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Role negotiation is a pivotal step in the role-based collaboration (RBC) process. There are many factors to be considered when decision makers set up criteria to evaluate group performance and agents’ abilities. In this paper, we investigate the applications of three traditional multicriteria decision making methods into role negotiation of RBC, i.e., simple additive weighting, multiplication exponent weighting, and weighted distance. The goal is to acquire ideal and satisfactory methods to set up the evaluation criteria for the role negotiation task. The main contributions of this paper include: 1) a method of generating the matrix of agent qualifications, with a discrete, usually, limited, and finite set of prespecified attributes and values; 2) the upper and lower thresholds of a role’s ability requirements, which are critical parameters for a workable team and its performance; and 3) the potential influences of specialist agents and generalist agents on the group performance. The proposed methods are verified by simulation experiments. The simulation results show that the proposed methods are reasonable, feasible, and practical. [ABSTRACT FROM AUTHOR]

In this paper, we propose an evolutionary multiobjective regenerator placement strategy for elastic optical networks (eMORP). The proposed optimization strategy uses the genetic algorithm NSGA-II to determine non-dominated solutions when the call request blocking probability and the total amount of regenerators used in the network are taken into account. In our simulations, we considered the amplified spontaneous emission noise generated by optical amplifiers (in-line, booster, and pre-amplifier) as physical impairment. Two recently proposed heuristics for regenerator assignment have been used, together with the regenerator placement strategies proposed and analyzed in this paper, for comparison purpose. The results obtained for two different network physical topologies state the efficiency of eMORP. Our regenerator placement strategy reduced considerably the call request blocking probability for the same number of regenerators in the network, as well as it acquired efficient solutions with just a fraction of the nodes with regeneration capability in comparison to other heuristics presented in the literature. [ABSTRACT FROM AUTHOR]

In existence of interval-valued parameters and managerial limitations, multicriteria decision-making (MCDM) problems become more complex than their original situation. In such complex cases, the classical MCDM solution methods mainly cannot handle the managerial constraints of the problem and, therefore, become useless. This paper focuses on an MCDM problem such as establishment of the branches of a glass production company in all districts (provinces) of Iran in existence of interval-valued parameters and some managerial limitations like total available budget, number of selected branches for establishment, etc. To overcome the aforementioned difficulties, a novel modified version of the simple additive weighting method, which is of the well-known MCDM approaches of the literature, is formulated as a mixed integer linear formulation. The novelty of this formulation can be summarized by the following points: 1) to cope with the interval values of the criteria; 2) to keep the possibility of selecting a set of best locations among all possible locations; and 3) to handle the managerial limitations of the problem. The proposed formulation is validated by the real data of the case study, and its sensitivity is studied in detail. Finally, the performance of the proposed approach is compared to an alternative proposed solution approach. [ABSTRACT FROM AUTHOR]

Software packages that meets the requirements of an organization should be appropriately investigated and evaluated. Picking up a wrong software package may adversely influence the business process and working function of an organization. Inappropriate software selection can turn out to be costly and it is a time-consuming decision-making process. This paper aims to provide a base for selecting the open source software packages based on analytic hierarchy process and technique for order preference similarity to ideal solution methodologies. In addition, the priority weights are generated and optimized by using teaching–learning based optimization approach. A well-organized algorithmic procedure is given in detail and a numerical example is examined to illustrate the validity and practicability of our proposed methodologies. [ABSTRACT FROM AUTHOR]

This paper focuses on applying multicriteria decision making tools to determine an optimal energy retrofit plan for a portfolio of buildings. We present a two-step decision making technique employing a multiobjective optimization algorithm followed by a multiattribute ranking procedure. The method aims at deciding, in an integrated way, the optimal energy retrofit plan for a whole stock of buildings, optimizing efficiency, sustainability, and comfort, while effectively allocating the available financial resources to the buildings. The proposed methodology is applied to a real stock of public buildings in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal management of the buildings’ energy efficiency. [ABSTRACT FROM PUBLISHER]

Contribution: The main contribution is to provide practitioners and researchers with an insight in efficiently and effectively employing multi-attribute decision making (MADM) methods in e-learning website selection problems. Background: Advances in information systems and the Internet have resulted in e-learning websites becoming an important interface for learners. The selection of a particular website directly affects the end users’ performance, with the objectives and expected outcomes of e-learning being largely dependent on the quality of the teaching processes and the effectiveness of online access. Evaluation and selection of these websites has thus become a decisive issue for learners and developers. Intended Outcomes: This paper is expected to provide decision support to leaders in education and educational organizations in effectively analyzing and managing their e-learning system. A knowledge-based system may be developed to develop and assess e-learning websites in a meaningful way. Application Design: A hybrid MADM method, fuzzy complex proportional assessment (COPRAS) is used to evaluate, rank, and select e-learning websites of “C” programming language. The fuzzy COPRAS method is validated by comparison with the technique for order preference by similarity to ideal solution, on an existing selection problem of 12 websites available in the open literature. Findings: Fuzzy COPRAS provides a comprehensive ranking of e-learning websites based on fitness degree values. The case study results rated tutorialspoint.com as the best of the e-learning websites considered, and howstuffworks.com was rated lowest. This paper shows that fuzzy COPRAS could be useful for e-learning practitioners, developers, and the researchers. [ABSTRACT FROM AUTHOR]

