Multi-criteria Decision Support Research Articles

Organizational Management: A Study of a Company of Distribution Services

The current trend of focusing on core activities created opportunities for some companies to act as subcontractors. This is the case for companies that handle the distribution of food products. Beyond the concern for speedy delivery, they must take into account the perishable nature of the products if they are to meet the expectations of their customers. The objective of this research, characterised as exploratory and descriptive, is to build a model to support the management of a company to meet the needs of food distribution, taking into account regional peculiarities, customers, and distribution services. To achieve this objective, we used the methodology of multicriteria decision support – constructivist (MCDA-C). The model allowed us to identify opportunities for improvement to the following aspects: minimum profitable load delivered; control of the localisation of products, maintenance of trucks, and accommodation of the products on the truck, which led to an increase in performance from 49 to 59 points using the proposed model.

Identifying Influencing Factors of Audit Risk Model: A Combined Fuzzy ANP-DEMATEL Approach

In the professional circles, factors affecting audit risk are treated independently; however, a more objective approach in assessing detection risk should be involved the relationships among the audit risk factors. This study introduces a framework based on a fuzzy multi-criteria decision support to identify the influencing factors may affect the audit risk model considering the interdependencies among them. We first takes advantages of a fuzzy screening method to screen forty more practical factors may affect the audit risk model out of 58 potential ones. Then, we applied a combined approach of Analytic Network Process (ANP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to weight the factors and prioritize them considering their inner and outer dependencies complemented with a case study of Iran. To be more compatible with ambiguities related to human beings and make more useful decisions in the real world, the fuzzy set theory is used. The combined approach used in this study providing correct, precise weight for each factor can explore a more rigorous framework for decision-making in risk assessment by integrating interdependent relationships within and among audit risk factors.

Determining the utility in management by using multi-criteria decision support tools: a review

The multiple criteria decision-making (MCDM) utility-determining techniques are considered to be new development techniques that have been recently presented, extended and used by some scholars. In the current work, an attempt is made to present a systematic review of methodologies and applications of the MCDM utility-determining techniques discussed in recent years. The researchers reviewed 86 papers, describing the use of the MCDM utility-determining techniques, which were published in the period 2004–2015 in more than 42 scientific journals. They mainly refer to the area of management and are extracted from online databases, such as Web of Science, Scopus and Google Scholar. According to the classification used by the researchers, the papers were grouped based on the five main MCDM utility-determining techniques, including stepwise weight assessment ratio analysis (SWARA), the weighted aggregated sum product assessment (WASPAS), the additive ratio assessment (ARAS), the method of complex proportional assessment (COPRAS), multi-objective optimisation by ratio analysis (MOORA) and MULTIMOORA (MOORA plus a full multiplicative form). Furthermore, the papers were categorised taking into account their authors, publication date, journal name, the technique and method used, research objectives, research gap and problem, solution and modelling and, finally, the results and findings. The results of this study show that, in 2013, scholars published more papers on the MCDM utility-determining techniques than in other years. It is also worth noting that a group of COPRAS methods (COPRAS-Grey and COPRAS-Fuzzy) was ranked number one among the methods used in this area. With regard to journals, the Journal of Civil Engineering and Management was ranked first in the list of journals, which contributed to this review.

Comparison of Multi-Criteria Decision Support Methods (AHP, TOPSIS, SAW & PROMENTHEE) for Employee Placement

The right decision in placing employees in an appropriate position in a company will support the quality of management and will have an impact on improving the quality of human resources of the company. Such decision-making can be assisted by an approach through the Decision Support System (DSS) to improve accuracy in the employee placement process. The purpose of this paper is to compare the four methods of Multi Criteria Decision Making (MCDM), ie Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Of Evaluations (PROMETHEE) for the application of employee placement in accordance with predetermined criteria. The ranking results and the accuracy level obtained from each method are different depending on the different scaling and weighting processes in each method.

