Multi-criteria Method Research Articles

Decision support system for evaluating park & ride system using the analytic hierarchy process (AHP) method

ABSTRACT The park and ride (P&R) system is a crucial transfer point between private and public transportation, playing a significant role in the sustainable urban mobility plan (SUMP) and operational objectives of the system. The implementation of the P&R system requires careful consideration of diverse criteria related to private vehicles and public transport, which must be evaluated by transportation planners with expertise in transportation, road safety, and mobility. When determining the optimal location for the P&R system, transportation planners specializing in transportation, road safety, and mobility must take a number of criteria into account. This paper discusses the use of the analytic hierarchy process (AHP) for establishing a P&R system. In addition, the multi-criteria method permits consensus on the primary criteria for the location of the P&R system in an intermediate city. The result shows that accessibility to public transportation has been identified as the most important criterion for establishing a P&R system by transportation planners. In conclusion, the AHP is a multi-criteria method that allows transport planners to evaluate the location of the P&R system.

Multicriteria sorting method based on global and local search for supplier segmentation

AbstractThe aim of this research is to develop a robust multicriteria method to classify suppliers into ordered categories and its validation in real contexts. The proposed technique is based on a property of net flows of the PROMETHEE method and uses global and local search concepts, which are common in the optimisation field. The results obtained are compared to those from the most cited sorting algorithm, and an empirical validation and sensitivity analysis is performed using real supplier evaluation data. Furthermore, it does not require additional information from decision‐makers as other sorting algorithms do for assigning incomparable or indifferent alternatives to groups. An extension of the silhouette concept from data mining is also contributed to measure the quality of ordered classes. Both contributions are easy to apply and integrate into decision support systems for automated decisions in the supply chain management. Finally, this practical approach is also useful to classify customers and any type of alternatives or actions into ordered categories, which have an increasing number of real applications.

An energy balance and multicriterial approach for the sizing of a hybrid renewable energy system with hydrogen storage

As the clamour for a Net Zero carbon energy economy increases, it is necessary to harness the potential of renewable energies in powering buildings to lower fossil power plants' contributions to the overall energy mix. This paper aims to present an energy balance load sensitivity analysis and multi-criteria method for sizing a green energy system for powering two office complexes that house space research laboratories. The energy component considered includes battery storage (BAT), captive diesel generator (DG), fuel cell (FC), hydrogen storage (H2T), solar photovoltaic (PV), and wind turbine. Using HOMER, the techno-economic features and the hourly operational details of the energy components were obtained. The efficacy of Entropy- Additive Ratio Assessment was deployed on the outputs from HOMER to obtain the most preferred energy system based on more than one criterion. The result of the study indicates that the most preferred energy system for Abuja is a PV, WD, FC, DG, and BAT, having a total net present cost (TNPC) of $220,930. In contrast, the most suitable energy system for the energy in the Anyigba office consists of PV, FC, and BAT, with its TNPC at $106,955.

A two-phase decision making based on the grey analytic hierarchy process for evaluating the issue of park-and-ride facility location

Planning a Park-and-Ride (P&R) system in an urban area of a city depends on a group of transportation planning professionals with different areas of expertise in mobility, agreeing on which criteria or set of criteria are the most important. In addition to analysing the criteria established as mobility policies in the Sustainable Urban Mobility Plan (SUMP), when establishing a set of facilities belonging to the P&R system. To find out which criterion is the most important one when combining the mobility criteria established in the SUMP with the criteria of transportation planners with different expertise, this paper applies the multi-criteria method known as the Grey Analytic Hierarchy Process (G-AHP). In this method, at first the main and secondary criteria are determined at two levels that allow building a hierarchical structure, then transportation professionals are surveyed, and finally, the formulation of the multi-criteria method is designed. The result of the study illustrates the effectiveness and usefulness of the proposed multi-criteria method to determine the hierarchy of criteria from most to least important to solve the problem of locating a P&R system. Also, the results are compared with two different multicriteria methods (FAHP and BWM) to see how they are alike and how they are different. The finding suggests that the planning of a P&R system and the criterion for the accessibility of public transport go hand in hand, regardless of the multi-criteria method employed.

