Multi-criteria Decision-making System Research Articles

A novel entropy-based weighted attribute selection in enhanced multicriteria decision-making using fuzzy TOPSIS model for hesitant fuzzy rough environment

The existing approaches of multicriteria decision-making (MCDM) process might yield unreliable and questionable results. The notable challenges of MCDM approaches are rank reversal paradox and uncertainty. The prime inspiration for researchers is the MCDM for hesitant fuzzy sets (HFSs). In some scenarios, the decision-makers could not choose one from numerous values while expressing their preferences. HFS which is the extension of fuzzy sets (FS) is found to be helpful in solving such decision-making (DM) problems. The DM process is revolutionized with the commencement of powerful and efficient tools of data representation for expressing vagueness and uncertainty in data sets as FSs (both generalized and hesitant ones). This paper copes with one such novel approach that involves entropy-based attribute weighting, followed by an evaluation of approximate sets in the fuzzy rough framework. Correlation of the input alternatives in respect of evaluation criteria and the output class is evaluated. With the fuzzy technique for ordered preference by similarity to ideal solutions (FTOPSIS), the generated correlation matrix is utilized for calculating the degree of closeness ( delta ) of the output classes to the input alternatives. This paper made a novel contribution of performance indicator centered on FTOPSIS for the hesitant fuzzy rough domain. The proposed method’s efficiency is established through comprehensive and systematic experimentation on datasets utilized by researchers globally. The proposed algorithms prove its ability to handle datasets that involve human-like hesitant thinking in the MCDM system by contrasting with the existing ones.

Maritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS

Human factors contribute to majority of maritime accidents. This study proposes an advanced methodology for maritime accident prevention strategy formulation from a human factor perspective. It is conducted by incorporating Bayesian network (BN) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in a multi-criteria decision-making system. In order to develop rational accident prevention strategies, this work integrates Multiple Correspondence Analysis (MCA), Hierarchical Clustering (HC) and Classification Tree (CT) to generate strategies and describes accident types as criteria for a new multi-criteria risk-based decision-making system. Specifically, MCA is performed to detect patterns of contributory factors explaining maritime accident types. It is complemented by HC and a CT, aiming at creating different classes of vessels. Next, a Bayesian-based TOPSIS model is built to illustrate the features of multiple criteria and the relations among alternatives (i.e. strategies), so as to select the best-fit strategies for accident prevention. The results show that the information, clear order, and safety culture are the three most effective recommendations for maritime accident prevention considering human errors, which presents new insights for accident prevention practice for maritime authorities.

Determination of the optimal location for constructing solar photovoltaic farms based on multi-criteria decision system and Dempster\u2013Shafer theory

Considering environmental concerns regarding air pollution which is induced by burning fossil fuels to generate electrical power, utilizing solar energy as a green and sustainable energy source is of great interest. This study proposes a novel framework to determine the optimal location for constructing solar photovoltaic (PV) farms. To locate the suitable areas for PV farms, firstly, a fuzzy-based method is utilized to homogenize the input parameters, thereafter, the analytical hierarchy process (AHP) and Dempster-Shafer (DS) methods are independently used. In the AHP method, the proper weight for each input parameter is generated utilizing a pairwise comparison matrix. However, the DS method identifies output in different confident levels. Finally, southeast of Fars province in Iran as a region with high sunny hours in the year is selected, and the applicability of proposed methods is examined. The results show that 32% of the case study is located at high and good suitability classes in the fuzzy_AHP method. However, it is 18.56%, 16.70%, 16.32% according to 95%, 99% and 99.5% confident levels in the fuzzy_DS method, respectively. Comparisons of the fuzzy_AHP and fuzzy_DS methods at 20 points with various solar radiation intensities and the number of dusty days parameters indicate that the fuzzy_DS method can more reliably determine the optimal PV farm locations. Additionally, as the fuzzy_DS method determines the optimal locations with different confident levels, this method can benefit decision-makers to determine the risks associated with selecting a specific site for constructing solar PV farms.

