Multi-criteria Decision-making Process Research Articles

D&C technique as an MCDM tool for managing the heritage value assessment of historic buildings in post-war cities: Mosul Old City as a case study

PurposeThe value assessment process of the built heritage is a multipart procedure that includes diverse aspects with overlapping requirements. This process requires various measures to obtain better results, making it a multi-criteria decision-making (MCDM) process. In post-disaster cases, a management system is required to promptly evaluate the degree of risk and damage and to set the preservation priorities in order to effectively supervise and protect the heritage places. MCDM is utilized for configuring and solving decision and planning problems encompassing multiple criteria, to assist decision-makers.Design/methodology/approachThis paper suggests an approach as a tool to be used in the development of the Built Heritage Record for Mosul Old City. It proposes a D&C technique as an MCDM effectual tool for solving multifaceted problems of heritage significance assessment process in post-conflict cities, adopting the conservation of the traditional houses of Mosul Old City as a case study.FindingsThe research results showed the need for a method to solve the complexity of the decision-making problem regarding the process of determining the value and significance of heritage buildings and sites in the old city of Mosul, and how to develop a methodology to facilitate decision-making within databases related to such a multi-criteria issue.Research limitations/implicationsFor an effective judgment of the expert or participant in the evaluation process, and to contribute to the decision-making more objectively, exploiting digital management programs including an interactive user interface with the ability to share on the Internet is the focal next intention of the project.Originality/valueThe paper shows that by using specific software, a database for heritage places of the old city of Mosul could be generated to apply the proposed system. The expert can utilize the software to calculate and define the total value of the place automatically according to the entered data.

Recommendation Algorithm of Industry Stock Trading Model with TODIM

In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China’s A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets.

Multi-objective optimization of a hybrid micro-grid system for reducing grid dependency in the automotive industry: a case study of Iran Khodro company

ABSTRACT The global energy market is rapidly replacing fossil fuels with renewables, propelled by the pressure to implement a globally sustainable energy strategy before the environmental effects become irreversible. Hybrid microgrids, which can provide electricity from multiple renewable sources, could be a cost-effective and reliable solution for factories wishing to reduce their carbon footprint. This paper proposes a grid-connected hybrid micro grid system (HMGS) for an automobile manufacturing facility that can operate autonomously. This HMGS is optimized with HOMER Pro and MATLAB software using a particle swarm evolutionary approach. A multi-criteria decision-making process is conducted to select the best battery technology for storage. The optimal configuration obtained is a hybrid PV-wind-diesel-battery sharing grid system with 691 kW PV,101 wind turbines, a 500-kW diesel generator, and ten lithium-ion batteries. This configuration has an energy cost of 0.0769 $/kWh, a loss load probability of 4.045%, and a renewable fraction of 50%. A sensitivity analysis was also carried out to thoroughly investigate the effects of various system parameters on the final cost and system configuration. The findings suggest the automobile industry has a very high potential to increase renewable fraction by reducing grid usage, thereby lowering its carbon footprint.

Suspect face retrieval system using multicriteria decision process and deep learning

Suspect face retrieval system using multicriteria decision process and deep learning

Ensemble feature selection using q-rung orthopair hesitant fuzzy multi criteria decision making extended to VIKOR

ABSTRACT This paper investigates ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) process. A novel algorithm is proposed for the study of ensemble feature selection and it is called as q-rung orthopair hesitant fuzzy MCDM extended to the Visekriterijumska optimizacija Ikompromisno Resenje (VIKOR) (q-ROHFS VIKOR). This is the first time in the literature an ensemble feature selection problem is modelled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR technique. In the proposed method, every feature is ranked based on different available rankers and a preference matrix is obtained. In the sigmoidal transformation, the tuning parameter plays a vital role in the fuzzification process. This tuning parameter reduces the computation run-time in the fuzzification process for different high-dimensional datasets that are considered in this paper. By using q-ROHFS VIKOR method, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features from which the user can select the desired number of features. To prove the efficiency and optimality of the proposed method, the comparison with basic filter-based feature selections and ensemble feature selection using feature ranking strategy is obtained. The proposed method in this paper is superior and efficient than the ensemble methods based on the accuracy and F-score levels upto 0.96.

