Multi-criteria Decision Support Model Research Articles

Multi-criteria decision support model for selecting the appropriate construction management firm

Construction management (CM) companies serve as representatives for clients, aiding them in overseeing construction projects throughout the project’s entire life cycle. Opting for the right CM firm enhances the likelihood of a successful project completion. Existing research has highlighted a lack of decision support models for CM firms. This study introduces a multi-criteria decision support model employing the Analytic Hierarchy Process (AHP) for the selection of suitable Construction Management firms. The primary objective of this model is to guide decision-makers in choosing the most suitable CM firm, ensuring that the provider offers the necessary services from project initiation to closure. Additionally, this research seeks to standardize the CM firm selection process based on key criteria, which were identified through a literature review. The significance of these criteria was assessed via a survey distributed to construction professionals in the United Arab Emirates (UAE). The survey collected feedback by having respondents make pairwise comparisons of the identified criteria. The data, consisting of 52 survey responses, were analysed using Expert Choice software and utilized to construct the AHP-based model. The findings reveal that the most crucial factors for CM firm selection are prior experience in similar projects, financial stability, proficiency in Building Information Modelling (BIM), the qualifications and capabilities of key staff members, ongoing project experience, and expertise in risk management. The study also presents a four-stage framework that decision-makers can follow when selecting the appropriate CM firm, and it includes a case study to illustrate the application of the proposed model.

Multi-criteria decision support model for material and supplier selection in the construction industry

Existing material and supplier selection practices in the construction industry are time-consuming and error-prone due to reliance on human judgment and personal communication with a limited number of suppliers. To address this problem, the present paper focuses on developing a new Material and Supplier Selection Model (MSSM) to identify the best selection of construction materials and suppliers based on: (1) unique characteristics of construction projects such as project location, quantity of work, and material/product specifications; (2) characteristics of suppliers and vendors such as price, delivery time, and quality; and (3) characteristics of materials such as performance, and environmental impacts. The model is integrated with updatable databases of materials and suppliers that include: (1) suppliers’ data such as name, location, and lead time; and (2) materials data such as model, specifications, and costs. The developed MSSM is implemented using the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method due to its capability of executing computations for problems containing both qualitative and quantitative criteria. An example project for material and supplier selection is used to demonstrate the MSSM use and document its new capabilities. The present model is expected to support contractors, construction managers, and decision-makers in selecting the best suppliers and materials based on specified criteria such as cost, time, and environmental impacts.

A Multi-Criteria Decision Support Model for Restaurant Selection Based on Users' Demand Level: The Case of Dianping.com

The Internet, by offering a variety of information sources such as online reviews, aids people in selecting restaurants. However, it also prolongs their decision-making process due to the need to integrate information across multiple criteria. Existing decision support models for choosing satisfactory restaurants overlook users' varying demand levels for each aspect of the restaurant, making the process less efficient. This paper aims to develop a multi-criteria decision support model for users to efficiently and accurately rank and select restaurants based on their demand level for various restaurant aspects. The 2-tuple linguistic ordered weighted averaging (2LOWA) aggregation operator is applied for the first time to aggregate user ratings, generating linguistic ratings that mirror the diverse levels of user demand for restaurant service, food, and environment. The importance weights (IW) method is introduced to calculate linguistic weights, thereby obtaining customized 2T-SFE composite scores under various user demand scenarios. The proposed model's applicability is demonstrated using a dataset comprising over 3.7 million reviews sourced from Dianping.com. The results show multiple personalized restaurant rankings with more linguistically understandable composite scores, enabling users to efficiently choose a suitable restaurant based on their preferences and requirements. Moreover, a list of restaurants satisfying most users with different demand levels can be generated by assessing their frequency of appearance in the top 10 restaurants across over 340 scenarios established by the proposed model. This contributes to offering more reliable and comprehensive restaurant recommendations and rankings, ultimately increasing customer satisfaction in the selection process.

