Conflicting Decision Criteria Research Articles

MADM-based network selection and handover management in heterogeneous network: A comprehensive comparative analysis

As radio access technologies, processing speeds, and multimode interfaces of low-powered portable devices continue to advance, the future of wireless communication is envisioned to offer pervasive network coverage, high data rates, and a wide spectrum of services while maintaining high mobility. High data rates, wide range of services, huge connectivity, capacity, and good geographic coverage are being provided by the ultra-dense deployment of small base stations (BSs) in heterogeneous wireless networks (HWN). But dense deployment of small BSs, high mobility, network heterogeneity, imbalanced traffic, and dynamic user preferences lead to frequent handover. Network overhead, excessive energy consumption, and a decrease in service quality and user satisfaction can be due to frequent handover. So, handover management is one of the crucial challenges in the implementation of 5G and beyond in HWNs for ensuring seamless connectivity, energy efficiency, and the required quality of services and experiences. The effectiveness of handover decisions in HWNs relies on the implementation of a suitable network selection mechanism. Multi-attribute decision-making (MADM) is being used to model and analyze appropriate network selection complexities by considering a broad spectrum of intricate and conflicting decision criteria for efficient handover decisions in HWN. This article extensively explores, compares, and analyzes vital MADM techniques utilized for modeling appropriate network selection strategies in terms of algorithmic strategies, cardinality, types and significance of decision attributes, and network utilities. This article also examines, analyzes, and recognizes the recent mobility management challenges and trends in utilizing MADM strategies to tackle network selection issues in high-speed HWNs.

Route selection in multimodal supply chains: A fuzzy risk assessment model-BWM-MARCOS framework

Selecting a route in distribution networks can be considered as a multiple criteria decision-making problem owing to the involvement of conflicting decision criteria, many possible alternatives, different preferences, and uncertainties. Therefore, in this study, we proposed a new hybrid decision-making framework to select an optimal transportation route in multimodal supply chains. The proposed framework innovatively integrates the fuzzy risk assessment model (FRAM), best-worst method (BWM), and measurement of alternatives and ranking according to the compromise solution (MARCOS). FRAM-based fuzzy inference reasoning was applied to calculate the highly reliable risk magnitudes of the qualitative decision criteria. The BWM was used to determine the weights of all decision criteria to reduce the complexity and calculation procedures. MARCOS was employed to rank multimodal routes based on the effects of optimistic (ideal) and pessimistic (anti-ideal) solutions. The proposed framework was validated through an empirical study of route selection within the Greater Mekong sub-region to demonstrate its usefulness and applicability. The findings showed that the hybrid FRAM-BWM-MARCOS methodology employs the predominant features of each method to effectively optimize the route selection problem. It ranks all multimodal transportation routes and identifies the most appropriate alternative as the best compromise solution of the problem. The proposed framework can be viewed as an expert system that aids decision makers and stakeholders in developing new interactive freight distribution plans and transportation policies in fuzzy environments.

Towards a Self- sustained House: Development of an Analytical Hierarchy Process System for Evaluating the Performance of Self-sustained Houses

Nowadays self-sustained buildings have gained a great attention to minimize the ecological impact of buildings on the environment. Houses are one of the building types that have tremendous effect on the environment and need to be self-sustained. Their design need to encounter a holistic approach that deals with a large number of criteria. Most of these criteria are qualitative and are often in conflict with each other making decisions during the preliminary design stage a hard task for architects. Architects need an easy evaluation method that can deal with multi- criteria and can quantify subjective data into numerical values to give the right decisions. The aim of this paper is to present a comprehensive application of Analytical Hierarchy Process (AHP)-a Multi Criteria Decision Making Method (MCDM). It can aid architects in selecting the most optimal design alternative while taking into account multiple, and even conflicting decision criteria. Herein, it will be

A new last aggregation fuzzy compromise solution approach for evaluating sustainable third-party reverse logistics providers with an application to food industry