Experimental study on FDM 3d printed object & position analysis using multicriteria decision-making process

Nowadays additive manufacturing is a very unique process of making objects layer by layer. The additive manufacturing process has been improved, but the additive production process is not going much further than the subtraction manufacturing process. In this study, the integrated structure of multicriteria decision making analysis process for position selection analysis in additive manufacturing has been investigated. Primarily, measurable data was evaluated using a multi-standard decision-making process without the decision-maker's choice. In contrast, a decision maker's preferences are qualitatively analyzed using the analytic hierarchy process and multi-objective optimization on the basis of the ratio analysis technique. In this study, a special combination production process such as fused deposit modeling is used and this case study has shown a good agreement with experiment. Further, printing time of analytic hierarchy process and multi-objective optimization on the basis of the ratio analysis method has been compared in this study. Among various orientation, PLA_Position-2 has been identified as most efficient alternative.

Multi-Criteria Decision Making Analysis of Optimal Service Delivery Technique Using AHP

In this paper, we evaluate the most optimal service delivery method that can be implemented by both governmental and private sectors to an effective delivery service to customers. They include conventional service delivery, E-services (online), M-services (smart phones), and robotic process automation (RPA). Benefits and costs were considered in the evaluation process. Benefits were technical feasibility, efficiency, security, reliability, and innovation. The costs included complexity, human resources, facilities management, and maintenance costs. AHP was used to facilitate the multi-criteria decision-making process. Results show that RPA is the least expensive and most beneficial method to delivery services in UAE.

Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations

Manufacturing companies’ preparedness level against external and internal disruptions is complex to assess due to a lack of widely recognized or standardized models. Resilience as the measure to characterize preparedness against disruptions is a concept with various numerical approaches, but still lacking in the industry standard. Therefore, the main contribution of the research is the comparison of existing resilience metrics and the selection of the practically usable quantitative metric that allows manufacturers to start assessing the resilience in digitally supported human-centered workstations more easily. An additional contribution is the detection and highlighting of disruptions that potentially influence manufacturing workstations the most. Using five weighted comparison criteria, the resilience metrics were pairwise compared based on multi-criteria decision-making Analytic Hierarchy Process analysis on a linear scale. The general probabilistic resilience assessment method Penalty of Change that received the highest score considers the probability of disruptions and related cost of potential changes as inputs for resilience calculation. Additionally, manufacturing-related disruptions were extracted from the literature and categorized for a better overview. The Frequency Effect Sizes of the extracted disruptions were calculated to point out the most influencing disruptions. Overall, resilience quantification in manufacturing requires further research to improve its accuracy while maintaining practical usability.

Multi-criteria Decision-Making Techniques for the Selection of Pareto-optimal Machine Learning Models in a Drinking-Water Quality Monitoring Problem

Machine learning algorithms are valuable tools for solving a wide variety of complex engineering problems. Usually, those problems have multiple criteria to fulfill, but such machine learning-based solutions are usually optimized using a single criterion. In such instances, a multi-objective optimization-based approach could bring interesting solutions by determining a set of Pareto-optimal solutions with different trade-off. Therefore, a multi-criteria decision-making process must be carried out. To the authors’ present knowledge, multi-criteria decision-making is yet to be fully explored for selecting preferable Pareto-optimal machine learning models after the training step. Therefore, this paper proposes applying and comparing five different multi-criteria decision-making techniques for selecting a preferred machine learning model. Additionally, an ensemble-based framework is proposed to cope with the difficulty of selecting parameters for such techniques. Those tools are tested on a complex real-world drinking-water quality monitoring problem. Results based on the [Formula: see text] score indicate that via a multi-criteria decision-making process ([Formula: see text]), it is possible to select better solutions than single-criterion approaches ([Formula: see text]). Moreover, the proposed ensemble framework is able to mitigate the difficulty in defining preferences and regions of interest, achieving competitive solutions.

