Multi-criteria Decision Research Articles

Comparative Sensitivity Analysis in Composite Material Selection: Evaluating OAT and COMSAM Methods in Multi-criteria Decision-making

The decision-making process is critical across various fields, influencing essential aspects like performance, safety, and sustainability. In engineering and materials science, selecting suitable materials is a complex process involving multiple interdependent criteria, which impact the reliability and lifecycle of a design. Traditional sensitivity analysis methods, such as the one-at-a-time (OAT) approach, assess the effects of individual parameter changes but often fail to capture the cumulative impact of simultaneous modifications. It can limit the effectiveness of decision tools in real-world scenarios where multiple factors vary concurrently. This study utilizes the Comprehensive Sensitivity Analysis Method (COMSAM) to address limitations in traditional sensitivity analysis by modeling simultaneous parameter adjustments across multiple criteria. Specifically, the study aims to validate COMSAM’s effectiveness in the practical problem of composite material selection, comparing it with the traditional OAT approach within a multi-criteria decision-making (MCDM) framework. By integrating both sensitivity analysis techniques with the Characteristic Objects Method (COMET), this research ensures robust evaluation and mitigates vulnerabilities like the rank reversal paradox. The findings demonstrate that COMSAM provides enhanced insights into the impact of parameter interdependencies, offering a more resilient foundation for decision-making in high-stakes environments where material properties and performance are paramount.

Analyzing New Quality Productive Forces in New Energy Vehicle Companies Based on a New Multi-Criteria Decision Analysis Model

Assessing the new quality productive forces (NQPF) of new energy vehicle (NEV) companies is crucial for promoting the sustainable development of the NEV industry. This paper systematically evaluated and analyzed the NQPF of Chinese listed NEV companies from 2018 to 2022 using a novel multi-criteria decision analysis (MCDA) model. To address limitations in traditional MCDA models, such as unbalanced weight distribution, insufficient ranking differentiation, and incomplete identification of key influencing factors, this study introduced a new model, IDOCRIW-PROBID (integrated determination of objective criteria weights—preference ranking on the basis of ideal-average distance). First, an evaluation index system tailored to NEV companies’ NQPF was developed. Then, the IDOCRIW method was used to objectively assign weights to the indicators, enhancing the scientific rigor of the weight distribution. The PROBID method was employed to rank companies based on their NQPF, identifying differences between them. Additionally, an obstacle degree model was introduced to analyze key influencing factors, compensating for the traditional MCDA model’s limitations in this regard. The results showed, first, that the proposed IDOCRIW-PROBID model has a high degree of consistency with the classical Entropy-TOPSIS (technique for order of preference by similarity to ideal solution) model in terms of ranking the results (correlation coefficient = 0.91), and that IDOCRIW-PROBID offers higher differentiation compared to other MCDA models, validating its reliability and superiority. Second, during the study period, the development levels of NQPF in Chinese listed NEV companies varied significantly, with most companies at a low level of development and showing a downward trend, indicating that companies face considerable challenges in improving their NQPF. Third, the obstacle degree analysis revealed that R&D lease fees, R&D depreciation and amortization, and direct R&D investment were the primary factors hindering NQPF growth. This research provides theoretical support and decision-making insights for strategic optimization in NEV companies and informs government policy formulation.

Design of a smart medical service quality evaluation system based on a hybrid multi-criteria decision model

As medical informatization continues to progress, the sophistication of smart medicine (SM) systems has led to a marked enhancement in the caliber of medical services provided. Nevertheless, the existing body of literature is sparse when it comes to frameworks for evaluating service quality, specifically within the domain of smart medicine (SM). Drawing on a hybrid multi-criteria decision-making model that fuses the best–worst method (BWM) with the VIKOR approach, this research has crafted an innovative framework for assessing the service quality of SM. BWM was used to obtain the weights of all dimensions and indicators under each dimension. Then, the service quality of hospitals H1, H2 and H3 in Xiamen was evaluated using the VIKOR method. The results showed that smart appointments, diagnosis and treatment are three important dimensions to evaluate SM service quality in medical institutions. The stability and robustness of the model were proved through sensitivity analysis. Findings suggest that hospitals can strengthen the construction of their appointment information platforms, the quality management of internal doctors, and the information connection between self-service terminals and information platforms to improve hospital service quality in the construction of smart medicine.