Multicriteria decision making (MCDM) problems are often encountered in complex system design. Most of them need to be evaluated with a large number of interactive and qualitative indices, which are difficult to be addressed effectively through the existing methods. In this paper, a novel fuzzy Choquet integral-based grey comprehensive evaluation (GCE) method, called fuzzy grey Choquet integral (FGCI), is proposed to evaluate MCDM problems with many interactive and qualitative indices. In this method, expert evaluation of qualitative indices is represented through fuzzy linguistic values. Fuzzy values are defuzzified and standardized to obtain the original evaluation matrix. The original values are replaced by the correlation coefficients, which, to a certain extent, eliminate the influence of experts’ subjective preference. An improved teaching-learning-based optimization algorithm is employed to identify $ {\lambda }$ -fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among ${\lambda }$ -fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of ${\lambda }$ -fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Until now, multi-hop device-to-device (MD2D) routing in mobile wireless networks has been designed to operate over a single-protocol routing framework. Consequently, to the best of our knowledge, no framework has been developed to enable the dynamic deployment and switching of multiple MD2D routing protocols under one framework. This paper proposes a multi-protocol framework to introduce the idea of clustering a cell and deploying different MD2D routing protocols for each cluster based on the cluster requirements. To this end, four clusters are developed with varying network conditions (i.e., node density, mobility rate, and the number of flows). Then, the performance of our two previously designed multi-hop routing protocols, namely hybrid SDN architecture for wireless distributed networks (HSAW) and source-based virtual ad hoc routing protocol (VARP-S), are investigated in each cluster in terms of energy consumption, end-to-end (E2E) delay, packet loss, and cellular-band overhead. Based on the achieved simulation results, a multiple-criteria decision-making (MCDM) approach based on the analytic hierarchy process (AHP) is developed to choose the most suitable protocol for each cluster to provide the best performance. The simulation results indicate that our proposed multi-protocol framework provides better performance compared to traditional single-protocol architectures. [ABSTRACT FROM AUTHOR]

With increasing renewable distributed energy resources integrated into a microgrid (MG), it is difficult to realize its optimal operation due to various uncertainties, e.g., power outputs of wind turbines and photovoltaic cells. This paper presents a flexible scheduling model for the optimal operation of an MG in which the expectation of operational cost and the robustness of scheduling solutions are considered, simultaneously. To balance the tradeoff between minimizing the expectation of operational cost and enhancing robustness, the proposed flexible scheduling model of MG is formulated as a multiobjective optimization problem by introducing a robustness preference parameter. In this way, the relationship between the expected operational cost and the robustness of scheduling solution is investigated, and the fuzzy decision making method is used for selecting a suitable solution. Finally, a modified MG is used for case study, and simulation results verify the applicability and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper aims at providing a novel techno-economic flexibility index to quantify the relative flexibility of different conventional generation technologies. The proposed measure takes into account a comprehensive set of technical flexibility characteristics of generation units including minimum stable generation level, operating range, minimum up/down times, and ramp up/down capability. The impact of various technical parameters on the flexibility restriction is determined based on the additional costs imposed to the system operator in order to satisfy that constraint. Afterward, the calculated flexibility index is incorporated into day-ahead market clearing procedure as a new objective function beside operation cost using a multiobjective decision-making approach. Simulation results show that the need of different flexibility levels changes the optimal generation dispatch and results in different operation costs. Furthermore, the impact of two emerging flexible options, i.e., bulk energy storages and a typical demand response program in the flexibility contribution of other generation technologies is also investigated. The results reveal that the integration of emerging flexible options frequently changes the flexibility share of conventional units and can provide the required flexibility level at a lower cost. [ABSTRACT FROM AUTHOR]

This paper proposes a new multiobjective hybrid particle swarm optimization (PSO)–krill herd (KH) Pareto-based optimization algorithm to optimize number and location of the measurement devices for accurate state estimation (SE) in smart distribution networks. Three objectives are considered to be minimized: 1) the total cost; 2) the average relative percentage error (APE) of bus voltage magnitude; and 3) APE of bus voltage angle. As the objective functions are conflicting with respect to each other, a multiobjective Pareto-based nondominated sorting hybrid PSO–KH optimization algorithm is proposed. In this approach, the random variation in loads and the metrological error of the measurement devices are also taken into account. The proposed algorithm minimizes the cost and enhances the accuracy of the distribution state estimator for better monitoring and control of the system. Furthermore, the impacts of distributed generation on SE performance are also investigated. The feasibility of the proposed algorithm is demonstrated on IEEE 69-bus system and practical Indian 85-bus radial distribution network. The results obtained are compared with conventional KH algorithm and PSO, with well-known multiobjective nondominated sorting genetic algorithm and also with an existing technique based on dynamic programming method for validation. [ABSTRACT FROM AUTHOR]

The Bonferroni mean (BM) operator, originally introduced by Bonferroni, assumes homogeneous relationship among the input arguments. Recently, extended BM (EBM) operator is developed to capture heterogeneous relationship among real arguments and linguistic 2-tuple data. In this paper, we investigate the EBM operator with Atanassov’s intuitionistic fuzzy sets (AIFSs) by using additive generators of strict t-conorms in the operations of AIFSs. This new operator is referred to as AIF-EBM operator. We also define weighted AIF-EBM (WAIF-EBM) operator by using the said operations. Moreover, we investigate several desirable properties of the proposed operators and we prove that some known specific intuitionistic fuzzy aggregation operators are special cases of the proposed AIF-EBM and WAIF-EBM operators. Subsequently, by utilizing the proposed operators, we develop a new approach for determining criteria weights, where criteria are heterogeneously interrelated. The contribution ends by introducing a numerical example with a comparative analysis of the proposed approach and the existing processes. The results of the numerical example are also analyzed with parameterized strict t-conorms. [ABSTRACT FROM PUBLISHER]