Evaluation and Prioritization of Criteria Affecting the Selection of Landscape Species, Using Multi-Criteria Decision-Making Systems

Evaluation and Prioritization of Criteria Affecting the Selection of Landscape Species, Using Multi-Criteria Decision-Making Systems

Analysis of Priority Scale for Watershed Reforestation Using Trapezoidal Fuzzy VIKOR Method: A Case Study in Semarang, Central Java Indonesia

Recently, resolving a problem based on multi-criteria decision-making systems has become an attractive method. One of recent techniques is trapezoidal fuzzy VIKOR method which was used for selecting the best option among criteria. This research is aimed to utilize the VIKOR method in prioritization of watershed reforestation in Semarang City, Indonesia. Several criteria have been set for the prioritization of reforestation areas to select watersheds as reforestation targets. In this case, the six criteria and six alternatives were used in order to determine the best priority of reforestation. The result showed that VIKOR method was successfully applied to determine the best option and Kali Garang watershed was the top priority as it has the highest rank and it meets reforestation criteria compared with other watersheds criteria.

Intelligent vulnerability prediction of soil erosion hazard in semi-arid and humid region

Soil erosion by water and other anthropogenic activities in the semi-arid and humid region is noticed as a major issue in the reduction in natural land by the loss of soil nutrients. The seven standard parameters were suggested in the literature for the assessment of soil erosion hazard, viz. soil loss, sediment yield, run-off potential, land capability class, drainage density, sediment transport index, and slope. In the present study, the combination of intelligent vulnerability prediction, multi-criteria decision-making, and geographic information system techniques provides an effective approach to identify the soil erosion hazard in the semi-arid and humid region. It makes this process more effective and efficient as the vulnerability of soil erosion hazard can be predicted by the proposed trained models for any locations that have the streamlined values of above seven parameters as suggested in this paper. The standard machine learning classifiers such as k-nearest neighbour, decision tree, random forest (RF), multinomial naive bays, adaptive boosting, and gradient adaptive boosting (GAB) have been applied on the spatial data set of “Pairi” river watershed found in “Chhattisgarh”, India. There are five categories of soil abrasion, viz. “very low”, “low”, “medium”, “high”, and “very high”, in this data set that represents an index of soil erosion hazard. The experimental results have given 91.5140% and 90.5525% accuracy using RF and GAB, respectively, whereas a much better log-loss measure, i.e. 0.27, is obtained by the GAB in comparison of 0.93 with RF. The results have been verified by visiting the ground truth locations.

Design and Analysis of Novel Hybrid Multi-Objective Optimization Approach for Data-Driven Sustainable Delivery Systems

This study presents a novel two-stage solution method designed for sustainable last-mile delivery systems in urban areas. A proposed hybrid solution methodology includes multi-criteria decision-making system to select the most efficient logistics providers by considering different performance indicators, and a mixed-integer linear optimization model for last-mile cargo distributions by drones within metropolitan areas. We present a multi-objective modeling approach by considering time windows for customer services and charging operations of drones and outline important characteristics of the mathematical programming problem to minimize transportation cost (in the meantime carbon dioxide emissions) and total sustainability score of the system by using epsilon constraint method to find out the Pareto frontiers. The main novelty of the proposed solution methodology is the inclusion of many performance indicators of last-mile delivery systems into multi-objective models for design of a sustainable city logistics. Additionally, the proposed model is applied to an illustrative case by using real-life data of one of the metropolitan in Turkey. The approach is shown as comparative analysis of proposed method with other two state-of-art solution methodologies for multi-objective problems, after defining some pre-processing, symmetry breaking steps, valid inequalities, and logic cuts.

A multi-criteria expert decision system for investment decisions: The case of commercial real estate investments in China

Consistent with the rapid growth of the Chinese economy and its commercial real estate (CRE) market, evaluating CRE investments in China is becoming an important application area where a multi-criteria expert decision system can make a significant contribution. We used a multi-criteria expert decision system to evaluate ten actual CRE investments in China. Based on thirty-one industry experts, we identified key decision factors to consider when evaluating different CRE investment projects and determined their importance weights. We then evaluated whether or not the ten CRE investment projects were considered to be successful, taking into account the unique characteristics of each CRE investment project. We finally compared the evaluations for the ten cases with their actual performance using four different performance indicators. The results showed that the evaluations had at least 87.5% accuracy across four different performance indicators. As probably one of the first systematic and in-depth scholarly studies to evaluate CRE investment projects in China, this study contributes to a better understanding of such evaluations and helps CRE investors evaluate CRE investment projects in a more informed fashion.