Sustainable Solutions for Co-Gasification of Sewage Sludge and Biomass: Insights from Aspen Plus, Response Surface Methodology, and Multi-Criteria Decision-Making Integration

This paper presents a novel framework integrating process simulation, optimization, and multi-criteria decision-making (MCDM) to identify sustainable co-gasification solutions for sewage sludge (SS) and biomass. Gasifier modeling in Aspen Plus, validated with experimental data, evaluated five feedstock blend routes. Sensitivity analysis was conducted on key gasification parameters, while Response Surface Methodology (RSM) optimized process routes by revealing complex relationships between variables and performance metrics. MCDM was applied to assess alternatives based on eight criteria covering environmental, economic, and social sustainability. The study introduced an integrated objective weighting method, leveraging data variations and correlations, alongside a vector-based ranking approach for prioritizing solutions. Results indicated that a biomass-to-sewage sludge ratio of 3:7, under co-gasification conditions of 649.72°C, 1.13atm, and a steam to feedstock ratio of 0.5, achieved the highest sustainability score of 0.506. This configuration outperformed other alternatives in terms of sustainability. Validation through three distinct analytic methods confirmed the robustness of the MCDM framework. In general, this work integrates simulation, RSM optimization, and MCDM analysis into a streamlined tool, offering a systematic progression from process simulation to decision-making. It reduces the reliance on costly trial-and-error experimentation while ensuring optimal operating conditions and comprehensive sustainability evaluation. These contributions tackle key challenges in data handling, optimization, and assessment, enabling more sustainable and cost-effective environmental technology innovations.

Extending multi-criteria coastal vulnerability assessment to low-lying inland areas: examples from Estonia, eastern Baltic Sea

The assessment of vulnerability to coastal hazards is a significant coastal management problem in regions with complicated shoreline, such as Estonia. This study implements the vulnerability assessment based on the multi-criteria decision analysis using fuzzy logic, analytical hierarchy process, and weighted linear combination (including input from experts) integrated with a geographical information system, to map the coastal vulnerability index (CVI) of the Estonian coasts at high resolution based on 16 parameters. The novelty of our approach is that we expand this assessment to a 2 km wide inland area that is an intrinsic but often overlooked part of coastal vulnerability estimates. The Estonian shores have mostly low and moderate vulnerability. Short segments with high vulnerability are impacted by severe waves and highly elevated water levels. The CVI also characterizes low-lying areas, such as large river valleys, reasonably well. Estimates of coastal vulnerability based on the three most important parameters according to experts’ judgements provide a reasonable approximation of the 16-parameter CVI in mostly homogeneous coastal regions, but less so elsewhere where its value is questioned. The results show that the application of the developed integrated decision support system, applied to a 2 km wide coastal strip, provides more information than single tools to assist coastal managers and stakeholders in planning, preparing for and responding to hazards.

Identifying WHO global priority endemic pathogens for vaccine research and development (R&D) using multi-criteria decision analysis (MCDA): an objective of the Immunization Agenda 2030