Optimal control of sewage treatment process using a dynamic multi-objective particle swarm optimization based on crowding distance

A variety of multi-objective optimization algorithms has been extensively investigated in the past decades to tackle the optimal decision of sewage treatment process. However, achieving the ideal solutions is challenging in the multi-criteria decision-making process from Pareto optimal sets due to the complicated relationships among influencing factors, especially in the case of large decision variables involved in the wastewater treatment process. We thus proposed an improved dynamic multi-objective particle swarm optimization algorithm based on crowding distance (DMOPSO-CD) to obtain global optimal solutions for the balance between energy consumption (EC) and effluent quality (EQ) in sewage treatment processes. The algorithm consists of optimization modules and a self-organizing fuzzy neural network, improving the global searching ability of particles, maintaining the diversity of non-inferior solutions, and solving the multi-objective vital issues in the optimization of sewage treatment process. The proposed optimization algorithm was applied to benchmark simulation model No.1, and the optimization results showed that the EC for wastewater treatment in dry, rainy, and storm weather was reduced by 7.87%, 6.28%, and 7.30%, respectively. This methodology outperformed several widely applied algorithms, including the multi-objective cuckoo search, non-dominated sorting genetic algorithm-II, and improving Pareto evolutionary algorithm in terms of EQ and EC, which opens a new window for the optimal decision of sewage treatment.

Entwicklung und Validierung potenzieller Strukturindikatoren zur Evaluierung des klinisch-infektiologischen Versorgungsangebots in deutschen Krankenhäusern während der COVID-19-Pandemie

IntroductionThis study describes the development and validation of structure indicators for clinical infectious disease (ID) care in German hospitals, which is important to adequately face the future challenges in ID medicine. MethodsA team of experts developed the structure indicators in a three-stage, multicriteria decision-making process: (1) identification of potential structure indicators based on a literature review, (2) written assessment process, and (3) face-to-face discussion to reach consensus and final selection of appropriate structure indicators. A field study was conducted to assess the developed structure indicators. A score based on the structure indicators was determined for each hospital and validated via receiver operator characteristic (ROC) curves using externally validated ID expertise (German Society of ID (DGI) Centre). ResultsBased on a list of 45 potential structure indicators, 18 suitable indicators were developed for clinical ID care structures in German hospitals. Out of these, ten key indicators were defined for the general and coronavirus disease 2019- (COVID-19-) specific clinical ID care structures. In the field survey of clinical ID care provision for COVID-19 patients in 40 German hospitals, the participating facilities achieved 0 to 9 points (median 4) in the determined score. The area under the ROC curve was 0.893 (95% CI: 0.797, 0.988; p < 0.001). Discussion/ConclusionThe structure indicators developed within the framework of a transparent and established development process can be used in the future to both capture the current state and future developments of ID care quality in Germany and enable comparisons.

Sustainability assessment of road pavement designs by integration of LCA and LCCA

ABSTRACT This study aimed to propose a framework for sustainable assessment of pavement by the integration of LCA and LCCA methods. Additionally, a tool was developed that employs a multi-criteria decision-making process to determine the most sustainable pavement design alternative. The developed tool was applied in a case study involving 496 weighing modes to assess the impact of different maintenance intervention levels and the discount and traffic growth rates on the sustainability of design alternatives with varying maintenance treatments. The results of the analysis revealed that asphalt pavement with a stabilised base demonstrated superior sustainability, covering over 87% of the weight modes. Conversely, concrete pavement exhibited lower sustainability, representing only 6.5% of the weight modes. Additionally, among maintenance activities, preventive treatments exhibited heightened sustainability in scenarios with higher discount rates and lower traffic growth. These findings highlighted the importance of selecting the appropriate pavement type for sustainable infrastructure development.

GIS-based multi-criteria decision-making techniques and analytical hierarchical process for delineation of groundwater potential.