Evaluation of residential land use compatibilities using a density-based IOWA operator and an ANFIS-based model: A case study of Tehran, Iran

The world is rapidly urbanized in the recent decades and urban areas are dealing with enormous changes and demands in utilizing the land units. To prevent incongruous land development, a seamless land use control planning tool, impowered by a decision support system is an imperative part of development management. This study focuses on evaluating the suitability of existing land uses through a spatial decision support system. Although the problem involves various criteria as well as expert judgments, we dealt with a multi-criteria group decision making problem. The proposed methodology consists of two main sections: density-based induced ordered weighted averaging (D-IOWA) operator as a group decision making section to combine expert opinions on the problem, and an adaptive neuro-fuzzy resonance system (ANFIS) to define the compatibility of the land uses in their surrounding environment. Expert opinions were aggregated by the D-IOWA operator and the ANFIS model was trained and then applied to the rest of the data to generate the compatibility map of the residential areas. An accuracy of 81%, and a root mean square error of 0.44 were observed. Maximum 15% variation in the outputs was observed in the sensitivity analysis. In addition to the performance measurements of the model, some other models were implemented and compared with the ANFIS model. The comparisons concluded that most of the models provided acceptable accuracies while the ANFIS model acquired the best results and confirmed to be used in similar circumstances. This study confirmed that a multi-criteria decision-making system supported by an accurate classification system can provide a basis for a detailed and accurate distribution of land use across the land unit.

An Analytical Hierarchical Process based Weighted Assessment of factors contributing Precipitation

The analytical hierarchical process is a prioritising algorithm used in the field of multi-criteria decision-making system. AHP has a special capability to handle intangible criteria, this exclusive nature strengthened its popularity. In this work, AHP is exercised to prioritise parameters that contribute to precipitation. Precipitation is any form of water that reaches the earth surface from the atmosphere such as rain, dew, snow, etc. Recent research consider precipitation as one of the crucial phenomena of climate change and lots of modelling techniques are proposed to predict precipitation using varied parameters. The significance of the study is to address minor fallout of AHP in rank reversal and uncertainty in ranking the given parameters, by attempting to prioritise six basic climatic parameters such as maximum temperature, wind velocity, relative humidity, solar radiation and elevation, which are considered to contribute precipitation.

Decision-making model for the evaluation of industrialized housing systems in Ethiopia

Purpose The industrialized housing system (IHS) is regarded as an effective building philosophy based on off-site construction techniques to achieve rapid and cost-effective housing development. The purpose of this paper is to develop a multi-criteria decision-making support system (DMSS) model for the evaluation of housing systems to select the relevant decision factors and to identify the types and characteristics of suitable IHSs for application in a mass housing development. Design/methodology/approach A multi-criteria DMSS model with the analytical hierarchy process was designed. Based on the literature review and also the response of the ten experts’ interviews, 30 decision factors were identified for evaluation. In addition, 5 IHSs were considered as a case study for testing the model. Then, 30 professionals participated in a questionnaire survey conducted to evaluate the priority vector importance level of the decision factors and housing systems. Findings The result of the decision-making process showed that the top three decision factors are customer needs, supply chain and the construction industry. In addition, both precast concrete beam and slab blocks, as well as agro stone panels are identified as suitable housing systems. The systems have the characteristics of being lightweight, easy to produce and erect, and cost-effective, and they use local input resources and semi-skilled labor. The findings also revealed the potential and practicality of the model among multiple alternatives across multiple decision factors. Research limitations/implications The study has faced the limitations of available professionals and experts who have rich experience in the application of IHSs. In addition, there were few types of alternative IHSs and limited practice of IHSs implementation in large-scale housing construction. These challenges caused limitations to the relevant data collection. In order to address these challenges, all the available experts from the different sectors of the construction industry with the experience of IHSs construction are invited to participate and the available alternative IHSs in the market are selected for evaluation. Practical implications The rational evaluation method used to determine the important decision factors and the general characteristics of the suitable housing systems can help housing developers and decision makers in developing countries to make informed and effective decisions. Social implications The findings of the study help to address the challenge of lack of sufficient housing supply to the overwhelming housing demand that exists and identify the most important decision factors and suitable housing systems that can be applied for the rapid and decent large-scale housing developments at an affordable price. Originality/value This paper bridges the knowledge gaps that exist regarding the identification and evaluation of IHSs in Ethiopia. This study can help practitioners, housing developers, and decision makers to make informed and effective decisions regarding the evaluation and selection of IHSs.