To date, global priorities for new vaccine R&D have not been systematically identified for endemic pathogens. As part of Immunisation Agenda 2030 (IA2030), we have systematically identified priority endemic pathogens for new vaccine R&D based on country and regional stakeholder values to address this need. MCDA surveys targeting policy makers and immunisation stakeholders in each World Health Organization (WHO) region were used to weight eight criteria for prioritisation. Applying those weights to regional pathogen data yielded regional top ten pathogen lists, which are intended to inform regional deliberations on R&D priorities. The regional top ten lists were combined into an IA2030 global priority list. To inform R&D, use cases for new vaccines and monoclonal antibodies were identified, then categorized in terms of the activities needed to accelerate progress. In five out of six WHO regions, Annual deaths in children under five and Contribution to antimicrobial resistance were the most heavily weighted criteria. How participants weighted the criteria was not associated with their region, biographical characteristics, or areas of expertise. Five pathogens were common priorities across all regions: M tuberculosis, HIV-1, K pneumoniae, S aureus, and Extra-intestinal pathogenic E coli. Six pathogens were priorities in single regions. Combining regional top ten lists provided a global list of 17 priority pathogens for new vaccine R&D. Thirty-four distinct use cases were identified for new products targeting these pathogens. While most are in the "Advance product development" category, ten are in the "Research" category and seven are in the "Prepare to implement" category. These priorities for new vaccine R&D will help stakeholders better respond to regional and country needs. The use cases will inform R&D and enable monitoring of R&D under IA2030. The work was funded by a Bill and Melinda Gates Foundation grant to WHO (INV-005318).

Multi-Criteria Decision Analysis using GIS in Assessing Suitability for a Solar-Powered Biomass Briquetting Plant in the Gambella Region, Ethiopia

Multi-Criteria Decision Analysis using GIS in Assessing Suitability for a Solar-Powered Biomass Briquetting Plant in the Gambella Region, Ethiopia

Mapping the Potential of Managed Aquifer Recharge in Africa: GIS-based Multi-Criteria Decision Analysis Approach

Africa faces numerous challenges related to rainfall variability, droughts, water scarcity, and climate change. Managed Aquifer Recharge (MAR)- groundwater recharge and underground water storage for later use or environmental support presents a viable alternative for water storage and may provide an effective tool for coping with such challenges. However, the potential area where MAR can be feasibly implemented has not been identified. This study mapped MAR feasibility using a Geographic Information System-based Multi-Criteria Decision Analysis (GIS-MCDA) and assessed MAR potential in Africa. The methodology focused on three key pillars of MAR feasibility: intrinsic suitability based on biophysical parameters, water source availability, and water demand. Maps responding to these pillars were developed and combined to create a composite MAR feasibility map. Results show that 18% of the continental area falls into the low feasibility class, 73% into the moderate feasibility class, and 7% into the high feasibility class. The feasibility map was validated against 17 existing MAR schemes in Africa, demonstrating a good correlation between their locations and areas with MAR potential. Results of sensitivity analysis of criteria weights of the biophysical parameters show that geology is the most influential criterion, followed by slope. In general, this first feasibility assessment shows good potential for MAR implementation in Africa. Therefore, MAR should be considered prominently among other water storage options for resilience building in Africa and policymakers should ensure adequate resource allocation for its implementation. The feasibility map can be used to guide MAR planning and investment decisions.

A multi-objective production scheduling model and dynamic dispatching rules for unrelated parallel machines with sequence-dependent set-up times

This study presents a production scheduling for unrelated parallel machines with machine and job sequence-dependent setup times. The system performance measures to minimize include makespan, total tardiness, and number of tardy jobs. The aim of the study is to develop a solution methodology that can solve the problem for the large scale. First, the problem is formulated as a mixed-integer linear programming model. The augmented ɛ-constraint method is applied to find Pareto solutions for small problem instances. The purpose is to demonstrate that Pareto solutions, which balance the trade-offs among three measures of performance, can be found for these instances. Dispatching rule-based heuristics is developed to solve large problem instances. The heuristics feature three dispatching rules that are designed to handle the dependent setup time. In addition, these rules are combined into six variants using a time-based rule-switching mechanism. The heuristics are tested with 18 problem instances, containing 244 to 298 jobs, in two demand scenarios derived from the monthly demand data from an industrial user. Under each demand scenario, a set of heuristics that provides the best performance with respect to the three measures is identified. The heuristics include combinations of the shortest completion time and due date-based rules. Finally, a multi-criteria decision-making analysis is performed to determine the conditions specified by the weight given to each measure, with which one heuristic is preferred over the others.