Organisational management: a study of a company of distribution services

The current trend of focusing on core activities created opportunities for some companies to act as subcontractors. This is the case for companies that handle the distribution of food products. Beyond the concern for speedy delivery, they must take into account the perishable nature of the products if they are to meet the expectations of their customers. The objective of this research, characterised as exploratory and descriptive, is to build a model to support the management of a company to meet the needs of food distribution, taking into account regional peculiarities, customers, and distribution services. To achieve this objective, we used the methodology of multicriteria decision support – constructivist (MCDA-C). The model allowed us to identify opportunities for improvement to the following aspects: minimum profitable load delivered; control of the localisation of products, maintenance of trucks, and accommodation of the products on the truck, which led to an increase in performance from 49 to 59 points using the proposed model.

Multi-criteria decision support for feature selection in network anomaly detection system

The growth of computer networks from LAN to cloud, virtualisation and mobility always keeps intrusion detection system (IDS) as a critical component in the field of network security infrastructure. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. The market for security solutions for next-generation is rapidly evolving and constantly changing to accommodate today's threat. Many intrusion detection techniques, methods and algorithms are implemented to detect these novel attacks. But there's no clear feature set, uncertainty bounds established as a baseline for dynamic environments. The main objective of this paper is to determine and provide the best feature selection for next generation dynamic environments using multi-criteria decision making, decision tree learning with emphasis on optimisation (contingency of weight allocation) and handling large datasets.

Decision-Making Methodologies for Reuse of Industrial Assets

When there is a social consensus that industrial assets are in fact heritage elements of cultural interest, their conservation and reuse must be considered with approaches that offer greater guarantees and that prevent their exposure to aggressive actions. In order for this to materialise, many aspects must be included in the decision-making process, from the characteristics of an asset and its surroundings, to the valuable aspects that distinguish it and that must be protected. This study aims to develop tools that guide the decision-making process regarding the most appropriate activity for each specific case study. Multicriteria Decision Support Techniques are evaluated as adequate support to create a proposal that fulfils these objectives. Furthermore, the Analytic Hierarchy Process is adapted to develop methodologies for assessing both the heritage value and the most compatible uses according to the characteristics of the asset. Subsequently, they are connected and such considerations regarding the heritage value of the asset are incorporated into the final decision. The tools developed are then applied to a case study to test their performance, assess their usefulness, and identify possible applications and future developments.

A multicriteria decision support approach for redesigning e-learning systems

In this paper we present a decision support approach for redesigning e-learning systems through the analysis of students' preferential structures. Our approach supports instructional designers to take decisions during the redesign process of an e-learning system by analysing the students' preferential structures in the following dimensions: 1) the dimension of students' learning behaviour; 2) the dimension of students' learning performance; 3) the dimension of students' feedback. Our approach is based on a new method from the domain of multicriteria decision-making that provides weights of individuals' preferences which become input in a clustering process that determines groups of preferences among students. Based on these clusters we analyse the other three dimensions and we present a framework for the decision making process capable to provide significant feedback for the redesign of the e-learning system. A case study is also described about the application of our approach in a real course.

Multi-criteria decision support for feature selection in network anomaly detection system

Multi-criteria decision support for feature selection in network anomaly detection system

A multicriteria decision support approach for redesigning e-learning systems

In this paper we present a decision support approach for redesigning e-learning systems through the analysis of students' preferential structures. Our approach supports instructional designers to take decisions during the redesign process of an e-learning system by analysing the students' preferential structures in the following dimensions: 1) the dimension of students' learning behaviour; 2) the dimension of students' learning performance; 3) the dimension of students' feedback. Our approach is based on a new method from the domain of multicriteria decision-making that provides weights of individuals' preferences which become input in a clustering process that determines groups of preferences among students. Based on these clusters we analyse the other three dimensions and we present a framework for the decision making process capable to provide significant feedback for the redesign of the e-learning system. A case study is also described about the application of our approach in a real course.