An approach to enhance argument-based multi-criteria decision systems with conditional preferences and explainable answers

In our current society, it is very common to have access to a huge amount of information and scenarios in which it is possible to choose among a great number of alternatives. Therefore, systems that automate or assist with the decision-making process are becoming crucial. Those systems should allow users to represent their preferences accurately, even in scenarios where the user has many forms of comparing the available alternatives. In this paper, we propose an argument-based framework for multi-criteria decision-making based on conditional rules, that allows users to encode their preferences among the comparison criteria in terms of requirement and tolerance values. From these rules, arguments that are involved in the comparison process are generated. In order to comply with users’ demands for explanations of the given answers, our proposed framework can provide qualitative explanations about the argumentation process, and also an explanatory graph that shows how all the alternatives were ranked and the connections with the user’s rules that were used. We also show the results of an empirical analysis of the performance of a developed prototype.

Multi-Criteria Decision-Making System for Detecting Anomalies in the Electrical Energy Consumption of Telecommunication Facilities

The managers of the telecommunication infrastructure face the challenge of detecting and removing anomalies in the area of energy consumption. New technologies such as smart meters present new possibilities for the control and optimization of energy consumption. The aim of the article is to present the framework of a tool for the detection of anomalies related to energy consumption. The developed multi-criteria system for anomaly detection (MSFAD) consists of three methods: time series prediction with Particle Swarm Optimization (PSO), categorization based on absolute energy consumption and segmentation with the use of relative changes in energy consumption. The framework was tested on the energy consumption logs received from a telecommunications company. The analyses show that combining these methods may lead to improved feedback and increase the number of anomalies detected. That, in turn, would allow for a faster response, and increase the quality of the services provided.

Multi-Criteria Spatial Decision Making Supportsystem for Renewable Energy Development in Kazakhstan

The Republic of Kazakhstan has significant deposits of fossil fuels and is one of the largest energy producers among the countries of Central Asia. At the same time, The Republic of Kazakhstan is one of the richest countries of the world in terms of renewable resources, evaluated to over 1000 billion kWh/year. The application of therenewable energy sources (RES), both on a large scale and at the level of a single household, ensures the transformation of the energy system to a “green state”. However, these initiatives should be substantiated by relevant supportive information to promote transformation of the country's economy to a qualitative ecological state.The paper covers developed multi-criteria decision-making system (MCDM) and software tools for processing of spatial heterogeneous data which could be applied for evaluation of the RES potential.The developed system serves to evaluate the potential of usable RES as it allows the assessment of a territory of the country in terms of installing photovoltaic and wind generators.A feature of the proposed MCDM is the use of an analytical hierarchical process (AHP) in combination with the Bayesian approach, which allows obtaining two complementary assessments of the territory areas. The method allows a rough estimate in an event of lack of data.The verification performed based on the available data on the installed solar and wind power stations shows that the system gives a relatively small root-mean-square error within 15%.

SCSA: Evaluating skyline queries in incomplete data

Skyline queries have been extensively incorporated in various contemporary database applications. The list includes but is not limited to multi-criteria decision-making systems, decision support systems, and recommendation systems. Due to its great benefits and wide application range, many skyline algorithms have already been proposed in numerous data settings. Nonetheless, most researchers presume the completion of data meaning that all data item values are available. Since this assumption cannot be sustained in a large number of real-world database applications, the existing algorithms are rather inadequate to be directly applied on a database with incomplete data. In such cases, processing skyline queries on incomplete data incur exhaustive pairwise comparisons between data items, which may lead to loss of the transitivity property of the skyline technique. Losing the transitivity property may in turn give rise to the problem of cyclic dominance. In order to address these issues, we propose a new skyline algorithm called Sorting-based Cluster Skyline Algorithm (SCSA) that combines the sorting and partitioning techniques and simplifies the skyline computation on an incomplete dataset. These two techniques help boost the skyline process and avoid many unnecessary pairwise comparisons between data items to prune the dominated data items. The comprehensive experiments carried out on both synthetic and real-life datasets demonstrate the effectiveness and versatility of our approach as compared to the currently used approaches.