Sustainable Tourism around Ecosystem Services: Application to a Case in Costa Rica Using Multi-Criteria Methods

Tourism is an activity that generates important benefits; in the case of Costa Rica, it focuses its activity on natural areas, using the different ecosystem services (ES) and obtaining economic and social benefits. However, its ecological value can diminish, making it necessary to implement methods to estimate its sustainability. This paper proposes an evaluation of tourism around ES by applying the MIVES method (Value Integrated Model for Sustainability Evaluation), based on the multi-attribute utility theory, which implies that it transforms different variables into a 0 to 1 relationship, where the closer it is to 1, the more sustainable it is. For this purpose, it considered a decision tree, integrating environmental, economic, and social requirements, 6 criteria, and 13 indicators. The method was applied to two sites, Golfito and Jimenez in Costa Rica. It considered the following stages: (i) literature review and expert consultation, (ii) decision tree, (iii) assignment of weights, (iv) sustainability indexes, and (v) sensitivity analysis. The most sustainable site is Jiménez, obtaining an overall index of 0.40 compared to 0.25 for Golfito. The economic and social requirements are the best evaluated, while the environmental requirement was the worst evaluated for both sites. The proposed methodology can be extrapolated to other natural areas.

Class Thresholds Pre-Definition by Clustering Techniques for Applications of ELECTRE TRI Method

The sorting problem in the Multi-criteria Decision Analysis (MCDA) has been used to address issues whose solutions involve the allocation of alternatives in classes. Traditional multi-criteria methods are commonly used for this task, such as ELECTRE TRI, AHP-Sort, UTADIS, PROMETHEE, GAYA, etc. While using these approaches to perform the sorting procedure, the decision-makers define profiles (thresholds) for classes to compare the alternatives within these profiles. However, most such applications are based on subjective tasks, i.e., decision-makers’ expertise, which sometimes might be imprecise. To fill that gap, in this paper, a comparative analysis using the multi-criteria method ELECTRE TRI and clustering algorithms is performed to obtain an auxiliary procedure to define initial thresholds for the ELECTRE TRI method. In this proposed methodology, K-Means, K-Medoids, Fuzzy C-Means algorithms, and Bio-Inspired metaheuristics such as PSO, Differential Evolution, and Genetic algorithm for clustering are tested considering a dataset from a fundamental problem of sorting in Water Distribution Networks. The computational performances indicate that Fuzzy C-Means was more suitable for achieving the desired response. The practical contributions show a relevant procedure to provide an initial view of boundaries in multi-criteria sorting methods based on the datasets from specific applications. Theoretically, it is a new development to pre-define the initial limits of classes for the sorting problem in multi-criteria approach.

Optimization of the Automated Production Process Using Software Simulation Tools

The purpose of this article is to point out the need to use software simulation tools in industrial practice to optimize the production process and assess the economic effectiveness of investment, including risk. The goal of the research is to find an optimal investment variant to ensure an increase in the production volume of at least 50% and to achieve the maximum economic efficiency of the investment, even considering the risk. The article presents a comprehensive approach that enables the achievement of the set research goal. The selection of the optimal version of the investment is carried out in three steps. Firstly, the versions of the investment variants are assessed from the production point of view using the program Tecnomatix Plant Simulation. Subsequently, the versions of the investment variants are assessed from an economic point of view and from a risk point of view. Economic efficiency is assessed using the financial criteria net present value (NPV), profitability index (PI), and discounted payback period (DPP), and risk analysis is carried out using Monte Carlo simulations. Finally, the accepted outputs are evaluated overall using a multi-criteria method, namely the method of partial order.

Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting.

Hybrid model for early identification post-Covid-19 sequelae.

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.

Multi-criteria classification of reward collaboration proposals

This article develops a mechanism for automatically classifying rewarded collaboration proposals. The research’s purpose is to increase transparency in the rewarded collaboration process, thereby inviting more collaboration proposals, to aid in the fight against criminal organizations. The research focuses on critical facets of the public security system and of the organized crime in Brazil. Through rewarded collaboration, a new approach to plea bargaining is achieved that helps detect, disrupt, and ultimately dismantle illicit operations. This multi-criteria approach enables the consideration of the interests of detainees, the priorities of police institutions, and the perspective of the community. This approach results in the formation of a holistic understanding of the issue, taking into account the costs and benefits to society of punishing defendants whose guilt can be established. Composition of Probabilistic Preferences Trichotomic is the multi-criteria method employed to take imprecision into consideration while performing classification into predetermined classes. It enables the evaluation of each proposal independently. This boosts the system’s objectivity and consequently its attractiveness. Taking the interaction between the criteria into consideration, the analysis naturally applies to any number of evaluation criteria and individuals involved in the investigated crimes. Novel forms of interaction modeling are compared in practical instances.