A Fermatean Fuzzy SWARA-TOPSIS Methodology based on SCOR model for Autonomous Vehicle Parking Lot Selection

Population growth in crowded cities and the resulting increase in vehicle use have led to the problem of insufficient parking. When public parking lots and urban growth are not in coordination, vehicles park on the street and close the crosswalks. In the coming years, this problem will become more complicated with the addition of autonomous vehicles (AVs) to urban traffic. This study addresses the research question of how to effectively select AV parking lots in urban areas experiencing population growth and increased vehicle usage. For this aim, a hybrid Multi-Criteria Decision Making (MCDM) methodology, combining SWARA (Step-wise Weight Assessment Ratio Analysis) and TOPSIS (Technique for Order Preference by Similarity) approaches in a Fermatean Fuzzy (FF) environment is proposed. The decision hierarchy based on the SCOR model has been developed to determine and construct the evaluation criteria. Then, a case study analysis has been applied to selected districts in Istanbul, which is Turkiye's most populous and developing city. Operating expenses, safety and security, and land costs are determined as the most important factors. As a result of the detailed fuzzy analysis, which districts should primarily be chosen for AV parking lots in Istanbul is determined and finally, the robustness and validity of the results obtained by the sensitivity analysis being questioned. The study contributes by providing insights into AV parking lot selection, demonstrating the efficacy of the proposed methodology, and highlighting the importance of addressing this issue in urban planning.

Selecting sustainable packaging materials and strategies: A holistic approach considering whole life cycle and customer preferences

With the increasing public awareness of packaging and the environmental impact caused by it, companies are driving their businesses to introduce and promote practices that help ease the negative impact of packaging on the environment. Studies done on consumer behaviour show that decisions nowadays are influenced by sustainability. Yet, companies struggle with changing practices and justifying adopting environmentally friendly packaging materials. The current paper presents a holistic approach, addressing the need for identifying, assessing, and selecting sustainable materials for packaging applications. The Kano model prioritises features that are then used with Ashby plots for identifying alternative materials. Life cycle assessment, as well as life cycle costing, are used for assessing the environmental and financial impact of the use of such materials. Automatically weighted Multi-Criteria Decision-Making Analysis (MCDA) is used to select the most appropriate packaging material. This methodology provides a combinatorial ranking for each criterion set by four pre-defined weight distributions for Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). The proposed methodology allows for a systematic and holistic design process when deciding on packaging materials for any product. The methodology is validated for the case of packaging weather-sensitive cast stone products and carbonated drinks. In cast stone, three packaging materials are ranked for pallets based on their attributes. Similarly, for carbonated drinks, three packaging materials are recommended at the end of the study.

Performance assessment of public transport networks: an AHP-ANP approach

Multi-criteria decision-making (MCDM) methods have been extensively employed by researchers in the public transport (PT) sector. While many studies utilize a single MCDM method, employing multiple MCDM methods can achieve more robust results. Furthermore, integrated MCDM methods have the potential to address the weaknesses and limitations inherent in single MCDM method. In this study, we address the limitations of single MCDM methods by integrating the analytic hierarchy process (AHP) with the analytic network process (ANP) to evaluate public transport networks performance. Based on the established public transport criteria matrix AHP model, we identify interdependencies among criteria. The AHP-ANP method is then applied to assess public transport networks in three case study cities in Australia. The sensitivity analysis conducted on both the AHP and integrated AHP-ANP approaches highlights the influence of criteria interdependency. This research contributes to mitigating the limitations of AHP method and applies AHP-ANP model to provide decision-makers with insights to enhance PT network performance and achieve smart PT network management.

