Journal of Multi-Criteria Decision Analysis最新文献

So far, the rank reversal (RR) phenomenon has been heavily discussed in the multiple criteria analysis literature. In this work, instead of focusing on RR occurrences within PROMETHEE II, an analysis of the third alternative with respect to a given pair of alternatives is conducted. The aim is to provide a way to identify third alternatives able to cause RR between a specific pair. This opens the question of building alternatives causing RR occurrences. Such alternatives can then be used to assess the resistance a given ranking has to RR. In the end, two different approaches are used. The first considers the number of third alternatives to be added to generate RR for a given pair of alternatives. While, the second one, provides a new threshold for RR occurrences in PROMETHEE II.

Surging glaciers are glaciers that experience rapidly accelerated glacier flow over a comparatively short period of time. Though relatively rare worldwide, Alaska is home to the largest number of surge-type glaciers globally. However, their impact on the broader socioecological system in the state is both poorly understood and under-researched, which poses a challenge in developing appropriate sustainability decisions in Alaska. We investigated how the surge patterns of the Bering Glacier in Alaska have potentially devastating effects on the local ecological biodiversity of its watershed via a structured decision-making analysis of the different possible consequences. Specifically, this analysis was conducted to explore the various outcomes of a Bering Glacier surge particularly if humans have an increased presence near the glacier due to the area potentially becoming a state park. This work explored the benefits of applying a risk and decision analytical framework in a cryosphere context, to better understand the socioeconomic impact of glacier surges. This is a novel approach in which a decision analysis tool was used to better understand an environmental sustainability challenge, offering an innovative method to support the achievement of the United Nations Sustainability Development Goals in Alaska. We therefore emphasise the need for integrated biophysical and socioeconomic analyses when it comes to understanding glacier hazards. Our research highlights the importance of understanding and researching biophysical changes as well as using a structured decision-making process for complicated hazard planning scenarios, exemplified via glaciated regions in Alaska, in order to create adaptation strategies that are sustainable and encompass the range of possible outcomes.

In this paper, we introduce new notions of mean values based on ideas of multicriteria optimization. The distances between the current point to all points in the sample are regarded as elements of a vector estimate. Such vector estimates are usually scalarized, for example, by taking the sum of all components. In contrast, we introduce preference relations on the set of all such vectors, based on the information about the preferences of the decision maker who could be a statistician, analyst or researcher. Such preference relations reflect the distances between points, including the case in which all distances are equally important. We define the mean values as the points whose corresponding vector estimates are nondominated with respect to the defined preference relation, and investigate their properties. Such mean values turn out to be multi-valued. We further explore the relationship between the new notions of mean values with their conventional definitions and suggest computational approaches to the calculation of the suggested new means. We also outline generalisations of the suggested approach to the case of multidimensional data.

This study combines the use of multi-objective decision trees (MODT) and a fuzzy TOPSIS approach to cope with sequential decision-making under uncertainty with multiple conflicting objectives. To this aim, the MODT generates a Pareto-optimal frontier of solutions for competing objectives, while fuzzy TOPSIS helps assess the noninferior solutions and assign weights to the objectives by considering possible uncertainties in outcomes as well as potential linguistic expression. The proposed approach is illustrated with a temporal decision problem concerning climate-driven refugee resettlement, taking into consideration the challenges of global warming and system interdependencies in an uncertain context. The fuzzy TOPSIS approach was illustrated using two different procedures, and the procedure based on vector normalisation graphically depicts the fuzzy triangular representation of the final ranking.

In 2018, the New York City (NYC) Department of Health and Mental Hygiene piloted a novel jurisdictional risk assessment (JRA) for public health disasters using the analytic hierarchy process (2018 JRA-AHP). This new approach offers a replicable and equitable model for consensus decision-making that incorporates the complexities of disaster preparedness. Its main contribution to the field is to offer a practical manifestation of emergency management's Whole Community Approach. The 2018 JRA-AHP applied this principle by expanding traditional definitions of ‘expertise’ to include lived experience of disaster and facilitating direct participation in the risk decision by a variety of individuals with diverse backgrounds, expertise and knowledge of public health disasters. This paper describes the theory, methods and results behind the JRA-AHP. The paper also presents a critical analysis of public health disaster risk assessments; contextualizes the Whole Community Approach using models of the relationship between democratic governments and communities; and addresses the practical applications of the 2018 JRA-AHP in NYC while exploring the potential challenges that other jurisdictions may face in attempted adaptation.