In today’s world, reverse logistics (RLs) activities have become increasingly crucial for companies looking for improved customer service, cost reduction, and sustainability perspectives. Limited resources, and insufficient technology and knowledge, have led manufacturing firms to cooperate with professional RL providers. However, evaluating the right third-party reverse logistics providers (3PRLPs) is a complex, uncertain multi-criteria decision-making (MCDM) problem, which is affected by many conflicting qualifications, the complexity of the human mind, and imprecise and uncertain information. This paper aims to introduce a novel framework that integrates fuzzy best worst method (BWM) and a new last aggregation fuzzy compromise solution for providing systematic decision support for organizations to select the most preferred partner. The reasons behind choosing both methods in an integrated way are that; the fuzzy BWM requires a smaller number of comparisons but can provide more reliable weights due to consistent criteria comparisons. Furthermore, the approaches based on compromise solutions are powerful decision-making tools because a compromise solution is a more feasible solution closest to the ideal that can be efficient in selecting the best alternative in the presence of conflicting decision criteria. Therefore, the fuzzy BWM approach is utilized to investigate the performance criteria of the 3PRLPs from economic, environmental, and social aspects of sustainability as well as risk factor. Expert weights and the preferences of sustainable 3PRLPs are then calculated simultaneously using a new last aggregation fuzzy compromise solution. Compared with the available literature, the proposed framework considers all possible sustainable criteria along with risk factor, which offers greater flexibility for experts to articulate their evaluations. In addition, the last aggregation fuzzy method eliminates distortion and loss of information and allows decision-makers to control the outcome’s precision. Moreover, the applicability of the proposed method is numerically demonstrated by developing a decision-support tool for the food industry. Sensitivity analysis and comparative analysis further illustrate the flexibility and practicability of proposed framework through which the decision-makers can make more accurate judgments regarding the evaluation of 3PRLPs.

Titanium alloy selection for biomedical application using weighted sum model methodology

To manufacture physical object some kinds of materials are required. Many times, lot of materials are available that satisfy the objective, in such cases material selection become very important. Material selection is however, thought-provoking and monotonous this is because of the complex connection between various conflicting decision criteria for choosing alternating material. Generally, the main objectives for material selection are cost and improvement in performance, however criteria like weight reduction, biocompatibility improvement, failure, corrosion performance are also inspirations for legitimate material choice. For instance, in the aviation industry weight reduction is main focus. If improper material used for any application, then it might lead to manufacturer and customer unsatisfaction. It can likewise lead to assembly failure as well as badly effects performance of a product, consequently productivity and profitability influenced badly and damage organization reputation. To take care of the issue of material selection, distinctive strategy has been utilized and one of the well-known techniques that have been applied is the Weighted Sum Model technique for the titanium and their alloys utilized in biomedical usage. It was observed that weighted sum model can be used to identify the ideally best Titanium alloy for biomedical applications. As a result of the findings, it has been determined that Ti-35.3Nb-7.1Zr-5.1Ta material is greatest all-rounder for biological applications. It was observed that the developed methodology can be applied to various different applications in along with material selection.

Application of TOPSIS Approach to Multi-Criteria Selection of Wind Turbines for On-Shore Sites

The remarkable developments in renewable energy at the global scale have paved the way for a sustainable future and cleaner environment. As a fundamental element of this renewable energy revolution, wind energy has received tremendous attention worldwide. In order to harness maximal energy from a wind farm, one crucial decision is the selection of a turbine that is most compatible with the geographical and topographical characteristics of the location under consideration. This selection mechanism ideally considers multiple conflicting decision criteria—i.e., an improvement in one criterion negatively impacts one or more other criteria. Therefore, a tradeoff solution is desired in which the selection criteria are simultaneously optimized to the best achievable limits. Considering the above observations, this paper proposes a TOPSIS (the technique for order of preference by similarity to ideal solution)-based wind turbine selection approach. The problem is modeled as a multi-criteria decision problem while considering hub height, wind speed, percentages of zero and rated output, and annual energy production as the decision criteria. A case study is shown for data collected from two potential sites in northern and north-western Saudi Arabia. Fifteen turbines with rated capacities ranging from 1.5 to 3 MW from various manufacturers were evaluated. Results indicate that the TOPSIS approach was effective in identifying the best turbines for each site. Furthermore, the proposed approach helped in identifying the similarities in the behavior of turbines for the two sites. A comparison of the TOPSIS approach with other multi-criteria decision-making techniques proved the robustness of the method.

