Journal of Multi-Criteria Decision Analysis最新文献

Targeted medical decision-making is a current strategy for addressing the heterogeneity in the patient population, especially when patients' preferences are included in the decision-making process. In this paper, we propose a user-customizable hybrid framework that can be adjusted at the patient group level to target a medical decision process. Our framework provides a flexible design, capable of balancing the gain from the reduction of provider time against the cost of prediction inaccuracy resulting from group customization. The framework combines a descriptive process, used to group the patients based on preference-based subjective features, with a predictive process, which uses objective features to match a new patient with a group. We illustrate our approach by applying it to the colorectal cancer-screening problem. The provider chooses what level of trade-off is appropriate, as a function of the acceptable error level. The group customization process allows decision makers to better allocate scarce resources, by potentially shortening the time-consuming process of modelling patients' preferences using individualised stability analysis. The proposed framework might be applied, with minor changes, to various medical decisions, or even to broader provider-user scenarios, in which targeted decision-making that includes user preferences is advantageous.

In today's dynamic business landscape, organisations increasingly realise the pivotal role of innovative marketing approaches (IMA) in gaining a competitive edge and driving sustainable growth. However, many organisations encounter formidable barriers to adopting and developing IMA, despite the growing importance of innovative approaches in marketing. The purpose of this research is to identify, prioritise, establish the inter-relationships and propose a ‘level-wise structural model’ of the barriers that an organisation faces in adopting and developing IMA. The research employed a mix of quantitative and qualitative methods. An exhaustive review of related literature and theoretical frameworks (namely the ‘resource-based view’ (RBV) theory, the ‘social capital theory’ (SCT) theory, the ‘innovation resistance theory’ (IRT) theory, and the ‘institutional theory’ (IT) theory), along with expert feedback, are used to categorise and finalise the barriers. Further, a case study from ‘micro, small and medium enterprises’ (MSMEs) in a developing nation such as India along with hybrid ‘multi-criteria decision-making’ (MCDM), that is, best-worst method (BWM), was applied to prioritise the barriers. While ‘interpretive structural modelling’ (ISM) was used to establish contextual relationships, and ‘matrice d'impacts croisés multiplication appliquée á un classment’ (MICMAC) analysis was employed to cluster the barriers. The findings suggest that among the main category technology-related barriers and from subcategories, infrastructure supports the top barriers and works as a driving obstacle while building IMA for sustainable development. The findings from this study can help develop effective marketing strategies that align profitability with environmental and social responsibility.

This paper investigates a family of multicost variational models driven by H-type I functionals. Concretely, under H-type I assumptions of the involved functionals, we state and prove necessary and sufficient criteria of efficiency for a feasible point in the considered class of controlled variational models.

MSC 2020 Classification: 49K20, 58E17, 58E25, 90C30

Pairwise comparison matrices (PCMs) are inevitable tools in some important multiple-criteria decision-making methods, for example AHP/ANP, TOPSIS, PROMETHEE and others. In this paper, we investigate some important properties of PCMs which influence the generated priority vectors for the final ranking of the given alternatives. The main subproblem of the Analytic Hierarchy Process (AHP) is to calculate the priority vectors, that is, the weights assigned to the elements of the hierarchy (criteria, sub-criteria, and/or alternatives or variants), by using the information provided in the form of a pairwise comparison matrix. Given a set of elements, and a corresponding pairwise comparison matrix, whose entries evaluate the relative importance of the elements with respect to a given criterion, the final ranking of the given alternatives is evaluated. We investigate some important and natural properties of PCMs called the desirable properties, particularly, the non-dominance, consistency, intensity and coherence, which influence the generated priority vectors. Usually, the priority vector is calculated based on some well-known method, for example, the Eigenvector Method, the Arithmetic Mean Method, the Geometric Mean Method, the Least Square Method, and so forth. The novelty of our approach is that the priority vector is calculated as the solution of an optimization problem where an error objective function is minimised with respect to constraints given by the desirable properties. The properties of the optimal solution are discussed and some illustrating examples are presented. The corresponding software tool has been developed and demonstrated in some examples.

The Best-Worst Method (BWM) is a well-known Multi-Criteria Decision-Making (MCDM) method. Numerous models of BWM have been developed by integrating various distance functions with the core principles of the method. This article addresses the multiplicative model of BWM. We first formulate an optimization model that is equivalent to the existing multiplicative model. This model provides a robust foundation for deriving analytical forms of optimal interval-weights, the Consistency Index (CI) and the Consistency Ratio (CR). The proposed approach eliminates the need for optimization software, enhancing its ease of implementation and time efficiency. Moreover, the derived analytical form of CR enables it to function as an input-based consistency measure. We then introduce a secondary objective function to select the best optimal weight set from the collection of all optimal weight sets. Finally, we present numerical examples to illustrate the proposed approach.

Amid growing concerns about environmental sustainability in agriculture, this study explores the potential of a game-theoretic approach to identify and implement sustainable crop patterns in the Marvdasht–Kherameh sub-basin, Iran. Employing a cooperative game-theoretic framework, the study considers farmers, environmental advocates and policymakers as stakeholders with distinct objectives: maximising profit, minimising environmental pollution and optimising water consumption. This research aims to develop and apply a comprehensive model that effectively integrates these diverse objectives, proposing practical, sustainable agricultural strategies for the region. The game-theoretic model is integrated with data analysis and optimization techniques to identify Pareto-optimal solutions that balance the competing objectives of stakeholders with vested interest. The advantage of this approach is its ability to produce a robust, spatially visualised solution using GIS-based mapping, which improves communication and aids in implementing sustainable crop patterns. Using regression models, an equilibrium point, defined as the intersection of the three polynomial regression models representing profit, nitrate fertiliser amount and water consumption objectives is identified. The optimised crop pattern corresponding to this equilibrium point is determined using a single-objective optimization model. Key findings include a 40% reduction in wheat cultivation and significant increases in barley (143%) and paddy rice (113%), leading to enhanced resource efficiency and sustainability by optimising water and nitrate fertiliser use. The results offer significant benefits, including detailed spatial visualisation and stakeholder-specific optimization for sustainable crop patterns, which can be adapted to other agricultural regions facing similar challenges.

In clinical consultation, Shared Decision Making (SDM) between the patient and clinician can inform the patient about available treatment options, likely harms and benefits in order to reach a joint decision that accommodates the patient's preferences. Multi-Criteria Decision Analysis (MCDA) can be beneficial for this complex problem by offering systematic approaches to elicit preferences for multiple conflicting criteria and evaluate alternatives accordingly. We propose an online SDM tool based on MCDA to guide decisions in clinical consultation where there are multiple treatment options whose outcomes in different factors vary according to the patient. The tool was designed with a panel of domain experts and it enables rapid elicitation of patient preferences and clinician judgements. It is based on Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II) to produce a comprehensive evaluation of the options. The patient-specific outcomes used in the SDM tool are derived from predictive machine learning models or published evidence. The tool was firstly qualitatively evaluated using a fictitious shoulder pain scenario with three focus groups of consultant and specialist physiotherapists. It was evaluated further with patients via structured interview format. The general response was positive; stating that the tool was informative about options and their performance in multiple criteria, and also useful in making a joint decision. The tool is ready to be incorporated into clinical care and evaluated by clinicians and patients in parallel with existing processes.