The analytic hierarchy process (AHP) is a cornerstone of multi-criteria decision analysis, enabling well-informed choices across diverse contexts. This paper introduces an original benchmark-based framework designed to enhance the precision of consistency classification for pairwise comparison matrices (PCMs) within the AHP methodology. This innovative approach quantifies the discrepancy between a given PCM and its benchmark matrix, comprising comparison ratios that faithfully reflect the relative preferences encapsulated within principal eigenvector values, thereby capturing the true degree of coherence. To ensure benchmark alignment with human perception, elements of the benchmark PCM are further rounded to the nearest values on the Fundamental Scale. The potency of our framework derives from two pivotal factors: the inherent Priority Preference Range within the principal eigenvector and the order of the PCM. Statistical thresholds for consistency are established using a technique based on simulated, logical PCMs, proposed by Bose [2022]. This rigorous method ensures an unbiased, objective and pragmatic evaluation of consistency, eliminating the subjectivity inherent in arbitrary thresholds based on random PCMs. Our approach rectifies the inconsistencies in the conventional CR method that yields false positives for PCMs of orders 3 and 4, and false negatives for higher orders. By harnessing customized benchmarks and eschewing random matrices, our framework systematically confronts the inherent consistency challenges within AHP, thus enhancing its decision-making capability. The practical utility of our approach is aptly demonstrated through AHPtools, an R-based library package designed to showcase our novel consistency evaluation method. The demonstration of the package in Appendix B will facilitate readers to easily apply our methodology to real-world PCM classification scenarios within the AHP. In conclusion, our benchmark-based framework heralds a transformative era in consistency classification within the AHP, empowering real-world multi-criteria decision-making with unprecedented precision and reliability, and ushering in a new paradigm of informed and astute outcomes.

Group decision-making (GDM) problems usually include stakeholders with different views and opinions. In order to find a collective solution, it is necessary to achieve a consensus reaching process (CRP) that may lead to the emergence of non-cooperative behaviors within the group. This article proposes to study how these non-cooperative behaviors appear in a group of decision-makers and what their level of impact is on the evolution of consensus and on the final decision. To provide some answers to this research problem, we propose a multi-criteria decision-making (MCDM) methodology implementing analytic hierarchy process (AHP) in order to provide a visualization of the CRP evolution and observe four non-cooperative behaviors within small-scale GDM: (1) collective agreement, (2) blocking behavior, (3) leadership demonstration, and (4) experts' coalition. We implement our methodology within a pedagogical framework, in 29 small-scale groups of masters and engineering students, through a case study related to the implementation of forest management scenarios in France. Our results show the evolution of the four non-cooperative behaviors within the groups, as well as their impact on the CRP outcomes.

This paper suggests a solution for validating an ANP model in multi-channel retailing proposed by Salmani et al. (2018) through its application in the pet consumables industry. The purpose of the proposed ANP model is to develop a measurement for estimating the demand proportion attributed to each established sales channel. Utilizing an ANP model, channel selection customer criteria as well as the interrelations between channel structures in a multi-channel setting are quantified. This paper applies a survey-based study to collect data from random pet consumable customers. Using a compatibility measurement, per-channel proportions of demand found by ANP are compared with actual data to examine the validity of the proposed model. The results of this study demonstrated the validity of the proposed ANP model applied in the pet consumables industry.

Sustainable development of cities constitutes nowadays a worldwide goal. Therefore, the related urban and architectural choices must fulfil sustainable objectives. In this context, sustainability assessment presents itself as a key and fundamental element to guide decision-making processes, orienting choices towards actions that make the built environment more sustainable. Among the several existing assessment tools and methods, multi-criteria decision analysis (MCDA) are part of the most widely used approaches to support sustainable decisions. Therefore, this article aims to understand what makes sustainability assessment through MCDA suitable or unsuitable to support decision-making processes in the context of sustainable urban and architectural design and observe how available MCDA methods support this purpose. To do this, a literature review related to MCDA methods in sustainable urban and architectural context has been performed. Descriptive statistics and tables are provided to point out the main trends according to specific research questions. In this sense, it is intended to highlight some potential gaps and points of reflection for future research developments that can support sustainable urban and architectural development.

Supply chain mapping is gaining heightened attention due to its vital role in improving supply chain visibility and resilience. Despite its crucial role in uplifting supply chain resilience, the critical elements of supply chain mapping are yet to be determined. The study adopts a twofold approach to identify and prioritize the dimensions and sub-dimensions of supply chain (SC) mapping. At the first stage, through an extensive review of literature, 43 sub-dimensions of SC mapping were identified. In the second stage, Gray - DEMATEL-based Analytic Network Process (GDANP) was employed by taking the input from 25 experts selected from Oil and Gas industry of an emerging market. The findings reveal three major dimensions of SC mapping followed by 15 sub-dimensions. Among the dimensions, upstream mapping contains the highest priority weights, followed by midstream and downstream mapping. The findings suggest a step-wise strategy to adopt SC mapping where upstream mapping should be given the first priority. The major contribution of this study is to develop a framework for measuring the extent of SC mapping of a firm using GDANP.