A three-stage framework for optimal site selection of hybrid offshore wind-photovoltaic-wave-hydrogen energy system: A case study of China

The integration of offshore wind-photovoltaic-wave-hydrogen production is a sustainable energy development concept that could smooth the intermittency of renewable energy generation, reduce energy abandonment, and improve energy efficiency. However, the successful deployment of a hybrid offshore wind-photovoltaic-wave-hydrogen energy system is premised on the selection of an appropriate location, which necessitates a robust framework to support this decision. This study proposes a three-stage decision framework based on Geographic Information System and multi-criteria decision-making to determine the optimal location from macro and micro perspectives. To ensure the effectiveness of site selection, it develops a comprehensive criteria system, including exclusion criteria and evaluation criteria, encompassing geographical, resource, economy, risk, and sustainable potential aspects. Then, the spatial simulation of alternative sites is conducted according to exclusion criteria to rule out infeasible alternative sites. The multi-criteria decision-making in the third stage is performed in a rough-fuzzy environment to eliminate the uncertainty in decision-making. Finally, a case study is conducted on Hainan Island, China. The results show that the western sea area of Dongfang City is the optimal location to deploy the hybrid offshore wind-photovoltaic-wave-hydrogen energy system. The proposed framework offers a reference for practical offshore renewable energy project implementation.

An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas

The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.

Supporting sustainability assessment of building element materials using a BIM-plug-in for multi-criteria decision-making

The environmental crisis requires the immediate implementation of accurate and robust sustainable solutions throughout the building life cycle. Life Cycle Sustainability Assessment (LCSA) is a scientifically recognised method that integrates the triple dimensions of the life cycle approach, and thereby enabling the evaluation of the performance of mitigation strategies implemented in building projects. However, implementing the LCSA in buildings is limited by the weighting of environmental, economic, and social dimensions to select the best option regarding the numerous materials available. In order to fill this knowledge gap, the paper aims to present the developed Smart BIM3LCA tool, which supports multi-dimensional assessment during the project´s early design steps. Automatic integration of the LCSA, a multi-criteria decision-making tool such as TOPSIS, and building information modelling (BIM), was developed to support the selection of building materials. The BIM plug-in was then validated through its application to a multi-family residential building to select the most sustainable materials during the project´s early design stage.

Integrated fuzzy decision-making methodology with intuitionistic fuzzy numbers: An application for disaster preparedness in clinical laboratories

Society is on constant alert due to the increasing frequency and severity of Seasonal Respiratory Diseases (SRDs), posing significant challenges from both a humanitarian and public health perspective. The recent COVID-19 pandemic has tested the capacity of clinical laboratories to address seasonal infections, epidemic outbreaks, and critical emergencies. This scenario has led to operational burdens, primarily from resource limitations, a lack of proactive planning, and the low adaptation to unforeseen circumstances. Coupling different data-driven approaches considering multi-criteria weighting, interdependence assessment, and outranking are critical for devising effective interventions upgrading the operability of clinical labs during SRDs. Nonetheless, a deep literature review revealed there are no studies using these hybridized approaches when addressing this problem. Consequently, this article proposes the application of an innovative hybrid Multicriteria Decision-Making (MCDM) methodology that integrates the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL), and Combined Compromise Solution (CoCoSo) to assess the disaster preparedness of clinical laboratories during SRDs. Initially, we applied IF-AHP to assign the relative weights to criteria and sub-criteria, considering the inherent hesitation and uncertainty in decision-making. Subsequently, IF-DEMATEL was utilized to analyze the interrelationships between criteria, providing insights into the interrelations among clinical lab disaster management drivers. Finally, the CoCoSo method was applied to estimate each lab’s Preparedness Index (PI) and detect response gaps when coping with SRDs. The suggested methodology was validated across nine clinical laboratories in Colombia during the most recent respiratory pandemic. This study contributes to the healthcare sector authorities by identifying key criteria and sub-criteria affecting the response of clinical labs, the elicitation of main response drivers in clinical labs when facing SRDs, and the calculation of a multidimensional indicator representing the preparedness of the labs. This work also enriches the literature by applying the IF-AHP, IF-DEMATEL, and CoCoSo approach to a challenging case study requiring a multi-method data-driven application. Furthermore, it suggests future directions to improve the proposed framework in other related contexts.