The Evolutionary Optimization of a Company’s Return on Equity Factor: Towards the Agent-Based Bio-Inspired System Supporting Corporate Finance Decisions

Return on Equity (ROE) is an important factor from the perspective of formulating and implementing a company’s financial strategies. It is also one of its evaluation criteria. It presents to investors the effectiveness of using their capital. Increasing profitability may be treated as a symptom of shareholder wealth, while its reduction may be a signal indicating a deterioration of the financial situation of the company. An investment in a company will be attractive to investors if the results obtained by it will enable the benefit from the dividend to be paid and if the share prices will show an upward trend. Therefore, profit and profitability are categories dependent on the company and affect the wealth of its owners. The ROE ratio is synthetic and is linked to, among others with the size of sales, asset use activity, and the size of the company’s debt. However, the decisions regarding the capital structure of a company should be made not only by purely economic and financial analyses but also should take into account the social and environmental effects of economic activities. To take into account not only short-term financial goals but also long-term sustainable development goals during the decision-making process, we need intelligent and creative multi-criteria decision support tools. Bio-inspired artificial intelligence techniques—such as evolutionary algorithms, deep neural networks, or swarm algorithms, to give only a few examples—are gaining more and more popularity in the recent years. Evolutionary algorithms are optimization techniques that are modeled on the processes of evolution that are taking place in natural populations. They can find approximate solutions to the NP-hard global, multi-modal, and multi-objective optimization problems. In this paper, we propose an innovative approach—an agent-based bio-inspired system supporting decisions in the area of corporate finance, which takes into account not only financial goals but also the sustainable development goals. The system will allow for multi-objective optimization with the use of bio-inspired algorithms. In this paper, we will concentrate on one module of the proposed system—the evolutionary algorithm optimizing the ROE factor. During the experiments, we will verify the ability of the proposed algorithm to provide decision makers with reasonable, useful, and, at the same time, also innovative and non-obvious solutions concerning the desired capital structure of a given company, which usually operates in a rapidly changing environment. The proposed system will allow for taking into account more than one criteria and perform multi-objective optimization with the use of an evolutionary algorithm or an agent-based co-evolutionary algorithm, so it will be possible to include also the long-term goals of sustainable development in the future.

A fuzzy multi-criteria spatial decision support system for solar farm location planning

In recent years, investment in solar energy has increased substantially across countries. Thus, selecting convenient locations for solar farms has become a fundamental problem when determining the investment required due to differences in climatic factors, the type and availability of land, transportation infrastructures, and the quality of power lines. Multi-Criteria Evaluation approaches based on crisp data are generally used in the selection process of optimal locations. However, despite being crisp, the data available when considering the evaluation criteria of the different alternatives constitute a discrete approximation performed on a spatial grid of potential locations. Thus, we introduce a three-stage fuzzy evaluation framework designed to account for the imprecision inherent to the evaluations when identifying the most convenient location for constructing solar power farms. First, we implement ANFIS (Adaptive Neuro-Fuzzy Inference System) on the set of grid intersection crisp data points and derive a coherent set of approximations per each potential discrete location and evaluation criterion. Then, the fuzzy AHP (Analytic Hierarchy Process) method is used to determine the weights of the different criteria considered from the linguistic evaluations provided by different experts. Finally, we define a set of if-then rules combining the different ANFIS evaluation criteria and their weights within a FIS (Fuzzy Inference System) whose output is used to determine the most convenient location for constructing a solar power farm. The efficacy of the proposed evaluation framework is demonstrated through its application to the Iranian regions of Kerman and Yazd.