Analyzing the Attractiveness of Businesses to Receive Investments for a Creative and Innovative Transition to a Circular Economy: The Case of the Textile and Fashion Industry

Excessive and often compulsive consumption has given the textile–fashion industry the reputation of being one of the industries causing the most pollution in today’s world. For this reason, there is a necessity for a transition from a linear to a circular approach in the textile–fashion industry. However, this is not an easy task, especially when considering the investments that must be made to put a circular economy structure into practice. In this sense, the transition to a circular economy in the textile–fashion industry presents a unique opportunity for businesses to attract investments to support this transition by leveraging creativity and innovation to reduce waste, minimize resource consumption, and increase the longevity of products and materials. Therefore, this study sets out to develop a multicriteria decision support model to measure the attractiveness of businesses to receive investments that aim at aiding the transition to the circular economy. The model uses the “play card” from Simos’ procedure and the Normalize software that provide a comprehensive, consistent, and transparent approach to decision making, which can help investors to evaluate the attractiveness of investment opportunities and identify businesses that have the potential for long-term success in the circular economy. Hence, catalyzing and obstructing factors of the circular economy discussed in the literature were selected to underpin the analysis model and to draw up robust investment recommendations to the investors. In addition to the scientific contributions of the model, indications are also provided to the private sector, public policy makers, and society on how sustainability can be driven by the circular economy.

Measuring sustainability, resilience and livability performance of European smart cities: A novel fuzzy expert-based multi-criteria decision support model

Cities of the 21st century are buffeted with challenges, leaving potentially serious consequences on the future of urban living. Smartening development in cities has reinvented hopes of melting down predicaments in the early 2000s'. However, perplexed by the intensifying complexities of smart cities, urban living in smart cities needs to be evaluated with multiple conflicting criteria. Multi-criteria-based evaluations have been an answer to this case when attempting to gauge the composite performance of multiple decision-making entities. Several multi-criteria assessment techniques exist when dealing with selection problems. Nonetheless, the vagueness associated with the methodologies accompanied by uncertainties and complexities is inevitable in multi-attribute assessments. Fuzzy-based multi-criteria models are often an answer to such uncertainties when modelling real-world problems. The study thus presents a novel fuzzy expert-based multi-criteria decision support model, where the Analytical Hierarchy Process (AHP) is combined with the Evaluation based on Distance from Average Solution (EDAS) approach under a spherical fuzzy environment to create a composite index for comprehensive performance monitoring. The case of 35 high-tech European cities was used to empirically validate the proposed novel approach and thus construct a composite index. The composite index considers the intricate facet of integrating the concept of smart cities with sustainability, urban resilience, and livability under a unified framework. The fuzzy c-means partitioning technique was then used to segment smart cities into high, medium, and low-performing classes. A comparative analysis considering several distance-based approaches under a fuzzy environment with the SF-AHP and EDAS methodology is conducted to validate the robustness and stability of the proposed novel decision support model. The results revealed London as the top-ranked smart city that promotes sustainability, resilience, and livability in its current urban development model. Dusseldorf, Zurich, Munich, Oslo, Dublin, Amsterdam, Hamburg, Rome, Moscow, and Stockholm were no exemption from addressing the tritactic goals of sustainability, urban resilience, and livability well into their urban development plan and were placed in the high-performance cluster. The proposed model is efficient to express decision makers' preferences in a larger space and modelling functional parameters including hesitancy independently in the 3-dimensional domain. The model supports decision-makers and relocation analysts to assess the performance of smart cities and set targets to improve performance to remodel urban development to a more sustainable, resilient, and livable pattern.

A novel normal wiggly hesitant fuzzy multi-criteria group decision making method and its application to electric vehicle charging station location

Electric vehicle charging station location (EVCSL) is critical to promoting the electric vehicle industry and how it is evaluated is typically a MCDM issue. In this paper, an applicable multi-criteria group decision support model is developed to evaluate the electric vehicle charging station location. To do this, we used normal wiggly hesitant fuzzy set to describe the fuzzy decision-making information, which can further dig the potential uncertainty. Considering that the experts’ preference can lead to significant changes in the results, k-means clustering method is used to group the experts in different fields. Moreover, the improved correlation coefficient and standard deviation (CCSD) method is used to determine the criterion weights, and considering that prospect theory can reflect the mental behavior of experts and the simplicity and efficiency of weighted aggregated sum product assessment (WASPAS) method, a combined ranking model is proposed to evaluate the alternatives comprehensively. The procedure of the model is given in detail, and the example of electric vehicle charging station location evaluation is employed to demonstrate our results. Comparative analyses and discussion show that the proposed model has powerful effectiveness, performs better than other existing methods, and largely extends the applicability of the method with theoretical and practical implications.

