so far been relatively little exploited in this area. The use of any from a number of existing MCDA-based software applications in the development and delivery of a MCDA-based interactive PDA can be an effective way of achieving “best-practice” or normative standards of decision making, such as 1) a well-constructed set of decision criteria or 2) logically consistent patient preferences. However, it also involves the use of resources such as the time and cognitive effort involved in decision-making. The comparative evaluation of alternative MCDA-based software applications in developing and delivering a PDA therefore involves trade-offs between decision effectiveness and decision resource criteria moving from the normative to the prescriptive. MCDA is an ideal tool for this meta-evaluation task as well as for the adoption decision itself. AIM: To analyse, as proof of concept, the use of MCDA for the development, implementation and evaluation of interactive PDAs in routine clinical practice. OBJECTIVES: 1. To assess the use with clinicians in the Spanish NHS of two alternative MCDA software applications which implement dissimilar MCDA techniques in the development of a PDA in routine clinical practice; 2. To assess the use with clinicians in the Spanish NHS of the same two alternative MCDA software applications in the implementation of a PDA in an environment replicating actual clinical consultations; 3. To build a meta-multi-criteria decision model based on the Decision Resources Decision Effectiveness Analysis (DRDEA) framework and assess the use of this model by clinicians in the Spanish NHS to make the choice between the two MCDA applications as the basis for a PDA. METHODS: 1) Two dissimilar MCDA software applications served as a basis for the development of a lung cancer clinical management PDA in close collaboration with two different groups of three clinicians from two different Spanish NHS hospitals (H1 and H2): 1) Expert Choice, which implements the Analytic Hierarchy Process (AHP) MCDA approach; 2) Annalisa in Elicia (ALEL), which implements the Simple Attribute Weighting (SAW) MCDA approach. The process of codevelopment of the PDA in hospitals H1 and H2 was documented; 2) Expert Choice was used to implement (i.e. deliver) the lung cancer clinical management PDA in three hypothetical consultations in hospital H1. In each consultation, one of the three clinicians involved in the development of the tool, with support by this researcher, guided a proxy patient (a non-clinical member of hospital staff) through the PDA. The same process was repeated with the MCDA software ALEL in hospital H2. The process of delivery of the PDA in hospitals H1 and H2 was documented; 3) This researcher built a meta-multi-criteria decision model based on the DRDEA framework to help clinicians choose between different MCDA software applications as the basis of a PDA. The MCDA approach used for this meta-model was Multi- Attribute Value Theory (MAVT). The model was implemented, using the software HiView 3, with three clinicians from hospital H3 for the choice between Expert Choice and ALEL as the basis of a lung cancer clinical management PDA. RESULTS: The thesis makes a three-fold contribution to research in patient-centred decision support. First, it presents two new MCDA software-based approaches to clinical decision support, based on joint work with clinicians in the Spanish NHS, for developing an interactive PDA for the clinical management of non-small cell lung cancer. Second, it describes the use of these decision support tools in the delivery of 5 an interactive PDA for the clinical management of non-small cell lung cancer in a hospital environment via simulated consultations between actual clinicians, with support from this researcher, and proxy lung cancer patients. Third, it presents and applies a new MCDA-based methodology for evaluating the use of alternative MCDA software applications in the development and delivery of interactive PDAs.

Effects of increasing fuzziness on analytic hierarchy process for spatial multicriteria decision analysis

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In today’s highly competitive and turbulent business environment, selection of reliable and high quality suppliers has become the most important purchasing decision in order to reduce the production cost while maintaining the product quality and customer satisfaction simultaneously. The problem of supplier selection gets complicated further when a company looks for various criteria to evaluate different suppliers that lead it to become a multi-criteria decision making (MCDM) problem. This work reviews supplier selection models based on both individual and hybrid MCDM methodologies. A case study of an automobile company is presented to illustrate and propose three alternative supplier selection models based on analytic hierarchy process (AHP) as an individual MCDM methodology and data envelopment analytic hierarchy process (DEAHP) and fuzzy analytic hierarchy process (FAHP) as hybrid MCDM methodologies.