The profound knowledge and management of groundwater resources is a prerequisite in order to ensure the sustainability of these resources. In this research, multi-criteria decision-making (MCDM) and analytical hierarchical process (AHP) method based on GIS were used to determine the groundwater potential recharge model of the Mornag plain (coastal area in North Tunisia). Influential eight factors were used in the groundwater recharge modeling: lithology, land use/land cover, hydrogeomorphology, elevation, rainfall, drainage density, lineament density, and soil. The influence of each factor was examined by the weighted linear combination method. The results reveal a very high to high groundwater recharge potential in the order of 20% of the total area. The validation of results by the histogram method showed that 41% of the total area corresponds to the moderate to very high recharge potential classes. The groundwater recharge potential model (GIS-MCDM-AHP) is useful in better management and planning of groundwater resources and implementation of wells and hydraulic structures in arid and semi-arid areas.

Crisp and fuzzy appraisal of tenderer’s qualifications in public works procurement in Turkey

PurposeIn Turkish public procurement practice of construction works, only bidders whose capabilities on each qualification criteria meet prescribed thresholds are taken into evaluation; however, final evaluation disregards contribution of contractor’s abilities to the delivery of project. The purpose of this study is to assign reasonable weights to qualification criteria stated in Public Procurement Law applicable for work contracts by the method of analytical hierarchy process (AHP) with crisp and fuzzy numbers and evaluate the results. Having optimum quality structures to obtain value for money and giving weight to environmental concern in tender evaluation stage can assist sustainable economy.Design/methodology/approachTo introduce qualifications of tenderers to evaluation process, a survey was conducted among 20 public procurement specialists at Public Procurement Authority of Turkey. AHP and fuzzy analytical hierarchy process (FAHP) methods were used to obtain the weights for qualifications.FindingsIn any method used in the study, experience became the most important criterion after bid price. Facility, machine and equipment criterion became the second and annual turnover criterion became the third most important criterion.Research limitations/implicationsThe set of qualification criteria used in this study is confined by Turkish Public Procurement Law and secondary regulations. However, as Public Procurement Law is in accordance with European Union directives, the conclusions of the study should not be considered to be geographically limited by the borders of Turkey. Also, professional to take part in the survey were randomly selected from the Public Procurement Specialist working at Public Procurement Authority.Social implicationsThe outcomes of the study may improve sustainability through public procurement.Originality/valueTo the best of the authors’ knowledge, this is the first study offering weights for tenderer’s qualifications to be used in a multi-criteria decision-making process for contractor selection in public work procurement process in Turkey.

Multi-criteria decision making and project portfolio management for transport infrastructure investment in Thailand

Transport project selection is always critical in government planning due to limited resources and diverse demands. Project portfolio management (PPM) was proposed in this study to suggest an optimal set of projects under predetermined constraints. With multiple conflicting criteria, comprehensive steps of multi-criteria decision making (MCDM) procedures were proposed in this study. The process begins with a step of pre-screening the projects and selecting the criteria for project evaluation. Sequential steps of: 1) weight determination process using the proportion method; 2) MCDM process using preference ranking organisation method for enrichment evaluations (PROMETHEE) II; 3) constraint consideration process using PROMETHEE V, was proposed to evaluate and select an optimal project portfolio. The study showed that decision making could be successfully done for the transport infrastructure investment with minimal complex calculation steps and questionnaire input from experts.

Influence of Selected Environmental Factors on the Business Type of Dentist's Practice in Germany: A Multi-Criteria Decision-Making Process With an Analytical Hierarchy Process.

In the interests of satisfying the dental services demands of German citizens area-wide, constant, and thoughtful planning of supply and demand is essential. With an anonymous online survey of 375 dentists a pairwise comparison of 9 factors extracted as relevant from the existing scientific literature were analyzed with an analytic hierarchy process (AHP) and ranked considering the various business types. In general, 5 local environmental factors have a dominant impact on founders' decision in German dentistry. In order: environment for the family, quality of life in private environment, real income, location of the practice, infrastructure. Real income is in first place (p = 0.287) for dentists who want to start a new single practice. For preferring a new community practice, it is on third place (p = 0.177) and for dentists who favor a takeover a single practice (p = 0.130) or joining a community practice (p = 0.096) or employment (p = 0.111) it is fourth place. For this purpose, the location of the practice is of greater priority than the real income for dentists who prefer not to start a new practice. The AHP method is a way to picture a priority list out of all relevant factors for setting up of a dental practice.