A Framework for COVID-19 Pandemic Intervention Modelling and Analysis for Policy Formation Support in Botswana

The purpose of this research was to develop a methodological framework that could be applied for policy formation in situations having a high level of uncertainty and heterogeneity of existing opinions among involved stakeholders about risk mitigation and management such as COVID-19 pandemic risk. In this paper, we present such a framework and its application for policy decision-making in Botswana for mitigating the COVID-19 pandemic. The purpose of the proposed model is twofold: firstly, to supply decision-makers with reliable and usable epidemiologic modelling since measures to contain the spread of the COVID-19 virus were initially to a large extent based on various epidemiologic risk assessments. Secondly, given that some sets of measures adopted in other parts of the world were progressively imposing high or even very high social and economic costs on the countries which adopted these measures, we provided a multi-criteria decision support model which could be used in order to weigh different policy approaches to combat the virus spread taking into consideration local impact assessments across a variety of societal areas. We describe how the formulation of a national COVID-19 strategy and policy in Botswana in 2020 was aided by using ICT decision support models as a vital information source. Then we present the virus spread simulation model and its results which are connected to a multi-criteria decision support model. Finally, we discuss how the framework can be further developed for the needs of Botswana to optimise hazard management options in the case of handling COVID-19 and other pandemic scenarios. The significant research contribution is on advancing the research frontier regarding a methodology of including the heterogeneity of views and identification of compromise solutions in policy-relevant discourses under a high degree of uncertainty.

Classification of medicines and materials in hospital inventory management: a multi-criteria analysis

In the hospital environment, to achieve an optimum level of operations and service, it is necessary to develop adequate inventory management system. Stocks can be managed, amongst other ways, through inputs classification, which is generally carried out based on a single criterion, such as monetary value, demand or criticality, which does not fully address the complexity of a hospital's inventory management system. Thus, the present study proposes a multi-criteria decision support model to help classify the stock of medicines and materials, enabling a more effective inventory management system for hospitals. Methodologically, the study followed 3 stages: (1) preliminary phase; (2) modelling and choice phase; and (3) finalization phase. Each stage had a set of specific steps that were followed. The first stage identified the actors of the process, objectives, criteria and alternatives, establishing 5 criteria and 48 alternatives; the second stage was the choice and execution of the multi-criteria decision method to solve the problem. It was decided to use the Flexible and Interactive Tradeoff method for the sorting problematic. Finally, in the third stage, the sensitivity analysis for the developed model and the validation of the results with decision makers were carried out. In the study, 48 medicines and materials were included to validate the proposed model; however, the model could be used for more items. From the total of 48 medicines and hospital medical materials selected for the study, the classification of 34 of these alternatives to a single class was obtained through modelling and the other 14 alternatives were destined to two possible classes; moreover, the sensitivity analysis performed showed robust results. The items classified in class W should receive special attention by the stock manager; therefore, they should be monitored weekly. Items classified in class B should be monitored biweekly and finally, items classified in class M, should be monitored monthly. The classification of medicines and materials developed according to the inventory demands allowed more efficient purchasing decisions, optimizing the stock of materials and medicines at the hospital while optimizing the inventory manager's activities, saving time. Consequently, the proposed model can support the development of other multicriteria models in different hospital scenarios.