Machine Learning-Driven Approach for Large Scale Decision Making with the Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) multicriteria method can be cognitively demanding for large-scale decision problems due to the requirement for the decision maker to make pairwise evaluations of all alternatives. To address this issue, this paper presents an interactive method that uses online learning to provide scalability for AHP. The proposed method involves a machine learning algorithm that learns the decision maker’s preferences through evaluations of small subsets of solutions, and guides the search for the optimal solution. The methodology was tested on four optimization problems with different surfaces to validate the results. We conducted a one factor at a time experimentation of each hyperparameter implemented, such as the number of alternatives to query the decision maker, the learner method, and the strategies for solution selection and recommendation. The results demonstrate that the model is able to learn the utility function that characterizes the decision maker in approximately 15 iterations with only a few comparisons, resulting in significant time and cognitive effort savings. The initial subset of solutions can be chosen randomly or from a cluster. The subsequent ones are recommended during the iterative process, with the best selection strategy depending on the problem type. Recommendation based solely on the smallest Euclidean or Cosine distances reveals better results on linear problems. The proposed methodology can also easily incorporate new parameters and multicriteria methods based on pairwise comparisons.

A comparative assessment of Conventional and Rough-Based Multi-Criteria methods for failure mode and effects analysis of Root canal treatment

This paper presents failure mode and effects analysis (FMEA) of a dentistry procedure i.e. root canal treatment (RCT) The aims of the present study are as follows: (i) to identify the potential failure modes (FMs) associated with RCT and (ii) to determine the ranks of the identified FMs to represent their degree of severity for the failure of the RCT procedure. Sixteen potential FMs of RCT are identified with the help of five members of the FMEA team. The FMs are evaluated with respect to three risk factors (RFs) i.e. severity (S), occurrence (O), and detection (D). Opinions regarding the importance of RFs on a scale of 1-9 and importance of each RF for each FM on a scale of 1-10 are gathered from FMEA team members. Subsequently, the obtained information is transformed into rough numbers in order to take care of the vagueness and inconsistency present in the human judgement. Further, weights of the RFs, in terms of rough numbers, are determined. Subsequently, four rough-based multi-criteria decision making (MCDM) methods i.e. rough weighted aggregated sum product assessment (R-WASPAS), rough additive ratio assessment (R-ARAS), rough TOPSIS (R-TOPSIS), and rough VIKOR (R-VIKOR) are used to rank the FMs. The final ranking of the FMs is derived using the grade average integrated ranking method. The results reveal that “Missed canal” and “Root canal under fillings” are the most and the least critical FMs, respectively. Further, the top 20%, the next 30%, and the last 50% FMs are categorized into highly, fairly, and the least critical groups. A few important suggestions are proposed to reduce the risks of the top 20% FMs. The results of the study may be useful for the professionals involved in the RCT and may help them in properly planning and successfully executing the RCT procedure.

A New Revised Group DEMATEL method with application on facility location problem

Multi-criteria methods are commonly used in facility location problem to evaluate different facility locations in the presence of a set of criteria in order to select the best facility location. The increasing availability of Internet-of-Things (IoT) data, can be a help in gathering the necessary data for the location evaluation. The paper proposes a New Revised Group DEMATEL (NRG-DEMATEL) multi-criteria method, aiming to analyse cause-and-effect relationships among a set of criteria. This method provides general conditions which imply the convergence to the null matrix of the sequence of powers of the initial relation matrix, hence the total influence matrix can be computed. DEMATEL is infeasible when the sequence of powers of the initial relation matrix does not converge to the null matrix. The NRG-DEMATEL can be applicable to all situations feasible or infeasible. For the cases that are feasible, our method leads to the same result as the original method DEMATEL. The causality diagram and the digraph, that can visualize the causal relationships of a set of criteria, are built with the NRG-DEMATEL method. In order to reduce the complexity of the digraph, a threshold value is set to filter out negligible effects. Two methods for computing the thresholds are considered in this paper: the arithmetic mean method and the arithmetic mean - standard deviation method. An application of the NRG-DEMATEL to hospital facility problem is realized.

A new composite indicator for Manufacturing efficiency

The efficiency of production processes is a topic of fundamental importance in the manufacturing sector. Different approaches for measuring the efficiency of manufacturing processes have been presented over time. However, the debate on the reliability of the tools and indicators currently available, as well as on their effectiveness and the ability to capture changes in dynamic contexts, is still open. This paper tries to overcome some of these limitations by proposing an innovative composite indicator to measure and control the performance of production processes. The composite indicator we introduced is based on an integrated approach based on Data Envelopment Analysis (DEA), and a new multi-criteria method such as Parsimonious Analytical Hierarchy Process (PAHP), taking also into account the opinion of the domain experts. The methodological approach we proposed has been tested in a production plant of an international company operating in the automotive sector. Results demonstrated that the integration of DEA and PAHP into a composite indicator allow to obtain a more reliable and effective control tool in the analysis of the efficiency of production processes.