The execution of industrial projects is subject to uncertainties that cause deviations from the designated project schedules. Therefore, it is desired to generate robust schedules in the sense of being insensitive to project disruptions, such as deviations in activity durations. Beyond that, efficient schedules that offer high robustness at a low cost are preferred. This paper focuses on the discrete time-cost tradeoff problem and develops a new method for identifying and ranking efficient schedules. First, a large schedule pool with robust schedules is generated. Efficient schedules are then identified and ranked by applying data envelopment analysis (DEA) with a particular super-efficiency model. An extensive evaluation based on a project testbed and an example construction project shows that the proposed method finds and ranks efficient schedules, that can be suitably presented to a project manager for decision support. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is a widely used tool of risk assessment to identify and eliminate failures of products and systems. However, the conventional FMEA has some defects, such as the same importance of risk factors, utilizing crisp numbers to evaluate failures without considering vagueness. Although fuzzy methods are used to improve the conventional FMEA, the fuzzy FMEA requires much priori information (e.g., fuzzy membership function), which cannot flexibly reflect the changes of a decision maker's preference. In addition, most of the previous methods suppose that decision makers are totally rational without considering their psychological factors. Actually, FMEA members’ judgments on the failure modes are often influenced by their bounded rationality. To solve those problems, an integrated FMEA model is proposed in this paper, which integrates the strength of variable precision rough set theory in handling vagueness and the merit of TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) approach in manipulating bounded rationality of decision makers. Finally, the proposed method is validated with a case study of a steam valve system to demonstrate its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Based on our previous work, this paper presents a dynamic hybrid framework, called DyHF, for solving constrained optimization problems. This framework consists of two major steps: global search model and local search model. In the global and local search models, differential evolution serves as the search engine, and Pareto dominance used in multiobjective optimization is employed to compare the individuals in the population. Unlike other existing methods, the above two steps are executed dynamically according to the feasibility proportion of the current population in this paper, with the purpose of reasonably distributing the computational resource for the global and local search during the evolution. The performance of DyHF is tested on 22 benchmark test functions. The experimental results clearly show that the overall performance of DyHF is highly competitive with that of a number of state-of-the-art approaches from the literature. [ABSTRACT FROM AUTHOR]

Designing soft errors resilient systems is a complex engineering task, which nowadays follows a cross-layer approach. It requires a careful planning for different fault-tolerance mechanisms at different system's layers: starting from the technology up to the software domain. While these design decisions have a positive effect on the reliability of the system, they usually have a detrimental effect on its size, power consumption, performance and cost. Design space exploration for cross-layer reliability is therefore a multi-objective search problem in which reliability must be traded-off with other design dimensions. This paper proposes a cross-layer multi-objective design space exploration algorithm developed to help designers when building soft error resilient electronic systems. The algorithm exploits a system-level Bayesian reliability estimation model to analyze the effect of different cross-layer combinations of protection mechanisms on the reliability of the full system. A new heuristic based on the extremal optimization theory is used to efficiently explore the design space. Two exploration strategies are proposed. The first strategy aims at optimizing the reliability of the system alone. It is suited in those cases in which reaching a given reliability target is the sole goal. It focuses on finding a reduced set of system's components that, when protected, allow the designer to reach the desired reliability level. As a positive effect, by reducing the number of protected components, the overhead introduced by the fault tolerance techniques is reduced as well. The second strategy jointly considers the effect that the introduced fault-tolerance mechanisms have on the execution time, power, hardware area and software size. This strategy supports the exploration of the design space setting multiple objectives on different design dimensions. An extended set of simulations shows the capability of this framework when applied both to benchmark applications and realistic systems, providing optimized systems that outperform those obtained by applying state-of-the-art cross-layer reliability techniques. [ABSTRACT FROM AUTHOR]

Every year 2.3 million people die worldwide due to occupational illnesses and accidents at work. By analyzing the workers’ behavior when in the presence of risks managers could assign tasks to those workers who appear to be the most sensitive to risk being assigned and are thus more likely to exert more caution in the presence of that risk. This paper presents a novel multiobjective formulation of the personnel assignment problem, maximizing workers’ sensitivity to risk, while minimizing cost and dislike for the task assigned. A worker’s sensitivity to risk for a task is quantified by a new measure, carefulness, which stems from the worker’s behavior and various human factors that affect the interaction with the risk. The problem is solved using a mixed evolutionary and multicriteria decision making methodology. An approximation of the Pareto front is first generated through the nondominated sorting genetic algorithm II. A hybrid decisional approach then exploits the technique for order of preference by similarity to ideal solution in order to select the Pareto-optimal solution that represents the nearest compromise to the decision-maker’s preferences. These preferences are derived through a fuzzy version of the analytic hierarchy process. The proposed framework was tested in four real-world scenarios related to manufacturing companies. The results show a significant increase in overall carefulness and a strong decrease in the dislike for the task assigned, with a modest increase in cost. The framework thus improves the work climate and reduces the risk occurrence and/or the impact on the workers’ health. [ABSTRACT FROM AUTHOR]

Enterprise design decisions include choosing among operational strategies, allocating scarce resources, and coordinating actions toward common goals, among others. In this paper, we explore how the concepts and applications of the efficiency performance measurement paradigm can be used to inform enterprise design. Up to date, efficiency performance measurement has been mainly used to describe how well an enterprise has performed, i.e., answering “what happened” via an ex post evaluation. However, informing enterprises on “what might happen” when making changes, i.e., an ex ante evaluation, has received less attention. To address this gap, we synthetize findings pertaining to enterprise design from the organizational design, enterprise systems engineering, and efficiency performance measurement literatures. The synthesis allows us to identify the integrative role that the efficiency performance measurement paradigm plays in informing enterprise transformation decisions. We illustrate this role by providing a case study of an enterprise dealing with design decisions. Our research provides exploratory evidence, for managerial stakeholders, about the ability of the efficiency performance measurement paradigm to answer how well an enterprise could perform if certain design decisions are made. [ABSTRACT FROM AUTHOR]