Spatial Multi-Criteria Decision Support System and Strategic Environmental Assessment: A Case Study

This contribution focuses on the Strategic Environmental Assessment (SEA) as an important tool to ensure sustainable development and reach a high level of environmental protection. More specifically, this paper provides an evaluation method based on the integration of Geographic Information System (GIS) and Multi-criteria Analysis—named Integrated Spatial Multi-criteria Decision Support System (ISMDSS)—to support the preparation of environmental assessment reports and the construction of scenarios for the adoption of urban plans, as an innovative tool that integrates objectives and multidimensional (economic, environmental, and social) components, as well as different approaches and models for the construction of a long-term shared vision. In particular, considerations are made by presenting a thought-provoking case study on the SEA of the urban plan of the municipality of Marzano di Nola, located in the province of Avellino in the Campania region. The experiment carried out showed the potentiality of the ISMDSS to evaluate the impacts of different scenarios with the aim of developing a sustainable urban municipal plan. The spatial dimension is useful in understanding the dynamics that characterize each environmental topic in a specific area, by considering not only the components of the natural and developed environment, but also the interactions with social and economic components.

Development of a new knowledge-based fabric recommendation system by integrating the collaborative design process and multi-criteria decision support

In this paper, a perception-based fabric recommendation system is proposed to help fashion designers to select the most appropriate fabric in the design process, meeting the perception of the target consumer. The proposed methodology is based on the development of an interactive hierarchical structure, decomposing the decision problem into five levels: goal, criteria (consumers' requirements), sub evaluation criteria (fabric properties), rating scale, and alternatives, which ensures the analysis of requirements of consumers and knowledge sharing among designers. The proposed knowledge-based recommendation system includes a collaborative design process, a commonly used sensory evaluation procedure, and a computational model using the Fuzzy AHP (Analytic Hierarchy Process) and Fuzzy TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) algorithms. The Fuzzy AHP method is used to structure the fabric selection problem and determine the relevant weights of the concerned criteria and corresponding components, while the Fuzzy TOPSIS method is used to evaluate the alternative fabrics based on the criteria obtained from the AHP process and to give the total final ranking of the involved fabric alternatives. Experimental results indicate that, using the proposed interactive hierarchical structure, professional knowledge of the designers can be fully extracted to ensure a high level of consumer satisfaction.

Avaliação dos municípios do Nordeste brasileiro pelos objetivos do desenvolvimento sustentável e o triple bottom line

<p>Os objetivos do desenvolvimento sustentável (ODS) da Organização das Nações Unidas buscam inserir o bem-estar, a redução da pobreza e a utilização eficiente dos recursos naturais na noção de desenvolvimento econômico. O <em>Triple Bottom Line </em>(TBL) é um modelo passível de ser parametrizado que propõe o dimensionamento da sustentabilidade nas dimensões social, econômica e ambiental e que pode ser aplicado na avaliação de organizações e municípios. Objetivou-se estabelecer um <em>ranking </em>de quinze municípios da região Nordeste pelo seu nível de sustentabilidade, utilizando dezesseis indicadores de ações, atividades e políticas públicas alinhadas com os ODS e sob a ótica do TBL. Cada município foi avaliado em relação a todos os indicadores pela Composição Probabilística de Preferências (CPP), um método multicritério de apoio à decisão. Os resultados demonstraram que tanto a perspectiva social quanto a econômica, envolve contrapartidas ambientais que podem comprometer a sustentabilidade dos municípios e que o bom desempenho em apenas uma dimensão não é suficiente para expressar a sustentabilidade, reforçando a importância da abordagem do desenvolvimento sustentável no crescimento econômico.</p>

An application of a multi-criteria decision support system to assess energy performance of school buildings

This work presents a multi-criteria classification system considering multiple, conflicting and incommensurate evaluation aspects influencing energy efficiency in school buildings. The multi-criteria ELECTRE TRI method is used to classify energy performance of school buildings into categories of merit using the IRIS software. The alternatives under evaluation are assigned to predefined ordered categories according to their absolute performance and not in comparison with the performance of other alternatives. Each alternative is assessed using reference profiles defining the boundaries of the categories in which the alternatives should be sorted. The model was applied using a set of performance indicators obtained in the framework of a research and development project aimed at assessing the energy performance of a sample of Portuguese school buildings. IRIS allowed inferring robust conclusions by indicating the range of categories for each alternative, considering the decision maker's preferences, captured by the parameters of ELECTRE TRI.

