Reference Point Approach Research Articles

Non-dominated sorting Harris’s hawk multi-objective optimizer based on reference point approach

A non-dominated sorting Harris’s hawk multi-objective optimizer (NDSHHMO) algorithm is presented in this paper. The algorithm is able to improve the population diversity, convergence of non-dominated solutions toward the Pareto front, and prevent the population from trapping into local optimal. This was achieved by integrating fast non-dominated sorting with the original Harris’s hawk multi-objective optimizer (HHMO). Non-dominated sorting divides the objective space into levels based on fitness values and then selects non-dominated solutions to produce the next generation of hawks. A set of well-known multi-objective optimization problems has been used to evaluate the performance of the proposed NDSHHMO algorithm. The results of the NDSHHMO algorithm were verified against the results of an HHMO algorithm. Experimental results demonstrate the efficiency of the proposed NDSHHMO algorithm in terms of enhancing the ability of convergence toward the Pareto front and significantly improve the search ability of the HHMO.

ARRIVAL TIME VALUATION OF COMMUTERS IN URBAN RAIL TRANSIT

Departure time choice of commuters is one of key decisions affecting the crowding of urban rail transit network during peak hours. It is influenced by arrival time value, the additional psychological pressure caused by in-vehicle crowding, and time uncertainty. This paper aims at investigating how commuters in urban rail transit value their arrival time at work/school. Three valuation frameworks are proposed based on the reference point approach of prospect theory. Non-linear value functions with different reference point alternatives are estimated using data from a survey and stated choice study of users of Shanghai Metro system. Results show that schedule delay with work/school start time as the only reference point cannot properly reflect the arrival time valuation of urban rail transit commuters. Instead, the valuation framework with preferred arrival time as a reference point fits best, which hits as much as 85.64% of the cases. The asymmetrical response to early-side and late-side arrivals is identified. The findings of this study provide an essential basis for the development of departure time choice model.

A Novel Assessment of Healthcare Waste Disposal Methods: Intuitionistic Hesitant Fuzzy MULTIMOORA Decision Making Approach

Waste produced from medical facilities systems incorporates a blend of dangerous waste which can posture dangers to humans and ecological receptors. Lacking administration of healthcare waste can prompt hazard to medicinal service specialists, patients, public health, communities and the wider environment. Hence, proper management of healthcare waste is imperative to reduce the associated health and environment risk. In this paper, we extend the MULTIMOORA decision making method with intuitionistic hesitant fuzzy set to evaluate the healthcare waste treatment methods. Intuitionistic hesitant fuzzy set is a generalized form of a hesitant fuzzy set. Intuitionistic hesitant fuzzy set considers the uncertainty of data in a single framework and take more information into account. The MULTIMOORA method consists of three parts namely the ratio system, reference point approach and the full multiplicative form. In the optimal ranking methods, the IHF-MULTIMOORA method is uncomplicated it is able to be used practically with high dimension intuitionistic hesitant fuzzy sets. For pathological, pharmaceutical, sharp, solid and chemical wastes, the preferred waste disposal methods are deep burial, incineration, autoclave, deep burial, and chemical disinfection, respectively.

An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges

MULTIMOORA is a useful multi-criteria decision-making technique. The output of the MULTIMOORA is a ranking obtained by aggregating the results of the ternary ranking methods: Ratio System, Reference Point Approach, and Full Multiplicative Form. In the literature of MULTIMOORA, there is not a comprehensive review study. In this paper, we conduct an overview of MULTIMOORA by categorizing and analyzing main researches, theoretically and practically. First, we go through an theoretical survey of MULTIMOORA in terms of the subordinate ranking methods, ranking aggregation tools, weighting methods, group decision-making, combination with other models, and the robustness of the method. We scrutinize the developments of MULTIMOORA based on uncertainty theories accompanied by analyzing the mathematical formulations of breakthrough models. Practical problems of MULTIMOORA are categorized into application sectors concerning industries, economics, civil services and environmental policy-making, healthcare management, and information and communications technologies. Bibliometric analyses are implemented into all studies. Also, we pose major theoretical and practical challenges. From the theoretical viewpoint, extensions of Reference Point Approach, cooperative group decision-making structure, and utilization of new uncertainty sets in MULTIMOORA model are the main challenges. From the practical viewpoint, industrial and socio-economic fields are appealing to be studied intensively.

A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances for hypoid gear product

Abstract A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances is proposed to improve competitiveness of hypoid gear product development facing with economic globalization. Firstly, to deal with the vagueness or impreciseness of the voice of customer (VOC), the fuzzy quality function deployment (fuzzy-QFD) using fuzzy weighted average method in the fuzzy expected value operator is introduced into hypoid gear manufacturing. It can convert them into the critical to qualities (CTQs), and the technical geometric and physical performance requirements. And then, the multi-objective optimization (MOO) modification of machine settings is proposed to establish a basic data-driven model for collaborative system. Different with the conventional modification only considering geometric performance, it provides an improved modification also considering the physical performances. Finally, a double-curved shell model of hypoid gear finite element is used to perform the numerical loaded tooth contact analysis (NLTCA), as well as to establish the data-driven relationships between machine settings and physical performance evaluations. Immediately, whole development is divided into three sub-problems: i) optimal operations of the noise factors by measurement and numerical control (NC) compensation, ii) identification of the prescribed ease-off topography by multi-objective optimization using iterative reference point approach and iii) modification considering geometric performance by a trust region algorithm with step strategy. The numerical instance in practical applications is given to verify the proposed methodology.

Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.

Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem involving multiple medications that can be prescribed to control the patient's blood glucose. The wide range of hyperglycemia lowering agents with varying effects and various side effects makes the decision quite difficult. This paper presents computer-aided medical decision support using a fuzzy Multi-Criteria Decision-Making (MCDM) model that hybridizes a Step-wise Weight Assessment Ratio Analysis (SWARA) method with a modification of Fuzzy Multi-Objective Optimization on the basis of a Ratio Analysis plus the full multiplicative form (FMULTIMOORA) method for pharmacological therapy selection of T2D. It makes the use of SWARA for obtaining the relative significance of every selected criterion by soliciting experts' opinions and FMULTIMOORA method for evaluation of each alternative according to all criteria based on a published clinical guideline. In this paper, an extended reference point approach is considered in the proposed hybrid MCDM model that resolves the classic reference point limitations and improves the FMULTIMOORA ranking procedure. Computational results indicate that Metformin is confirmed as the first-line medication and Sulfonylurea as the second-line add-on therapy. The Glucagon-like peptide-1 receptor agonist, Dipeptidyl peptidase-4 inhibitor, and Insulin are placed 3rd, 4th, and 5th, respectively. A sensitivity analysis is conducted to validate the model performance by comparing its result with studies in the literature, other fuzzy MCDM techniques and an interval MULTIMOORA method based on an observational dataset. The close correspondence between the final rankings of anti-diabetic agents resulted from the proposed hybrid model and other methodologies provide significant implications for endocrinologists to refer.

Multi-Criteria Analysis of Water Allocation Scenarios in a Water Scarce Basin

In 2015, a “Post-2015 Development Agenda” is defined for future global developments in water management. The proposed goal is referred to as SDG’s or Sustainable Development Goals, which will balance the economic, social and environmental dimensions of sustainable development with a strong linkage between environment and socio-economic goals. One of the major concepts of SDG’s is water security, which is the basic element of the Global Goal on Water, now defined as: “Securing Sustainable Water for All”. The proposal for a Global Goal for Water is associated with targets and indicators to help countries reach the goal by 2030. Among the five main targets specified, two major ones emphasize “water allocation” as an important support of water security, particularly in conditions of water security. This paper focuses on this latter issue in the case of water scarce Gediz Basin along the Aegean coast of Turkey, where irrigation is the major water consuming activity. In addition to allocation of water among other water use sectors and irrigation, it is important in the basin how irrigation water is allocated to various crops, as previous droughts struck crop yields the most. Thus, 16 alternative allocation scenarios are developed to assess the crop yield produced by each scenario. These alternatives are represented by social, economic and environmental indicators as criteria for selection the most favorable alternative. The selection is realized by Multi Criteria Analysis (MCA) methodology, using the Reference Point Approach (RPA). In terms of methodology, presented paper shows that RPA is a powerful tool to guide engineers and decision makers to rationally select among several possible alternatives in water allocation.

Integrasi Sentiment Analysis SentiWordNet pada Metode MOORA untuk Rekomendasi Pemilihan Smartphone

Besides specification and price, smartphone reviews can affect on consumer interest buying. This study aims to use the value of smartphone review sentiment as one of the attributes/criterias in addition to specifications and prices on the calculation of Decision Support System using MOORA method to generate smartphone recommendations. Sentiment value is obtained from sentiment analysis using SentiWordNet. There are two approaches of MOORA method used in this research, Ratio System and Reference Point Approach. Testing has been done by comparing the results of smartphone recommendations between approaches on the MOORA method, with or without sentiment analysis, on smartphone rankings based on the number of smartphone fans on the GSM Arena site. The test results show that the method of MOORA with Ratio System approach without sentiment analysis has the best accuracy among other approaches.

A comprehensive approach for the selection of a portfolio of interdependent projects. An application to subsidized projects in Spain

Abstract Interdependency is an ubiquitous concept in project portfolio selection, both at the firm and industry levels. However, despite the wide acceptance of this fact, there is a remarkable scarcity of models involving interdependencies amongst non-financial investments. This paper presents a flexible model developed to address the scarcity of interdependency models: a fuzzy interactive multi-objective model, based on the reference point approach, which elicits the evaluation of interdependencies from a group of experts in a natural way. The model is presented in a sequential way, to highlight its contributions. Then, it is applied to solve a real portfolio selection problem, consisting on a large set of investment projects eligible for a subsidy program. The portfolio selected using the model achieves a sharp reduction in the deviations from objectives compared to the portfolio originally selected by the granting authorities.