In recent years, the ever-rising return streams for repair service have forced the electronics manufacturers to expand their reverse logistics capacities. However, most existing papers on the reverse logistics network design neglected the time sensitivity of the return flows. Moreover, most of these investigations were primarily concerned with the single objective problems of either minimizing the total cost or maximizing the profit. In this paper, we propose a biobjective mixed-integer linear programming model for the multiperiod design problem of a reverse logistics network for repair service. A multiperiod setting is taken into account to make the reverse logistics network flexible to accommodate the gradual changes in the capacity of the facilities and the network configuration. To solve the NP-hard problem with biobjective, we develop a hybrid evolutionary algorithm that combines nondominated sorting genetic algorithm II (NSGA-II) with a local search method. We compare the hybrid evolutionary algorithm with NSGA-II and ϵ-constraint method using numerical examples. The comparison results indicate that the hybrid evolutionary algorithm outperforms the NSGA-II in most cases. The ϵ-constraint method performs best for the small instances, but it cannot solve large instances within reasonable time. Finally, an extensive parametric analysis is conducted and several managerial insights are derived. [ABSTRACT FROM AUTHOR]

Metaheuristics have recently become the forefront of the current research as a powerful way to deal with many electromagnetic optimization problems. Based on the simulation of the herding behavior of krill individuals, a krill herd (KH) algorithm was recently proposed to solve optimization problems. In order to extend the classical mono-objective KH algorithm approach, this paper proposes a new multiobjective KH (MOKH) algorithm and a modified MOKH approach using the beta distribution in the inertia weight tuning. Numerical results on a multiobjective constrained brushless direct current motor design problem show that the evaluated MOKH algorithms present a promising performance. [ABSTRACT FROM AUTHOR]

Mobile wireless devices can now act as push-based data broadcasters, disseminating information in crowded places. While assuming this role, a device must take into account various factors such as the mean service time, energy expenditure, or copyright costs. Apart from adhering to multiobjective optimality, a broadcast schedule must also be as small as possible in size to be producible and cacheable on a mobile device. This paper analytically proves the existence of optimal schedules that are finite and minimal in size. Furthermore, the optimality of the schedule is allowed to refer to an unlimited number of criteria. The analysis shows that multiobjective optimality can be met precisely by extremely small schedules, enabling their use in mobile devices. Simulations indicate perfect efficiency of the minimal schedules in realistic application scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, we present a new method for fuzzy multiple attributes group decision-making based on the proposed ranking method of trapezoidal interval type-2 fuzzy sets and the TOPSIS method. Firstly, we present a new method for ranking trapezoidal interval type-2 fuzzy sets. Secondly, we construct the decision matrix and the average decision matrix, respectively. Thirdly, we construct the weighting decision matrix and average weighting decision matrix, respectively. Fourthly, we construct the ranking decision matrix by calculating the ranking values of trapezoidal interval type-2 fuzzy sets in the average decision matrix. Then, we get the absolute positive ideal solution and the absolute negative ideal solution with respect to the attributes based on the ranking decision matrix. Then, we calculate the distance between each alternative and the absolute positive ideal solution and calculate the distance between each alternative and the negative ideal solution, respectively. Finally, we calculate the relative degree of closeness of each alternative. The larger the value of the relative degree of closeness of an alternative, the better the preference order of the alternative. The proposed fuzzy multiple attributes group decision-making method can overcome the drawbacks of the existing method. [ABSTRACT FROM AUTHOR]

In this paper, hybridization of invasive weed optimization (IWO) and space transformation search (STS) are presented to solve, by applying multiple populations for multiple objectives individually, multiobjective optimization. This whole process is addressed as hybrid cooperative multiobjective optimization IWO (HCMOIWO). We carried out an application to solve system of nonlinear equations. In HCMOIWO, M single objectives are optimized simultaneously using the hybrid IWO with STS and all the nondominated solutions that are extracted from the group of parent weeds and offspring are stored in an archive, A . This archive is used not only to store nondominated solutions, but also to exchange information among subpopulations to explore the new search areas along the Pareto front. To exploit the nondominated solutions, a local search technique is adopted in HCMOIWO. The performance of HCMOIWO is evaluated with different sets of benchmark problems having different characteristics. Empirical results reveal the supremacy of HCMOIWO over state-of-the-art algorithms reported in recent literature. [ABSTRACT FROM AUTHOR]

The anticipated increase in electric vehicles (EVs) adoption necessitates the need for electrified transportation infrastructure to charge these vehicles. Although the EV parking garage can represent a good investment opportunity, it brings more challenges to the distribution system operator. Therefore, the allocation and sizing of the parking garage should be carefully planned. The planning process should take into consideration the economic aspects of the investor, as well as the technical aspects of the distribution system. In this paper, a bilayer Pareto multiobjective optimization problem is formulated to optimally allocate and size an EV parking garage. The optimization formulation tries to maximize the profits of the EV parking garage investor, as well as minimize the losses and voltage deviations for the distribution system operator. Dealing with these contradicting objectives simultaneously will results in a set of Pareto solutions. The results showed the different tradeoffs that might be induced while dealing with these contradictory objectives. A decision-making criterion based on statistics was used to decide on the optimal location and size of the parking garage. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location was also performed. [ABSTRACT FROM PUBLISHER]