Prioritizing stakeholders to boost collaborative R&I projects benefits: an analytic network process approach

A methodology was developed to prioritize stakeholders of a collaborative research and innovation (R&I) project in the circular bioeconomy area, towards enhancing its benefits from a multi-perspective point of view. The concept of R&I project benefits was broken down into criteria, evaluating different attributes related to the project outputs and outcomes, to the project management processes, and to the social, environmental and economic dimensions. The devised methodology was based on a combination of the analytic network process multicriteria decision making method and the key benefit categories from the P5 standard for sustainability in project management. The P5 standard has been shown to adequately frame the benefits to stakeholders of R&I projects in the topic of circular bioeconomy. Key benefits identified by the experts relate to the categories "society and costumers" and "consumption". The following stakeholders should have priority in the development of the project stakeholder management plan: research team members, leaders at the consortium organizations, project management team members and environmental NGOs. Future research will include a longitudinal study of the perceived stakeholder and benefit categories priority over time

Science policy frameworks for a post-pandemic green economic recovery

As current production and consumption patterns exceed planetary boundaries, many leaders have stressed the need to adopt green economic stimulus policies in the aftermath of the COVID-19 pandemic. This paper provides an integrated multi-stakeholder framework to design an economic recovery strategy aligned with climate stabilisation objectives. We first employ quantitative energy and economic models, and then a multi-criteria decision process in which we engage social actors from government, enterprises and civil society. As a case study, we select green recovery measures that are relevant for a European Union country and assess their appropriateness with numerous criteria related to climate resilience and socio-economic sustainability. Results highlight trade-offs between immediate and long-run effects, economic and environmental objectives, and expert evidence and societal priorities. Importantly, we find that a ‘return-to-normal’ economic stimulus is environmentally unsustainable and economically inferior to most green recovery schemes.

Decision support system (DSS) for selecting sustainable insulation material using Pareto search and novel fuzzy-modified technique for order of preference by similarity to ideal solution (TOPSIS) approach

Efforts to improve the sustainability of the building construction sector have tended to focus on reducing operational energy cost, whereas sustainability considerations in the material selection at the design and construction phase have received less attention due to construction budget constraints. The present study proposes a decision support system (DSS) to assist decision-makers in accounting for sustainability in their construction material selections. We demonstrate how the developed DSS can be used to identify the most sustainable insulation materials and thicknesses among commercially available alternatives. The DSS ranks available alternatives by incorporating individual project information, material information, and the decision maker’s preferences. Technique for order of preference by similarity to ideal solution (TOPSIS) and Pareto search technique are combined in the methodology. By limiting the alternatives to the ‘Pareto front’ of life cycle assessment (LCA) and life cycle cost in a multi-objective optimization setting, we seek to reduce subjectivity in the multi-criterion decision-making process. Moreover, product-specific environmental product declaration is used to calculate the embodied energy for the LCA. The framework recommends commercially available materials and thicknesses accordingly. The proposed method is programmed in Python to establish a user interface for data input and output of results.

Picture fuzzy aggregation approach with application to third-party logistic provider selection process

Picture fuzzy sets (PFSs) are frequently composed of positive, neutral, and negative memberships and have the benefit of precisely capturing the preferences of decision makers (DMs). This paper introduces innovative picture fuzzy aggregation operators (AOs) based on fundamental operations, which have a number of advantages when dealing with real-world scenarios. Unpredictability and fuzziness coexist in decision-making analysis due to the complexity of the decision-making environment. ”Picture fuzzy numbers” (PFNs) outperform ”intuitionistic fuzzy numbers” (IFNs) when dealing with unclear input. This study proposes two aggregation operators (AOs): ”picture fuzzy hybrid weighted arithmetic geometric aggregation (PFHWAGA) operator” and ”picture fuzzy hybrid ordered weighted arithmetic geometric aggregation (PFHOWAGA) operator.” The proposed operators outperform the current PFN-defined operators. The proposed operator is utilised in the multi-criteria decision-making (MCDM) process to third-party logistic provider selection.