Siting charging stations and identifying safe and convenient routes for environmentally sustainable e-scooter systems

E-scooters are considered a new type of micromobility that promotes sustainable transportation modes by shifting the private car mode to a Mobility as a Service. Irregular parking and high life cycle global warming impacts of e-scooters due to dockless operating system cause a tendency from dockless systems to charging stations. This article presents one of the first comprehensive spatial analysis studies aiming to develop a novel GIS-based multi-criteria decision support model for: (1) siting optimum e-scooter charging station locations that will integrate e-scooter system with the existing public transportation systems and point of interests, and (2) finding the most secure and convenient road network to connect the charging stations. Identifying the optimum road network for connecting the charging stations and developing a raster based spatial method for this purpose distinguish this study from the previous studies. The developed model was implemented by performing a case study in Karsiyaka, Izmir, Turkey. 35 suitable locations were determined for siting e-scooter charging stations and the optimal connectivity network connecting each station with its neighboring stations were identified. The model can be reproduced for other locations and used to develop innovative policies and plans for environmentally more sustainable and operationally effective shared e-scooter systems.

EVALUATING ECONOMIC FREEDOM VIA A MULTI-CRITERIA MEREC-DNMA MODEL-BASED COMPOSITE SYSTEM: CASE OF OPEC COUNTRIES

Economic freedom indicators create a beneficial and suitable guide and a crucial reference for investors, policymakers, lenders, and market researchers worldwide. In light of these indicators, the economic freedom performances of countries can be determined. The Heritage Foundation annually releases a ranked list of the country based on their performance in terms of fourteen economic freedom criteria with equal weights through a simple aggregation approach. According to an average-based aggregator, equal weight of economic freedom criteria and calculating rank of countries cannot be a completely reliable approach. Thus, this work establishes a composite index system in the form of a decision support system that employs the method based on the removal effects of criteria (MEREC) and the double normalization-based multi-aggregation (DNMA) to specify the economic freedom levels of the OPEC countries. MEREC obtains the importance weights of indicators without the interference of any stakeholder or decision-makers. Afterward, DNMA, as a novel ranking multi-criteria method, is applied to sort countries based on their performance against all economic freedom criteria. This is the first attempt in the literature to calculate the index of economic freedom utilizing an integrated multi-criteria decision support model. Whereas “investment freedom” is the most significant indicator of economic freedom, the UAE is in the best position in terms of economic freedom among OPEC countries. A fourphased sensitivity control is also performed so as to verify the robustness and usefulness of the developed decision tool.

COVID-19 vaccine distribution: exploring strategic alternatives for the greater good

The dire state of the COVID-19 pandemic crisis symbolized the urgency for efficient distribution and administration of vaccines to combat the virus as the most urgent public health service. This paper presents a prototype multi-criteria decision support model based on goal programming that can effectively support vaccination plans for the greater good of society. The optimization goals of the model include minimizing the number of fatalities and risk of spreading the disease, while complying with government health agency’s priority guidelines for vaccination. This study applied the model to a real-world dataset to demonstrate how it can be effectively applied as a decision support tool for vaccine distribution plans and manage future pandemics.

Model to support intervention prioritization for the control of Aedesaegypti in Brazil: a multi-criteria approach

BackgroundDespite continuous strategic investments to mitigate the complexity involving arboviruses control, it is still necessary to further research methods and techniques to achieve in depth knowledge and shorter response times in the application of intervention activities. Consequently, the current work focused its efforts on the development of a multicriteria decision support model for the prioritization of prompt response activities for Aedes aegypti control, based on a case study in the city of Natal/RN.MethodThe research was carried out in three stages: a) preliminary; b) modelling and choice; and c) finalization; the second stage was made possible by the Flexible and Interactive Tradeoff (FITradeoff) method for ranking problematic. Furthermore, the research encompassed ten actors who were involved in the model construction, eight internal and two external to the Natal Zoonoses Control Center (ZCC-Natal) as well as the observation of four operating scenarios for arboviruses control, based on transmission levels; and, evaluation of eleven alternatives from six different criteria perspectives.ResultsRankings of the interventions evaluated in each of the four control operation scenarios present in the city of Natal/RN were obtained, considering technical criteria guided by the Pan American Health Organization (PAHO).ConclusionsAs a result, it was developed a structured decision-making model that could help decision makers to minimize the effects and risks associated with the proliferation of the vector.