THE APPLICATION OF THE ANALYTIC HIERARCHY PROCESS APPROACH TO THE SELECTION OF GAS RADIANT HEATING SYSTEM IN AN INDUSTRIAL BUILDING

Those involved in the heating industry of large-scale facilities have different expectations of them. It is expected that heating systems will be characterized, among other things, by rational investment and operating costs, enable maintenance of internal conditions suitable for workers or related to the technological process. Identification of selected factors influencing the selection of infrared gas heaters for heating the industrial hall is the purpose of the article. The identified factors form a base of criteria for their evaluation using the Analytic Hierarchy Process (AHP) multi-criteria method. Using an industrial building as an example, a hierarchical model is presented to determine which variant meets the requirements to the highest degree and which decision can be considered optimal. The presented algorithm allows a thorough analysis and evaluation of the factors affecting the selection of infrared gas radiant heaters as a heating solution for industrial halls. In the case of the analyzed hall, the solution with ceramic heaters and standard radiation efficiency proved to be the solution with the highest decision-making index.

SUPPORTING THE CHOICE OF THE BEST-FIT AGILE MODEL USING FITRADEOFF

The goal of this study is to propose a decision model for supporting the choice of the best-agile model, based on the Flexible and Interactive Tradeoff (FITradeoff) method. This study provides the definition of an extensive set of 38 measurable criteria that were considered for evaluation of the most popular agile process models used in small and medium-scale enterprises. To illustrate the application of the model, we applied it to a set of alternatives that includes DSDM, SCRUM, XP2 and Crystal and an experienced project manager acted as our decision maker during the interactive elicitation process of the FITradeoff. The results show that the FITradeoff is very valuable for this class of problem because of its strong mathematical foundation and the possibility of combining two different ways for modeling the preferences of decision makers, which makes the process cognitively easier than other multi-criteria methods and increase the confidence on the results. Although our study focuses on the context of small and medium-scale companies for software development, the approach can be used to other types of environments, including distributed software development and large enterprises.

A New Approach to University IT Project Portfolio Management Based on Multi-Criteria Methods and the COBIT 5 Governance Framework

A New Approach to University IT Project Portfolio Management Based on Multi-Criteria Methods and the COBIT 5 Governance Framework

Governance Applicable to Public Organizations: An Analysis based on the Multicriteria Method (MCDM) Parsimonious AHP (PAHP) under the perspective of the Leadership Mechanism in the light of the Union Court of Audits (TCU)

Objective: This study aims to analyze and classify the best public organizations in terms of Good Public Governance Practices related to the Leadership Mechanism based on the assessment of governance carried out by the Federal Court of Accounts. Method: For this analysis, ranking and evaluation of public organizations regarding Governance, the Multicriteria Decision Support Method (MCDM) Parsimonious AHP (PAHP) was used. Results: The PAHP method was able to rank 378 public organizations that were audited by the TCU following a pre-established questionnaire by this Court of Accounts. Contributions: This work contributed to Scientific Research in the area of Operational Research applied to Public Administration, as it brought relevant issues to academia and society.

Gestão de riscos em arquivos proposição de um método para priorização de ações para o impedimento da perda do patrimônio documental

In view of the recurring disasters,that put archival collections at risk,and the complex nature of addressing this issue, especially given the limited financial resources allocated to these sectors, we propose a method to assist managers of archival institutions or sectors in prioritizing actions to prevent the loss of documentary heritage and institutional memory. We utilized the Analytic Hierarchy Process (AHP) multicriteria method in conjunction with the integrated Fine-Mosler model to develop this method. The proposed approach is exemplified through the central archives of two public agencies. Our results demonstrate that this method is an effective tool for defining the order of priorities for preservation actions that must be taken for archival collections.

Parking sistemi u okviru koncepta pametne mobilnosti - pregled i evaluacija

The accelerated growth of cities and the development of information and communication technologies (ICT) have led to the emergence of the concept of smart mobility. Smart mobility enables efficient, flexible and environmentally friendly travel by various modes of transport using smart transport systems, smart infrastructure and smart technologies. One of the possible solutions for establishing smart mobility is the implementation of smart parking systems. The large number of vehicles in urban urban areas makes finding a parking space a difficult task, which further affects traffic congestion and environmental pollution. To overcome this challenge, it is necessary to establish an efficient and advanced parking system that allows drivers to find and reserve a suitable parking space, dynamically plan a route and guide and/or park without human intervention. In this regard, the paper provides an overview and analysis of smart parking systems with a special focus on the advantages and disadvantages of each individual type. In addition, using a multi-criteria method known as the Analytical Hierarchical Process (AHP), the paper provides an evaluation of smart parking systems based on the key performance.