In multicriteria decision aiding (MCDA), the Choquet integral has been used as an aggregation operator to deal with the case of interacting decision criteria. In this context, a practical problem that arises is related to the identification of the parameters associated with the Choquet integral, which are known as the Choquet capacities. In this paper, we address the problem of capacity identification by means of unsupervised learning, which, in MCDA, refers to the situations in which only the decision matrix is available. Our contribution is twofold. First, we discuss the extension of some previous works on the subject as well as some of their limitations. Then, we introduce a novel method, which is able to associate the parameters of the Choquet integral with the decision table correlation structure. As attested by numerical experiments, the proposed approach is conceptually simple to be implemented and can detect interactions between criteria in a data-driven fashion. [ABSTRACT FROM PUBLISHER]

Measurement is customarily performed to acquire information useful to support decisions. More and more frequently, the complexity of analyzed systems and problems requires to measure various heterogeneous properties and to process the returned results in order to obtain information directly exploitable to support decision-making activities. However, despite a high relevance for many engineering applications, methods for rigorously processing data about heterogeneous empirical properties have not been properly analyzed yet. As a result, decision makers attain their conclusions by subjectively fusing together the available measurement results without following a formal procedure, which has a negative impact on the verifiability and the reliability of the whole evaluation process. This paper proposes a simple and effective multicriteria decision-making method that can be applied whenever measurement results are available for the considered decision criteria. Moreover, unlike most of the scientific literature on decision making, the proposed method provides not only the most desirable decision outcome, but it also returns information on the outcome reliability. This is achieved by leveraging on measurement fundamentals. In particular, by expressing measurement uncertainty using interval values, the related interval priorities of the considered decision alternatives can be determined. The proposed methodology is finally applied to a dependability-benchmarking case study. [ABSTRACT FROM AUTHOR]

This paper develops a new method called bi-technique for order preference by similarity to ideal solution (Bi-TOPSIS) to address the multicriteria decision making problems involving interrelated criteria using bipolar measurement with negative values. The Bi-TOPSIS method incorporates the capability of the bi-capacity technique into the TOPSIS to address two important issues: 1) how to measure the interactions between criteria and 2) how to aggregate values measured on a bipolar scale. In practical applications, this method allows the use of benchmarks to demarcate “good” from “bad” performance, thus enhancing the interpretability of the evaluation results. To examine its effectiveness, an empirical study on the evaluation of city social sustainability is conducted. The outcome of the study demonstrates the feasibility and applicability of the new Bi-TOPSIS method. [ABSTRACT FROM AUTHOR]

In this paper, we study the multiattribute decision-making (MADM) problem with intuitionistic fuzzy values that represent information regarding alternatives on the attributes. Assuming that the weight information of the attributes is not known completely, we use an approach that utilizes the relative comparisons based on the advantage and disadvantage scores of the alternatives obtained on each attribute. The relative comparison of the intuitionistic fuzzy values in this research use all the three parameters, namely membership degree (“the more the better”), nonmembership degree (“the less the better”), and hesitancy degree (“the less the better”), thereby leading to the tradeoff values of all the three parameters. The score functions (advantage and disadvantage scores) used for this purpose are based on the positive contributions of these parameters, wherever applicable. Furthermore, these scores are used to obtain the strength and weakness scores leading to the satisfaction degrees of the alternatives. The optimal weights of the attributes are determined using a multiobjective optimization model that simultaneously maximizes the satisfaction degree of each alternative. The optimal solution is used for ranking and selecting the best alternative on the basis of the overall attribute values. To validate the proposed methodology, we present a numerical illustration of a real-world case. The methodology is further extended to treat MADM problem with interval-valued intuitionistic fuzzy information. Finally, a thorough comparison is done to demonstrate the advantages of the solution methodology over the existing methods used for the intuitionistic fuzzy MADM problems. [ABSTRACT FROM PUBLISHER]

This paper presents a positive approach in an optimized design of a combinatory unified power-quality conditioner (UPQC) and superconducting fault current limiters (SFCLs). This is based on a normalized simulated annealing algorithm compared with analytic hierarchy process objective optimization. The optimization algorithm for simultaneous optimization computes and produces three-dimensional alignments in Pareto front at the end of the optimization run. The results show that the SFCL can reduce the volt-ampere rating of the UPQC by limiting the fault current, thereby reducing the cost of UPQC installation. The aforementioned algorithm requires advanced numerical techniques for simulation studies by PSCAD/EMTDC on a sample distribution system for determining a global optimal combination of UPQC and SFCL by considering individual parameters and accounting for the constraints, which is the main motivation of this paper. This will result in a more thorough knowledge of “scaling factors” and the system quality mechanism which will enable the most efficient systems from the viewpoints of cost cutting, energy savings, and downsizing. [ABSTRACT FROM AUTHOR]

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE +EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design. [ABSTRACT FROM PUBLISHER]

The broadband capacity of the twisted-pair lines strongly varies within the copper access network. It is therefore important to assess the ability of a digital subscriber line (DSL) to support the DSL services prior to deployment. This task is handled by the line qualification procedures, where the identification of the line topology is an important part. This paper presents a new method, denoted topology identification via model-based evolutionary computation (TIMEC), for line topology identification, where either one-port measurements or both one- and two-port measurements are utilized. The measurements are input to a model-based multiobjective criterion that is minimized by a genetic algorithm to provide an estimate of the line topology. The inherent flexibility of TIMEC enables the incorporation of a priori information, e.g., the total line length. The performance of TIMEC is evaluated by computer simulations with varying degrees of information. Comparison with a state-of-art method indicates that TIMEC achieves better results for all the tested lines when only one-port measurements are used. The results are improved when employing both one- and two-port measurements. If a rough estimate of the total length is also used, near-perfect estimation is obtained for all the tested lines. [ABSTRACT FROM AUTHOR]