Multi-Criteria Wind Turbine Selection using Weighted Sum Approach

Wind energy is becoming a potential source for renewable and clean energy. An important factor that contributes to efficient generation of wind power is the use of appropriate wind turbine. However, the task of selecting an appropriate, site-specific turbine is a complex problem. The complexity is due to the presence of several conflicting decision criteria in the decision process. Therefore, a decision is sought such that best tradeoff is achieved between the selection criteria. With the inherent complexities encompassing the decision-making process, this study develops a multi-criteria decision model for turbine selection based on the concepts of weighted sum approach. Results indicate that the proposed methodology for finding the most suitable turbine from a pool of 18 turbines is effective.

A Model Proposal for Locating Wind Turbines

Total energy demand has increased all around the world in recent years, as a result of growing population and industrialization. Most of the existing sources to generate energy are fossil fuels and it is known that fossil fuels are scarce. In order to satisfy increasing energy demand, searches for new energy generation sources have been conducted and these searches are ended with the proposal of generation of energy by using renewable sources. Decision makers are facing with some important decision problems related to energy generation from renewable sources. In the scope of this study, site selection problem which is one of the most important problems related to renewable energy is considered. Site selection decision consists of many alternatives and many conflicting decision criteria. Due to the hesitance of decision makers in criteria evaluation, a decision making approach based on hesitant fuzzy sets is proposed for solution of the problem. Weights of evaluation criteria for locating wind turbines are obtained by using the proposed decision making approach.

Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.

A utility-based fuzzy TOPSIS method for energy efficient network selection in heterogeneous wireless networks

Mobile terminals in 4G heterogeneous wireless networks continuously undergo horizontal and vertical handovers. In order for a mobile terminal to be connected to a network in the best possible way in terms of QoS performance and energy consumption, access network selection as the main decision within the handover process is obviously crucial. This paper presents a novel method that takes into account user preferences, network conditions, QoS and energy consumption requirements in order to select the optimal network which achieves the best balance between performance and energy consumption. The proposed network selection method incorporates the use of parameterized utility functions in order to model diverse QoS elasticities of different applications, and adopts different energy consumption metrics for real-time and non-real-time applications. User preferences are easily configured for different application and situation contexts through the use of linguistic assessments and their representation as triangular fuzzy numbers. The aggregation of multiple criteria for the calculation of the overall rating of the networks is performed through the use of the Fuzzy Set Representation TOPSIS method that resolves the issue of inconsistency related to conflicting decision criteria and is modified through the use of the employed utility functions for the elimination of the ranking abnormality problem. Finally, simulations are conducted in order to demonstrate how the proposed method would work and confirm its suitability and effectiveness.

A utility-based fuzzy TOPSIS method for energy efficient network selection in heterogeneous wireless networks

Mobile terminals in 4G heterogeneous wireless networks continuously undergo horizontal and vertical handovers. In order for a mobile terminal to be connected to a network in the best possible way in terms of QoS performance and energy consumption, access network selection as the main decision within the handover process is obviously crucial. This paper presents a novel method that takes into account user preferences, network conditions, QoS and energy consumption requirements in order to select the optimal network which achieves the best balance between performance and energy consumption. The proposed network selection method incorporates the use of parameterized utility functions in order to model diverse QoS elasticities of different applications, and adopts different energy consumption metrics for real-time and non-real-time applications. User preferences are easily configured for different application and situation contexts through the use of linguistic assessments and their representation as triangular fuzzy numbers. The aggregation of multiple criteria for the calculation of the overall rating of the networks is performed through the use of the Fuzzy Set Representation TOPSIS method that resolves the issue of inconsistency related to conflicting decision criteria and is modified through the use of the employed utility functions for the elimination of the ranking abnormality problem. Finally, simulations are conducted in order to demonstrate how the proposed method would work and confirm its suitability and effectiveness.