Developing a multicriteria decision support framework for CHP based combined district heating systems

CHP based combined district heating (DH) systems with gas-fired boilers for peak load shaving have higher energy and environmental efficiencies compared to DH systems supplied heat by heat only boilers. However, proper multicriteria decision making method is lacking for them. This paper is dedicated to develop a decision support framework from economy, energy, technology and environment viewpoints, in order to facilitate the planning/retrofitting of the combined DH systems. Firstly, the installation strategy of gas-fired boilers is introduced, and then combined heating alternatives to be addressed are constructed by choosing different base load ratios of CHP. Secondly, a criterion aggregation system is developed, based on which weights can be elicited using complementary judgment matrix (CJM) plus feasible weight space (FWS) methods. Thirdly, an application-oriented, multicriteria decision support framework is demonstrated in a real-life DH system in Daqing, China. Stochastic multicriteria acceptability analysis (SMAA) is implemented to synthetically handle the decision problem, which is characterized by incommensurable measurements, conflicting preferences, uncertainties and imprecise information. The results indicate that the developed framework works well in the multicriteria decision making for the combined district heating systems. The optimal base load ratio in the demonstration case is between 0.66 and 0.77 with high confidence.

Landscape Services Assessment: A Hybrid Multi-Criteria Spatial Decision Support System (MC-SDSS)

This research aims to test a scalable and transferable Geographic Information System (GIS)-based evaluation methodology for the identification, quantification and assessment of multi-functional landscape features. The evaluation of multi-functional features is one of the key tasks required when it comes to identifying the values that people attribute to landscapes, according to the principles of the European Landscape Convention. Mapping the static distribution of Landscape Services (LS) through data-derived estimates and performing spatial composite indicators are fundamental steps in understanding the current state of the Social–Ecological System (SES) of threatened or resilient landscapes. The methodological process is structured in four phases: intelligence (i), design (ii), choice (iii) and outcome (iv), according to the framework of the Multi-Criteria Spatial Decision Support System (MC-SDSS). This process has been implemented in the case study of the National Park of Cilento, Vallo di Diano and Alburni (Italy). The weighting of the spatial indicators, which simulates the model of LS-functioning for the study area, derives from an entropy-based method. Such a method, by which the weights are estimated without decisional agents, concerns a key-concept of information entropy theory, whereby the amount of information for each criterion determines its relative importance within a defined set of spatial criteria. The output of the model concerns mapping composite indicators of the LS; this involves the macro-categories of Regulating, Provisioning, and Cultural Services.

An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety

PurposeRoad accidents have come to be considered a major public health problem worldwide. The aim of many studies is therefore to identify the main factors contributing to the severity of crashes.MethodsThis paper examines a large-scale data mining technique known as association rule mining, which can predict future accidents in advance and allow drivers to avoid the dangers. However, this technique produces a very large number of decision rules, preventing decision makers from making their own selection of the most relevant rules. In this context, the integration of a multi-criteria decision analysis approach would be particularly useful for decision makers affected by the redundancy of the extracted rules.ConclusionAn analysis of road accidents in the province of Marrakech (Morocco) between 2004 and 2014 shows that the proposed approach serves this purpose; it may provide meaningful information that could help in developing suitable prevention policies to improve road safety.

Worker-Recovered Companies in Brazil: self-management process assessment by applying multi-criteria decision support tools

Company recovery is a practice developed by workers who, in the imminence of becoming unemployed, negotiate or fight for access to the means of production of bankrupting companies, and start to manage them collectively, guided by the principles of self-management. Nevertheless, how to assess self-management in worker-recovered companies (WRCs)? The criteria selected by a bibliographic review on the concept of self-management were used in dealing with the data collected by the Brazilian WRCs national mapping. A multi-criteria decision support tool was used to build a model for analyzing and classifying the companies in three categories related to their form of management. The multi-criteria approach allowed to create an assessment of self-management practices in the WRCs studied.