This paper presents an improved simulated annealing (SA) algorithm for multi-objective optimization, which is a positive approach in the design of high-temperature superconducting (HIS) fault-current limiters (SFCLs). The main goal of this paper is to achieve an effective and feasible approach in the structural design of HTS FCLs by means of multiobjective decision-making techniques, based on normalized SA. The combination of electrical and thermal models of a purpose-designed resistive-type HTS FCL is defined as a component in PSCADIEMTDC simulations from which the proposed method will be used to optimize the selective parameters of the SFCL. The above requires the need of advanced numerical techniques for simulation studies by PSCAD on a sample distribution system for determining a global optimum HIS FCL, by considering individual parameters and accounting for the constraints, which is the main motivation for initiating this paper. [ABSTRACT FROM AUTHOR]

In the context of additive multiattribute aggregation, we address problems with ordinal information, i.e., considering a ranking of the weights (the scaling coefficients). Several rules for ranking alternatives in these situations have been proposed and compared, such as the rank-order-centroid weight, minimum value, central value, and maximum regret rules. This paper compares these rules, together with two rules that had never been studied (quasi-dominance and quasi-optimality) that use a tolerance parameter to extend the concepts of dominance and optimality. Another contribution of this paper is the study of the behavior of these rules in the context of selecting a subset of the most promising alternatives. This study intends to provide guidelines about which rules to choose and how to use them (e.g., how many alternatives to retain and what tolerance to use), considering the contradictory goals of keeping a low number of alternatives yet not excluding the best one. The comparisons are grounded on Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the study of evolutionary algorithms for solving multiobjective optimization problems with a large number of objectives. First, a comparative study of a newly developed dynamical multiobjective evolutionary algorithm (DMOEA) and some modern algorithms, such as the indicator-based evolutionary algorithm, multiple single objective Pareto sampling, and nondominated sorting genetic algorithm II, is presented by employing the convergence metric and relative hypervolume metric. For three scalable test problems (namely, DTLZ1, DTLZ2, and DTLZ6), which represent some of the most difficult problems studied in the literature, the DMOEA shows good performance in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions. Second, a new definition of optimality (namely, L-optimality) is proposed in this paper, which not only takes into account the number of improved objective values but also considers the values of improved objective functions if all objectives have the same importance. We prove that L-optimal solutions are subsets of Pareto-optimal solutions. Finally, the new algorithm based on L-optimality (namely, MDMOEA) is developed, and simulation and comparative results indicate that well-distributed L-optimal solutions can be obtained by utilizing the MDMOEA but cannot be achieved by applying L-optimality to make a posteriori selection within the huge Pareto nondominated solutions. We can conclude that our new algorithm is suitable to tackle many-objective problems. [ABSTRACT FROM AUTHOR]

In many real-world problems, the range of consequences of different alternatives are considerably different. In addition, sometimes, selection of a group of alternatives (instead of only one best alternative) is necessary. Traditional decision making approaches treat the set of alternatives with the same method of analysis and selection. In this paper, we propose clustering alternatives into different groups so that different methods of analysis, selection, and implementation for each group can be applied. As an example, consider the selection of a group of functions (or tasks) to be processed by a group of processors. The set of tasks can be grouped according to their similar criteria, and hence, each cluster of tasks to be processed by a processor. The selection of the best alternative for each clustered group can be performed using existing methods; however, the process of selecting groups is different than the process of selecting alternatives within a group. We develop theories and procedures for clustering discrete multiple criteria alternatives. We also demonstrate how the set of alternatives is clustered into mutually exclusive groups based on 1) similar features among alternatives; 2) ideal (or most representative) alternatives given by the decision maker; and 3) other preferential information of the decision maker. The clustering of multiple criteria alternatives also has the following advantages. 1) It decreases the set of alternatives to be considered by the decision maker (for example, different decision makers are assigned to different groups of alternatives). 2) It decreases the number of criteria. 3) It may provide a different approach for analyzing multiple decision makers problems. Each decision maker may cluster alternatives differently, and hence, clustering of alternatives may provide a basis for negotiation. [ABSTRACT FROM AUTHOR]

Multiattribute decision-making involves choosing from a set of alternatives each of which is evaluated along multiple criteria that reflect the dimensions of interest to the goals and values of the decision-maker. Dominance-based decision-making narrows down the focus of the decision to the Pareto Optimal Set—the subset of alternatives consisting of those that are not dominated by other alternatives according to the criteria of evaluation. The elimination of dominated alternatives can be argued to be a compelling principle of rationality since each dominated alternative is logically inferior to its dominating alternative, given the criteria of evaluation. One kind of uncertainty in multiattribute decision making arises out of noisy or inaccurate criteria evaluations. The application of the principle of dominance is not quite rational if the criteria evaluations are known to be noisy. In this paper, we see how dominance-based decision-making can be applied to multiattribute decision-making problems with uncertainty due to noisy criteria values. In particular it will be shown that, for bounded uncertainty it is possible to produce the smallest sufficient subset that is guaranteed to contain all of the nondominated alternatives, and the largest necessary subset that contains only nondominated alternatives. For unbounded uncertainty, we will see how these notions of sufficiency and necessity can be adapted to varying degrees of probabilistic assurances desired by the decision-maker, and that the varying degrees of user assurance map naturally to a family of dominance rules, from which an appropriate rule can be selected based on the uncertainty and expression of desired user-assurance. [ABSTRACT FROM AUTHOR]

