Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2026Scopus SJRQ4Sustainable land management under uncertainty: A hybrid Bayesian BWM and RPM framework for spatial prioritization
Konurhan, Zekeriya
Sustainable land management is a complex problem due to environmental constraints and decision-maker uncertainty. This study presents an integrated solution combining Geographic Information Systems (GIS), the Bayesian Best-Worst Method (Bayesian BWM), and the Ratio Product Model (RPM) to improve the accuracy of spatial prioritization in land-use decisions. The integration of the Bayesian BWM and RPM statistically minimizes the cognitive uncertainty in expert opinions whilst enabling the most suitable areas to be ranked with precision within their own categories, without any normalization constraints. The proposed model was developed for three main categories —residential, industrial, and agricultural— in the province of Balıkesir. The analysis results revealed that the ‘ease of access to services’ (0.5660) criterion was the most decisive factor for residential suitability, whilst ‘distance to power transmission lines’ (0.1322) and ‘average rainfall’ (0.1315) were the most decisive factors for industry and agriculture, respectively. The suitability maps show that the Bandırma-Susurluk corridor is strategically prioritized for industry, whilst the Manyas-Gönen basin is prioritized for agricultural production. RPM scores (0.2750 for settlement, 0.6036 for industry, and 0.3868 for agriculture) quantify implementation priorities, providing a reliable decision-support tool for managing uncertainty in spatial planning.
- Kitap BölümüYalnız künye2022A Multi-attribute Decision-Making to Sustainable Construction Material Selection: A Bayesian BWM-SAW Hybrid Model
Alkan, Ramazan; Yucesan, Melih; Gul, Muhammet; Rezaei, J; Brunelli, M; Mohammadi, M
The increase in urbanization and developments in the production industry has led to rapid progress in the construction sector. Many new strategies are developed in the industry to reduce costs and improve building quality. In recent years, the necessity of sustainable construction practices comes to the fore to renew the building stock damaged as a result of natural disasters and reduce the cost concerns that arise in this situation to a reasonable level. Due to limited resources and environmental concerns, researchers and practitioners have begun to develop sustainable building materials. The problem of selecting these materials when constructing a new building is vital. In particular, depending on the sector's rapid growth in Turkey, it is becoming more and more important to select the best sustainable construction material. Therefore, this paper proposes a model to evaluate the most appropriate sustainable construction material via two multi-attribute decision-making (MADM) methods called “Bayesian Best-Worst Method (BWM) and Simple Additive Weighting (SAW)”. Initially, the criteria derived from existing literature were evaluated with the aid of construction sector-based respondents and extra information about the interrelationship between the criteria were determined by credal ranking in Bayesian BWM. Then, via SAW, the most appropriate material was selected among a set of alternatives. Two cases regarding sustainable insulating material selection are considered for the demonstration of the proposed MADM model.
AtıfScopus15WoS14OpenAlex13
İncele - MakaleYalnız künye2022Scopus SJRQ1Occupational health, safety and environmental risk assessment in textile production industry through a Bayesian BWM-VIKOR approach
Ak, M. Fatih; Yucesan, Melih; Gul, Muhammet
Occupational risk assessment (ORA) is a process that consists of evaluating, ranking, and classifying the hazards and associated risks arising in any workplace from the viewpoint of occupational health and safety. Many ORA methods have been proposed in the literature, from a single independent expert to participatory methodologies made by group decision and simple to complex ones. In this paper, a holistic ORA is presented, which uses two important multi-attribute decision methods named Bayesian Best-Worst Method (Bayesian BWM) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Bayesian BWM is used to determine the importance weights of six different assessment criteria, which are the probability of hazardous event (P), frequency (F), severity (S), detectability (D), cost (C) and sensitivity not to use personal protective equipment (SNP). Since the classical BWM finds solution to the weights of a number of criteria from only one expert's judgment, Bayesian BWM is preferred in this paper (1) to enable participation of a group of experts, (2) to aggregate the preferences of these multiple experts into consensus without loss of information and (3) to follow a probabilistic way for solving the ORA problem. The hazards are then ranked by VIKOR. The approach is implemented in the ORA process of a textile production plant. Results of risk analysis showed that electricity hazard and associated risks constitute the highest risk ratings. These hazards arise from the product, process, human and working environment. The associated risks are evaluated, prioritized, and detailed control measures are proposed. This study made comparisons with the classical BWM-VIKOR approach to demonstrate the proposed approach's difference and practicality. Results can also help practitioners and risk analysts in formulating the improvement measures to increase the overall safety of the working environment further.
AtıfScopus87WoS78OpenAlex87İncele - MakaleYalnız künye2022Scopus SJRQ1Performance evaluation of Turkish Universities by an integrated Bayesian BWM-TOPSIS model
Gul, Muhammet; Yucesan, Melih
This study aims to develop a university ranking model with the aid of performance measures in the “University monitoring and evaluation reports-2019” published by the Council of Higher Education Institution in Turkey. In this context, some of the performance criteria stated in these reports are filtered and 34 sub-criteria under five main criteria are weighted using the Bayesian Best-Worst Method (BBWM). Then, 189 listed public and private universities are ranked using the TOPSIS multicriteria decision-making (MCDM) method. We have adopted the MCDM concept because many evaluation criteria/sub-criteria and alternative universities are taken into account. For this study, we apply an integrated MCDM model. First, we use BBWM to accomplish the first goal and adopt the TOPSIS method for the second purpose, using the BBWM results. Our purpose in using BBWM is due to its probabilistic structure that reduces the loss of information when handling group decisions. In this context, the evaluations of 11 academic experts are combined and a solid weighting is made by obtaining the credal rankings of performance criteria. Using TOPSIS is its logic of proximity to ideal and the ability to evaluate many alternatives. In the context of the study, state-private university-based, nomenclature of territorial units for statistics-2 (NUTS-2)-based and classical geographical regions-based rankings are also discussed. The study seeks to help universities optimize their performance efficiently. The results of the study can be adapted as a reference for other educational institutions and public institutions in their efforts to evaluate, improve their performance and form various policies.
AtıfScopus79WoS61OpenAlex79İncele