A multi-criteria decision support model for adopting energy efficiency technologies in the iron and steel industry

A multi-criteria decision support model for adopting energy efficiency technologies in the iron and steel industry

A GIS-based multi-criteria decision support model for planning municipal solid waste collection points: A case study of Çağdaş Neighbourhood, Çiğli District, Izmir, Turkey.

This study aims to develop a geographic information system (GIS)-based multi-criteria decision support model to create optimal plans for locating municipal solid waste (MSW) collection points. The model performs a series of consecutive GIS-based spatial analyses to determine alternative plans. Then, it weighs the alternatives considering the social, economic and environmental criteria to determine the optimum solution through analytical hierarchy process. The model was implemented as a case study in Çağdaş neighbourhood of Izmir, Turkey. A total of 42 locations were determined as the optimum collection points out of 245 possible collection points, which yields 39% reduction in collection points compared to the existing system. Total number of waste bins and average walking distance to waste collection points were calculated as 129 and 33 m, respectively. The municipal authority would spend 48.79 $ day-1 on fuel for waste collection and transport. In addition, daily air pollutant emissions generated during the operations were estimated as 2.052 g CO, 0.231 g NMVOC, 8.409 g NOx, 0.954 g N2O, 0.260 g NH3, 0.000227 g Pb and 0.0231 g PM 2.5. The results indicated that 14 out of 69 collection points in the existing collection system were not allocated to any waste source geographically. This study presents a unique method for planning MSW collection points on two key aspects: (1) development of a novel method to determine all possible collection point locations using Thiessen polygons and (2) presenting a holistic planning method considering the impacts of the collection system on the waste generators and waste collectors.

Similarity Analysis of Methods for Objective Determination of Weights in Multi-Criteria Decision Support Systems

Decision support systems (DSS) are currently developing rapidly and are increasingly used in various fields. More often, those systems are inseparable from information-based systems and computer systems. Therefore, from a methodical point of view, the algorithms implemented in the DSS play a critical role. In this aspect, multi-criteria decision support (MCDA) methods are widely used. As research progresses, many MCDA methods and algorithms for the objective identification of the significance of individual criteria of the MCDA models were developed. In this paper, an analysis of available objective methods for criteria weighting is presented. Additionally, the authors presented the implementation of the system that provides easy and accessible weight calculations for any decision matrix with the possibility of comparing results of different weighting methods. The results of weighting methods were compared using carefully selected similarity coefficients to emphasise the correlation of the resulting weights. The performed research shows that every method should provide distinctive weights considering input data, emphasising the importance of choosing the correct method for a given multi-criteria decision support model and DSS.

Evaluating the efficiency of membrane’s refurbishment solutions to perform vertical extensions in old buildings using a multicriteria decision-support model

ABSTRACT The initial premise of this research is that the relative efficiency of refurbishment solutions with architectural membranes needs to be measured in order to allow its comparison with conventional solutions, helping decision makers to select the most efficient solutions. The evaluation of this efficiency depends on economic features, but also on functional, technological and environmental ones. This study presents a model to solve this problem, using decision trees, multicriteria decision-making methods (SAW and AHP) and a sensitivity analysis. The selection of the criteria and the assignment of the corresponding weights was attained through an expert group survey for a baseline scenario, aiming maximizing functional performance (such as energy savings) and minimizing employed resources (materials, costs, etc.). The most efficient refurbishment solution among the set of alternatives was reached using the developed model. The methodology was applied to a case study – an old building from the nineteenth century, located in Portugal, which was refurbished with a vertical extension. The result reveals that the proposed model is successful and illustrates the potential of this evaluation methodology to compare and quantify the efficiency of a series of different lightweight constructive solutions. It also underlines the advantages of using lightweight building technologies, especially with architectural membrane materials, in building refurbishments.