In multiobjective optimization, tradeoff analysis plays an important role in determining the best search direction to reach a most preferred solution. This paper presents a new explicit interactive tradeoff analysis method based on the identification of normal vectors on a noninferior frontier. The interactive process is implemented using a weighted minimax formulation by regulating the relative weights of objectives in a systematic manner. It is proved under a mild condition that a normal vector can be identified using the weights and Kuhn-Tucker (K-T) multipliers in the minimax formulation. Utility gradients can be estimated using local preference information such as marginal rates of substitution. The projection of a utility gradient onto a tangent plane of the noninferior frontier provides a descent direction of disutility and thereby a desirable tradeoff direction, along which tradeoff step sizes can be decided by the decision maker using an explicit tradeoff table. Necessary optimality conditions are established in terms of normal vectors and utility gradients, which can be used to guide the elicitation of local preferences and also to terminate an interactive process in a rigorous yet flexible way. This method is applicable to both linear and nonlinear (either convex or nonconvex) multiobjective optimization problems. Numerical examples are provided to illustrate the theoretical results of the paper and the implementation of the proposed interactive decision analysis process. [ABSTRACT FROM AUTHOR]

This paper analyzes the optimal problems of dc-segmentation for multi-infeed HVDC systems based on stability performance and studies the selecting method for the optimal dc-segmentation scheme. First, three major stability problems of multi-infeed HVDC systems are pointed out, which implies the necessity of the optimization for dc-segmentation. Second, three typical evaluation indices which correspond to the above three stability problems are introduced: multi-infeed interaction factor, multi-infeed effective short-circuit ratio, and frequency bias factor. To improve evaluation effects of the typical indices, the subindices are presented. Third, in order to solve this multi-objective decision-making problem, a linear weighted sum model is built, and a comprehensive weight method is adopted to calculate the weight coefficients. Based on the stability problems, a selecting method is established to choose the optimal dc-segmentation scheme. Finally, a four-machine multi-infeed HVDC system and a practical multi-infeed HVDC system in China are taken as examples, respectively. Candidate dc-segmentation schemes of each system are evaluated by the proposed method, and the optimal one is selected. Detailed stability studies of the candidate schemes are executed. The result indicates that the stability of the optimal scheme is better than the stabilities of others, which verifies the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Different Earth-observing (EO) sensors have various capabilities for diverse observing tasks. Sensor planning services make the choice of web-ready sensors for specific observing tasks with regard to observing requests and sensor capabilities. Sensor capabilities rely on various parameters; thus, choosing EO sensors for specific observing tasks relying directly on these parameters is a multicriteria decision process. A sensor's capability can be drawn from these parameters with the help of an algorithm. Furthermore, if divided into different clusters based on capabilities, applicable sensors can be more easily chosen for a category of observing tasks. In this paper, a spaceborne EO optical sensor static capability index (SSCI) mechanism is drawn from an evaluation-and-clustering algorithm, which is composed of a self-organizing neural map in combination with weighted principal component analysis. The scheme of SSCI relies on no expert analysis system and thus is more flexible and efficient. EO scenarios of disaster reactions are among the application of this algorithm. In particular, scenarios of flooding disaster forecasting, relief aiding, and postdisaster loss assessment within the framework of International Charter on Space and Major Disasters have been utilized for experiments. They have shown that the SSCI assessing algorithm is feasible and stable, and the EO optical sensor clustering algorithm based on SSCI can offer reasonable clustering accuracies of EO optical sensors. In our experiments, the EO optical sensor SSCI computation and clustering algorithm had a time consumption within 2 s and 2 min, respectively, and memory consumption within 200 MB on a normal personal computer. [ABSTRACT FROM PUBLISHER]

The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Evolutionary clustering is a recent framework for clustering dynamic networks that introduces the concept of temporal smoothness inside the community structure detection method. Evolutionary-based clustering approaches try to maximize cluster accuracy with respect to incoming data of the current time step, and minimize clustering drift from one time step to the successive one. In order to optimize both these two competing objectives, an input parameter that controls the preference degree of a user towards either the snapshot quality or the temporal quality is needed. In this paper the detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed. The main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive. Experiments on synthetic data sets show the very good performance of the method when compared with state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

This paper presents a particle swarm optimization (PSO)-based multi-objective planning algorithm for reactive power compensation of radial distribution networks with unified power quality conditioner (UPQC) allocation. A UPQC consists of a series and a shunt inverter. The UPQC model based on phase angle control (UPQC-PAC) is used. In UPQC-PAC, the series inverter injects a voltage with controllable phase angle in such a way that the voltage magnitude at load end remains unchanged. Due to the phase angle shift, the series inverter participates in load reactive power compensation along with the shunt inverter during healthy operating condition. In the proposed approach, the optimal location, the optimal reactive power compensation required at the location, and the optimal design parameters of UPQC are determined by minimizing three objective functions: 1) the rating of UPQC, 2) network power loss, and 3) percentage of nodes with undervoltage problem. These objectives are simultaneously minimized to obtain a set of non-dominated solutions using multi-objective PSO (MOPSO). The performances of two MOPSO variants are compared and the better one is used in all subsequent studies. A load flow algorithm including the UPQC-PAC model is devised. The performance of the proposed algorithm is validated with different case studies. [ABSTRACT FROM PUBLISHER]

In this paper, we present and analyze four efficient models that produce significantly improved results by optimizing conflicting power and skew objectives in the clock network buffer sizing problem. Each model is in geometric programming format and has certain advantages, such as maximum reduction in power, robustness to process variation, and striking a balance between skew and power optimization. The buffer sizing problem is formulated as a geometric programming problem to provide globally optimal solutions to the four models. We also show that a geometric programming multiobjective model can be used to optimize both power and skew without requiring any tuning from a designer. The presented self-tuning multiobjective formulation not only provides optimal solutions for buffer sizes, but also finds the tuning parameters that result in overall combined reduction in power and skew without loss of convexity. The effectiveness of the models are illustrated on several publicly available benchmarks. The models provide on average 40% to 60% improvement in power while reducing skew in several cases. We have also proposed a smart heuristic for discretization of the continuous geometric programming solution that preserves skew and power. Finally, we provide a guideline for designers to decide which one of the proposed models is the most appropriate for their needs. [ABSTRACT FROM PUBLISHER]