Approximate solutions of moral dilemmas in multiple agent system

Moral dilemmas are one of the major issues of current research in ethical reasoning. In particular, it is well known that admitting moral dilemmas in Standard Deontic Logic generates a family of inconsistencies that are intrinsically unsolvable. Since managing dilemmas means performing preferential reasoning, we argue that one simple approach to both types of problems is by ordering actions. We notice that in general, more than local orderings between two actions, agents have intrinsic preferences based on classification issues, like the action type, and that, once we have discharged the dilemma as it is intrinsically, preferential reasoning is performed by using a second-level choice approach. Decision theory has dealt with the problem of making decisions in presence of conflicting decision criteria, and some researcher has pointed out that this is the case of moral dilemmas as well. In practice, the choice of preferences in presence of conflicting criteria can be seen as a form of preferential-ethical reasoning. Although this is certainly an important topic in multiple agent investigations, it is definitely neglected in the current investigations. It is well known that humans are quite clever in solving moral dilemmas, and the usage they make of preferential reasoning is very complex. In this paper we address the problems of preferential-ethical reasoning in a combinatorial fashion and provide an algorithm for making decisions on moral dilemmas in presence of conflicting decision criteria. We then evaluate the complexity of the algorithm and prove that this approach can be applied in practice.

Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral.

Rank reversals in multi-criteria decision analysis with statistical modelling of ratio-scale pairwise comparisons

In multi-criteria decision analysis, the overall performance of decision alternatives is evaluated with respect to several, generally conflicting decision criteria. One approach to perform the multi-criteria decision analysis is to use ratio-scale pairwise comparisons concerning the performance of decision alternatives and the importance of decision criteria. In this approach, a classical problem has been the phenomenon of rank reversals. In particular, when a new decision alternative is added to a decision problem, and while the assessments concerning the original decision alternatives remain unchanged, the new alternative may cause rank reversals between the utility estimates of the original decision alternatives. This paper studies the connections between rank reversals and the potential inconsistency of the utility assessments in the case of ratio-scale pairwise comparisons data. The analysis was carried out by recently developed statistical modelling techniques so that the inconsistency of the assessments was measured according to statistical estimation theory. Several type of decision problems were analysed and the results showed that rank reversals caused by inconsistency are natural and acceptable. On the other hand, rank reversals caused by the traditional arithmetic-mean aggregation rule are not in line with the ratio-scale measurement of utilities, whereas geometric-mean aggregation does not cause undesired rank reversals.

A multicriteria approach to model specification and estimation

In decision theory, incommensurabilities among conflicting decision criteria are typically handled by multicriteria optimization methods such as Pareto efficiency and mean-variance analysis. In econometrics and statistics, where conflicting model criteria replace conflicting decision criteria. probability assessments are routinely used to transform disparate model discrepancy terms into apparently commensurable quantities. This tactic has both strengths and weaknesses. On the plus side, it permits the construction of a single real-valued measure of theory and data incompatibility in the form of a likelihood function or a posterior probability distribution. On the minus side, the amalgamation of conceptually distinct model discrepancy terms into a single real-valued incompatibility measure can make it difficult to untangle the true source of any diagnosed model specification problem. This paper discusses recent theoretical and empirical work on a multicriteria ‘flexible least squares” (FLS) approach to model specification and estimation. The basic FLS objective is to determine the “cost-efficient frontier’, that is, the set of estimates that are minimally incompatible with a specified set of model criteria. The relation of this work to previous work in econometrics, statistics, and system science is also clarified.

A fuzzy extension of Saaty's priority theory

We present a fuzzy method for choosing among a number of alternatives under conflicting decision criteria: a fuzzy version of Saaty's pairwise comparison method (1980) extended by de Graan (1980) and Lootsma (1981).Each ratio expressing the relative significance of a pair of factors is displayed in a matrix, from which suitable weights can be extracted. Since these ratios are essentially fuzzy-they express the opinion of a decision-maker on the importance of a pair of factors-we have adapted the above-mentioned method in such a way, that decision-makers are asked to express their opinions in fuzzy numbers with triangular membership functions. We apply the method at two distinct levels: first to find fuzzy weights for the decision criteria, and second, to find fuzzy weights for the alternatives under each of the decision criteria. By a suitable combination of these results, we obtain fuzzy scores for the alternatives, as well as their sensitivities. Using these fuzzy scores, the decision-makers should be able to make a choice for one of the alternatives.