In two volumes, this new edition presents the state of the art in Multiple Criteria Decision Analysis (MCDA). Reflecting the explosive growth in the field seen during the last several years, the editors not only present surveys of the foundations of MCDA, but look as well at many new areas and new applications. Individual chapter authors are among the most prestigious names in MCDA research, and combined their chapters bring the field completely up to date. Part I of the book considers the history and current state of MCDA, with surveys that cover the early history of MCDA and an overview that discusses the “pre-theoretical” assumptions of MCDA. Part II then presents the foundations of MCDA, with individual chapters that provide a very exhaustive review of preference modeling, along with a chapter devoted to the axiomatic basis of the different models that multiple criteria preferences. Part III looks at outranking methods, with three chapters that consider the ELECTRE methods, PROMETHEE methods, and a look at the rich literature of other outranking methods. Part IV, on Multiattribute Utility and Value Theories (MAUT), presents chapters on the fundamentals of this approach, the very well known UTA methods, the Analytic Hierarchy Process (AHP) and its more recent extension, the Analytic Network Process (ANP), as well as a chapter on MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). Part V looks at Non-Classical MCDA Approaches, with chapters on risk and uncertainty in MCDA, the decision rule approach to MCDA, the fuzzy integral approach, the verbal decision methods, and a tentative assessment of the role of fuzzy sets in decision analysis. Part VI, on Multiobjective Optimization, contains chapters on recent developments of vector and set optimization, the state of the art in continuous multiobjective programming, multiobjective combinatorial optimization, fuzzy multicriteria optimization, a review of the field of goal programming, interactive methods for solving multiobjective optimization problems, and relationships between MCDA and evolutionary multiobjective optimization (EMO). Part VII, on Applications, selects some of the most significant areas, including contributions of MCDA in finance, energy planning problems, telecommunication network planning and design, sustainable development, and portfolio analysis. Finally, Part VIII, on MCDM software, presents well known MCDA software packages.

Participatory management is a working method of paramount importance based on the principles of knowledge sharing and accountability for addressing the sustainable management of the fishery sector. To approach this multidimensional problem we applied two Multi Criteria Decision Analysis (MCDA) methods, the Analytic Hierarchy Process (AHP) and the Non-Structural Fuzzy Decision Support System (NSFDSS), which were applied incorporating uncertainty to generate probabilistic rankings. The NSFDSS technique was applied for the first time to address a fishery problem. Two surveys were carried out among Mediterranean Advisory Council (MEDAC) stakeholders with different backgrounds. By the two surveys we: i) made an AHP test for exploring stakeholders’ perception of the objectives and indicators used in the monitoring of the stocks, ecosystem, and fisheries, and ii) introduced the NSFDSS technique, gathering feedback on stakeholders’ preferences on management options for improving fishery sustainability (e.g., reducing discards, improving ecosystem state and economic yield in the long term). In the AHP the respondents were asked to evaluate the importance of one objective against another according to a scale of semantic scores from 1 to 5, whereas a simpler scoring scale, with only 3 possible options, was used in the NSFDSS. The two MCDA methods were proven to be useful to elicit stakeholders’ view on the potential effects of key issues on economic and environmental fishery sustainability. The results showed stakeholders’ awareness of the fact that the reproductive potential should be secured by checking mortality and/or fishing intensity. Consistently, among the ecological indicators that are tracking the fisheries policy objectives, a higher rank was attributed to “mean size of the spawners,” while cost efficiency was considered to be essential for improving profits. Regarding the economic indicators, stakeholders gave higher priority to “revenue” in comparison to “production (catches),” which is a sign of awareness that increasing fish production does not necessarily turn into increased revenue. Among the different management strategies, “fleet withdrawal” (scraping) was considered as the worst option, while the “combination of measures” was considered to be the best alternative for achieving a sustainable fishery in the long term.

One of the most challenging tasks in data mining is to choose a better classifier for classification problems as this involves multiple criteria. The multiple criteria decision making (MCDM) techniques are noteworthy to judge different alternatives on various criteria. In this paper, a simplified MCDM technique is applied to make a judgement on different alternatives (classifiers) among multiple criteria in financial risk datasets. For this purpose, the paired t-test statistical significance test and significant win-loss tables are used to determine the performance scores for each classifier. Then, the weights are determined using an analytic hierarchy process (AHP) and finally simplified MCDM weighted sum model is applied to rank the classifiers. In addition, the efficiency of this simplified MCDM method is compared with other top MCDM methods such as TOPSIS, PROMETHEE and VIKOR to evaluate if there are any discrepancies in ranking. Analysis has been done for the three financial risk datasets from the UCI machine learning repository and surprisingly this simplified approach and the other top MCDM methods produce consistent rankings. Logistic regression and Bayesnet are ranked as the top two classifiers for financial risk datasets by this simplified approach and the other top MCDM methods. The simplified MCDM model can be applied to rank the classifiers which use simplified backgrounds in making right decisions among multiple criteria.

<p>Multi-criteria decision analysis (MCDA) is a family of decision support methods that allow analysts to structure a decision problem through the selection and evaluation of multiple and often conflicting criteria, using established techniques to standardize, weight, and combine these criteria. Through a case study of an area-based deprivation index for the city of Toronto’s 140 neighbourhoods, we examine the variability of MCDA results under different decision models. We use interactive cartographic visualization to explore the impact of criterion weighting and three decision rules: weighted linear combination, locally weighted linear combination, and ordered weighted averaging. The modelling of socioeconomic deprivation using these different decision rules and their parameters yielded different spatial patterns of deprivation for the same set of variables and weights. The results highlight the importance of examining multiple decision models before making policy recommendations.</p> <p><br></p> <p> </p> <p>Keywords: analytic hierarchy process, area-based composite index, locally weighted linear combination, multi-criteria decision analysis, ordered weighted averaging, socio-economic deprivation</p>