This paper aims at designing a high-speed hysteresis motor rotor with permanent magnets embedded in the magnetic system. The considered motor, due to the rotational magnetization and considerable impact of harmonic losses in the rotor, is an electrical machine whose design requires the involvement of a field model. A multicriteria design procedure consisting of a computer experiment, employing time-stepping finite element and vector hysteresis models and a search for the feasible design using the analytic hierarchy process, is conducted. The feasible design is found uniquely and a prototype motor is constructed and verifyingly tested. [ABSTRACT FROM AUTHOR]

This paper presents a solution for multi-objective economic dispatch problem with transmission losses semidefinite programming (SDP) formulation. The vector objective is reduced to an equivalent scalar objective through the weighted sum method. The resulting optimization problem is formulated as a convex optimization via SDP relaxation. The convex optimization problem was solved to obtain Pareto-optimal solutions. The diversity of the solution set was improved by a nonlinear selection of the weight factor. Simulations were performed on IEEE 30-bus, 57-bus, and 118-bus test systems to investigate the effectiveness of the proposed approach. Solutions were compared to those from one of the well-known evolutionary methods. Results show that SDP has an inherently good convergence property and a lower but comparable diversity property. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel circular winding brushless dc (CWBLDC) motor, which offers high torque density as BLDC motors and low-torque ripple as permanent-magnet synchronous machines (PMSMs). The novelty of the proposed machine is the circularly connected windings and its way of commutation, which make it suitable for high-power high-performance marine propulsion applications. First, the structure and the operation principle of CWBLDC are described. The method for calculating phase current of CWBLDC is subsequently introduced and validated by prototype experiment. Then, the multiobjective optimization problem for designing a high-power low-speed CWBLDC is formulated. Through large-scale design parameter sweeping, the best type of slot–pole combination and the best design scheme is selected and validated by finite element method (FEM) simulation. Finally, a multifactor regression analysis is performed to establish the relationship between performance objectives and design variables. [ABSTRACT FROM PUBLISHER]

This paper studies a multi-objective maintenance optimization embedded within the imperfect preventive maintenance (PM) for one single-unit system subject to the dependent competing risks of degradation wear and random shocks. We consider two kinds of random shocks in the system: 1) fatal shocks that will cause the system to fail immediately, and 2) nonfatal shocks that will increase the system degradation level by a certain cumulative shock amount. Also, an improvement factor in the form of quasi-renewal sequences is introduced to modulate the imperfect maintenance by raising the degradation critical threshold proportionally. Finally, the two decision variables for maintenance scheduling, the number of PMs to replacement, and the initial PM interval, are determined by simultaneously maximizing the system asymptotic availability, and minimizing the system cost rate using the fast elitist non-dominated Sorting Genetic Algorithm (NSGA-II). Sensitivity analysis for two parameters, including imperfect PM degree, and quasi-renewal coefficient of imperfect PM interval, is performed to provide insight into the behavior of the proposed maintenance policies. The comparison results show that the optimization solution is consistent between one-objective and multi-objective optimization, and the Pareto frontier for the maintenance optimization problem can provide alternative solutions according to customer preference and resource constraints. [ABSTRACT FROM PUBLISHER]

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance. [ABSTRACT FROM PUBLISHER]

This paper presents a novel approach to solve multiobjective robotic trajectory planning problems. It proposes to find the Pareto optimal set, rather than a single solution usually obtained through scalarization, e.g., weighting the objective functions. Using the trajectory planning problem for a redundant manipulator as part of a captive trajectory simulation system, the general discrete dynamic programming (DDP) approximation method presented in our previous work is shown to be a promising approach to obtain a close representation of the Pareto optimal set. When compared with the set obtained by varying the weights, the results confirm that the DDP approximation method can find approximate Pareto objective vectors, where the weighting method fails, and can generally provide a closer representation of the actual Pareto optimal set. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of framing and time pressure on human judgment performance in a complex multiattribute judgment task. We focus on the decision process of human participants who must choose between pairwise alternatives in a resource-allocation task. We used the Analytic Hierarchy Process (AHP) to calculate the relative weights of the four alternatives (i.e., C1, C2, C3, and C4) and the judgment consistency. Using the AHP, we examined two sets of hypotheses that address the impact of task conditions on the weight prioritization of choice alternatives and the internal consistency of the judgment behavior under varying task conditions. The experiment simulated the allocation of robotic assets across the battlefield to collect data about an enemy. Participants had to make a judgment about which asset to allocate to a new area by taking into account three criteria related to the likelihood of success. We manipulated the information frame and the nature of the task. We found that, in general, participants gave significantly different weights to the same alternatives under different frames and task conditions. Specifically, in terms of ln-transformed priority weights, participants gave significantly lower weights to C2 and C4 and higher weight to C3 under gain frame than under loss frame, and also, under different task conditions (i.e., Tasks #1, #2, and #3), participants gave significantly higher weight to C4 in Task #1, lower weights to C1 and C4, higher weight to C3 in Task #2, and lower weight to C3 in Task #3. Furthermore, we found that the internal consistency of the decision behavior was worse, first, in the loss frame than the gain frame and, second, under time pressure. Our methodology complements utility-theoretic frameworks by assessing judgment consistency without requiring the use of task-performance outcomes. This work is a step toward establishing a coherence criterion to investigate judgment under naturalistic conditions. The results will be useful for the design of multiattribute interfaces and decision aiding tools for real-time judgments in time-pressured task environments. [ABSTRACT FROM AUTHOR]