Emergency water supply during disasters in Iran: a multi-criteria group decision analysis

Emergency water supply during disasters in Iran: a multi-criteria group decision analysis

Prediction of eco sustainability component using fuzzy Z numbers based ratio analysis and interval type 3 fuzzy logic system

Amid rapid global urbanization, understanding eco-sustainability challenges in megacities like Delhi is crucial both locally and globally. This study introduces a novel multi-criteria group decision-making approach, the Z-Fuzzy Step-Wise Weight Assessment Ratio Analysis (SWARA), to address these complex challenges. This method prioritizes key eco-sustainability components like air quality index (AQI), water quality index (WQI), Temperature, and Precipitation, based on their impact, aiming to enhance resource allocation and environmental quality. Criteria weights are determined using fuzzy analytical hierarchical processing with Z-numbers, which manage decision-makers' uncertainty through a reliability function, illustrating their confidence levels and supporting the adaptability and effectiveness of eco-sustainability measures. A case study is conducted on seven sub-cities in Delhi examining their unique environmental impacts and urban sustainability challenges. Moreover, the most effective component of eco-sustainability say AQI is also dependent on some factors like PM10, PM2.5, SO2, O3, NO2, and CO. Considering these factors as inputs, an interval type-3 fuzzy logic system (IT3FLS) has been constructed to predict AQI, supporting timely interventions and improvements in eco-sustainability management. The IT3FLS model demonstrated exceptional precision across all Delhi sub-cities, with low root means square error (<0.05), and high R2 values (>0.94), affirming its effectiveness. To validate the Z-Fuzzy SWARA rankings, we compared them with existing Z-Fuzzy methods, consistently ranking AQI first. The IT3FLS's AQI predictions were also compared with those from fuzzy logic and advanced machine learning algorithms, demonstrating superior performance. Finally, the study underscores the managerial implications, emphasizing their role in guiding decision-making and strategy implementation.

Where to plan shared streets: Development and application of a multicriteria spatial decision support tool

In response to the growing recognition of the vital role played by streets as public spaces in enhancing the vibrancy of urban life, various concepts aiming at creating greener and more inclusive streets have gained popularity in recent years, especially in North America. Shared streets are one example of such concepts that have attracted the attention of citizens and of urban and transportation planning professionals alike. This was the case in the city of Sherbrooke (Quebec, Canada) where, in response to numerous citizens’ requests, a need was identified to develop decision aid tools to help evaluate and rank street segments based on their potential to become shared streets. To achieve this, an action-research project was initiated in which we conducted a socio-technical process based on MACBETH, a multicriteria evaluation method. The project led to the development of a spatial decision support tool, operationally used today by the city professionals. This tool ensures a more informed and transparent decision-making process and supports shared streets planning policy. The methods developed are generalizable and can be adapted to other cities facing similar planning problems.

A novel Sigma-Mu multiple criteria decision aiding approach for mutual funds portfolio selection

A Sigma-Mu approach is proposed for mutual funds portfolio selection. The mean and variance of the overall performance of each asset are considered, according to an additive aggregation model, subject to weights’ preferences provided by the decision maker. These preferences concern two independent sets of weights, i.e., those pertaining to the investment indicators and those pertaining to the time periods associated with the estimation of the indicators. For the first time in the Sigma-Mu framework, a weighting matrix is exploited, assisting on the development of a method to appraise the sources of variance, due to the weighting scheme of either the indicators or the periods. The Mu's, Sigma's and covariances estimated according to the Sigma-Mu approach, enter as inputs to mixed-integer quadratic programming (MIQP) mean-variance portfolio optimization models, in order to implement an empirical testing procedure, for a period of 8 years. The underlying MIQP models are equipped to consider non-convex investment policy constraints, such as the number of securities to be included in the portfolio, specific binary buy-in thresholds, the desired exposure of the portfolio to each investment advisor etc. The dataset that has been chosen for the empirical testing includes European mutual funds, that offer a broad exposure to the whole span of investment strategies and styles. The results document that the suggested approach may effectively be utilized in mutual funds investment management, since the portfolios constructed by the suggested methodology are associated with superior absolute and risk-adjusted performance against benchmarks.