Cluster Analysis of Multicriteria-Classified Wheeled Armored Vehicles

Cluster analysis was performed on the data representing the defense of 32 wheeled armored vehicles based on a Multi Criteria Decision Support model. The number of clusters was determined by nonhierarchical clustering, while the vehicles were assigned to a cluster by non‑hierarchical clustering. The number of clusters are either three or eight. For three assumed clusters, the BTR‑type, the equipment designed before and after 2000 were grouped together, while for eight assumed clusters, these groups split into subgroups. Each subgroup consist of vehicles with similar defense, while the distinction between the subgroups could be made on the basis of modernization, the evolution of defense techniques in time.

A Participative Method for Prioritizing Smart City Interventions in Medium-Sized Municipalities

One of the most critical issues relating to smart cities is the selection of the most suited interventions, among numerous available options. Especially when medium-sized municipalities are concerned that typically have less capabilities and available expertise, the need for a methodological framework for supporting the selection of possible interventions is profound. The aim of this article is to propose such a participative method for investigating the sectors in which a municipality could take actions, and for deciding on possible interventions. The approach includes citizens' opinion, municipality policy, and the organizational, technological and economic status of the municipality, as well as best practices from other smart cities. This way, a multi-criteria decision support model was developed, in order to select the best application scenarios for a medium-sized municipality. The model was applied in Samos Island in Greece, revealing that citizens show great interest in the development of smart applications. The whole approach can be easily applied, with the proper modifications, in a large variety of cities, offering a useful tool to decision makers and societies.

Managing Urban Plant Invasions: a Multi-Criteria Prioritization Approach.

Alien plant invasions in urban areas can have considerable impact on biodiversity and ecosystem services (ES). Managing urban plant invasions is particularly challenging given the complex interactions between ecological, economic and social elements that exist in the urban milieu. Strategic landscape-scale insights are crucial for guiding management, as are tactical site-scale perspectives to plan and coordinate control efforts on the ground. Integrating these requirements to enhance management efficiency is a major challenge. Decision-support models have considerable potential for guiding and informing management strategies when problems are complex. This study uses multi-criteria decision tools to develop a prioritization framework for managing invasive alien plants (IAPs) in urban areas at landscape and local scales. We used the Analytic Hierarchy Process (AHP; a multi-criteria decision support model) to develop and rank criteria for prioritising IAP management in the City of Cape Town (CoCT), South Africa. Located within a global biodiversity hotspot, Cape Town has a long history of alien plant introductions and a complex socio-political make-up, creating a useful system to explore the challenges associated with managing urban plant invasions. To guide the prioritization of areas for IAP management across the CoCT, a stakeholder workshop was held to identify a goal and criteria for consideration, and to assess the relative importance given to each criterion in IAP management. Workshop attendees were drawn from multiple disciplines involved with different aspects of IAP research and management: government departments, scientists and researchers, and managers with a diverse set of skills and interests. We selected spatial datasets and applied our multi-criteria decision analysis in a Geographic Information System (GIS) to develop a landscape-scale prioritization map. To address issues relevant in an urban setting, we also modified an existing IAP management framework to develop a tactical (site-level) prioritization scheme for guiding on-the-ground control operations. High-priority sites for IAP management were identified at landscape- and local scales across the study area. Factors related to safety and security emerged as pivotal features for setting spatially-explicit priorities for management. The approach applied in this study can be useful for managers in all urban settings to guide the selection and prioritization of areas for IAP management.

Decision support model for the selection of asphalt wearing courses in highly trafficked roads

The suitable choice of the materials forming the wearing course of highly trafficked roads is a delicate task because of their direct interaction with vehicles. Furthermore, modern roads must be planned according to sustainable development goals, which is complex because some of these might be in conflict. Under this premise, this paper develops a multi-criteria decision support model based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution to facilitate the selection of wearing courses in European countries. Variables were modelled using either fuzzy logic or Monte Carlo methods, depending on their nature. The views of a panel of experts on the problem were collected and processed using the generalized reduced gradient algorithm and a distance-based aggregation approach. The results showed a clear preponderance by stone mastic asphalt over the remaining alternatives in different scenarios evaluated through sensitivity analysis. The research leading to these results was framed in the European FP7 Project “DURABROADS” (No. 605404).