Multiple Criteria Decision-Making in Heterogeneous Groups of Management Experts

In commercial organizations operations, frequently some dynamic events occur which involve operational, managerial, and valuable information aspects. Then, in order to make a sound decision, the business professional could be supported by a Multi Criteria Decision-Making (MCDM) system for taking an external course of action, as, for instance, forecasting a new market or product, up to an inner decision concerning for instance, the volume of manufacture. Thus, managers need, in a collective manner, to analyze the actual problems, to evaluate various options according to diverse criteria, and finally choose the best solution from a set of various alternatives. Throughout these processes, uncertainty and hesitancy easily arise, when it comes to define and judge criteria or alternatives. Several approaches have been introduced to allow Decision Makers (DMs) to deal with. The Interval Multiplicative Preference Relations (IMPRs) approach is a useful technique and the basis of our proposed methodology to provide reliable consistent and in consensus IMPRs. In this manner, DMs’ choices are implicitly including their uncertainty while maintaining both an acceptable individual consistency, as well as group consensus levels. The present method is based on some recent results and an optimization algorithm to derive reliable consistent and in consensus IMPRs. In order to illustrate our results and compare them with other methodologies, a few examples are addressed and solved.

A multi-criteria decision-making support system for choice of method of compensation for highway construction contractors in Greece

ABSTRACTThe choice of method of contractor compensation is a complex decision that should be taken by awarding authorities (AAs) based on a systematic and scientific examination of alternative options instead of the decision-maker's knowledge, experience and intuition. Little research work has been carried out that applies multi-criteria decision-making (MCDM) methods for the choice of contract type in the construction industry. This paper contributes to this area in research by presenting a comparative analysis of the application of the Technique for Order Preference by Similarity to Ideal Situation method and the multi attribute utility theory to the problem of choosing appropriate contract types for two pilot projects in Greece with varying characteristics and AA's needs. For each project, nine selection criteria (SC) were weighted by two experts using the analytical hierarchy process, the revised Simos’ method and goal programming. Based on the results, a decision support system for the choice of contract type for highway construction contractors in Greece is proposed which utilizes the experience of professionals from AAs in Greece and abroad and applies the above-mentioned MCDM methods.

Incremental rough set approach for hierarchical multicriteria classification

Multicriteria classification refers to classify objects evaluated by a set of criteria to preference-ordered decision classes. Dominance-based rough set approach has been successfully introduced to express and reason inconsistencies with a dominance principle in multicriteria classification problems. Hierarchical attribute values exist extensively within many real-world applications, which provide a hierarchical form to organize, view and analyze data from different perspectives for accommodating the preference variability. In this study, we consider an extension of dominance-based rough set approach by applying an incremental learning technique for hierarchical multicriteria classification while attribute values dynamically vary across different levels of granulations. We formalize the dynamic characteristics of knowledge granules with the cut refinement and coarsening through attribute value taxonomies in the hierarchical multicriteria decision systems. In consequence, incremental algorithms for computing dominance-based rough approximations of preference-ordered decision classes are developed by applying the resulted prior-knowledge as the input, and only recomputing those outputs which depend on the changed attribute values. This paper presents the theoretical foundation of the proposed approach. Example analysis and experimental evaluation are also provided for illustration of the feasibility and efficiency.

Multi-criteria Decision-Making for Greenways: The Case of Trabzon, Turkey

ABSTRACTGreenways are multi-objective planning tools for sustainable development that take several criteria into account. In order to resolve the conflicts among objectives, greenway planning requires advanced strategies for decision-making processes and techniques. To aid in this, multi-criteria decision-making (MCDM) systems have significant capacity to analytically prioritize and select alternatives. This research develops a strategy for defining, prioritizing and selecting greenway alternatives with the support of Analytical Hierarchy Process (AHP) based on an empirical study in the urban region of Trabzon (Turkey). The study finds that AHP has promising capacity to analytically prioritize alternatives and rationally select the best alternative.

Analytical network process based optimum cluster head selection in wireless sensor network.

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.