Knowledge based decision support system for appraisement of sustainable partner under fuzzy cum non-fuzzy information

PurposeThe purpose of this study is to design a DSS for construction sectors, which can determine the status of the related supplier alternatives, accompanying G-T, SC measures and their interrelated metrics. In today’s era, a supplier is observed as significant among entire agents of green supply chain (SC) management. Presently, it is determined that appraising worth of the supplier under green-traditional (G-T), SCs concerns still require the support of novel algorithmic/decision support systems (DSSs), which could embrace potential decision-making.Design/methodology/approachThe authors have proposed a DSS (consisting of the implementation of multi-level multi-criterion decision-making [ML-MCDM], reference point approach [RPA] and multi-objective optimization on the basis of simple ratio analysis [MOOSRA] methods on constructed MCDM supplier evaluation appraisement module) for measuring the performance score of clay-brick suppliers coming under G-T SCs corresponding to fuzzy and non-fuzzy information. A comparative analysis is conducted among the performance scores against alternatives, obtained by the three methods, i.e. ML-MCDM, RPA and MOOSRA, for robustly making a potential decision.FindingsThe presented research offers a DSS toward managers of construction sectors for benchmarking the performance scores against supplier alternatives under G-T SC measures and their interrelated metrics, modeled by fuzzy cum non-fuzzy information.Originality/valuePresented research work exhibited a DSS that can be used by construction sectors for benchmarking the supplier alternatives in accordance with their performance scores under G-T SCs. The MCDM G-T supplier evaluation appraisement module is constructed pertaining to small-scale clay-brick production units, located in the northern part of India to check the effectiveness of the proposed DSS.

Solving multi-objective optimization problems in conservation with the reference point method.

Managing the biodiversity extinction crisis requires wise decision-making processes able to account for the limited resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker’s preferences. Our paper reviews some of the existing approaches to solve multi-objective decision problems and presents new multi-objective linear programming formulations of two multi-objective optimization problems in conservation, allowing the use of a reference point approach. Reference point approaches solve multi-objective optimization problems by interactively representing the preferences of the decision-maker with a point in the criteria (objectives) space, called the reference point. We modelled and solved the following two problems in conservation: a dynamic multi-species management problem under uncertainty and a spatial allocation resource management problem. Results show that the reference point method outperforms classic methods while illustrating the use of an interactive methodology for solving combinatorial problems with multiple objectives. The method is general and can be adapted to a wide range of ecological combinatorial problems.

Reference Point Approach for Multi-objective Assessment and Reduction of Ground-Level O3 Air Quality Monitoring Network in Vojvodina Province, Serbia

A monitoring network of ground-level ozone (O3) concentration levels in Vojvodina Province, Serbia, has been established in several phases, resulting in nine sampling sites (monitoring stations). Because maintenance of a monitoring network is financially very demanding, identifying potentially redundant ozone sites and reducing the network to a cost-effective and functional one are challenging and complex tasks. To provide an easily applicable but reliable analytical framework that will allow decision-makers and other stakeholders to identify redundant ground O3 monitors, the reference point approach, presented by Cetinkaya and Harmancioglu (Journal of Hydrology, 2014), is adopted for a multi-objective assessment of the O3 monitoring network. The evaluation process of the stations’ performance and their ranking is implemented in several phases. Firstly, a comprehensive set of 13 performance attributes is defined and associated with location and environmental criteria, followed by defining the set of alternatives. Next, all required attribute data are collected and stations are evaluated using the reference point approach. Finally, verification of the results is performed by aggregation of ranks obtained using the ideal point multi-criteria methods compromise programing and Technique for Order of Preference by Similarity to Ideal Solution. The aggregation process is performed using the Borda count and Kemeny social choice theory methods. Results indicate that the number of stations can be significantly reduced by 67%. Also, selection of the three best performing stations enabled identification of a core network that is expected to be functionally and financially sustainable under growing environmental and economic pressure.

Modified Synchronous Vector Control Design of Multilevel Inverters for AC Grid Applications

This article presents a modified synchronous vector control design of a five-level cascaded H-Bridge inverter for integration of distributed generation (DG) resources to the power grid. The presented control strategy explores the limitations in the conventional synchronous vector control algorithm of grid-connected converter (GCC). The dynamic reference point (DRP) approach is considered to improve the performance of GCC. Compared with conventional control algorithm, DRP based control algorithm exhibits several specific advantages, such as increased reliability of the grid-interfaced converter, more active and reactive power injection capacity of the converter, and reduced cost of the converter. The objectives of control technique of the DG interface system are also employed to enhance the standard of the power grid. The optimized space vector pulse-width modulation (OSVPWM) control technique is used for grid integration. The simulation and experimental results are presented in order to validate the proposed functionalities of the grid-interfaced DG system. The effectiveness of the proposed control scheme is validated with better utilization of DG system, faster dynamics with reduced total harmonic distortion, better power factor, compensated load reactive power, and fault withstanding capability.

A multicriteria approach for analysis of the impact of GHG limiting policies on economic growth in Poland

The paper deals with the following questions: How to best proceed with a process of the macroeconomic transformation of a medium-size European country due to adjustment of its economy to the EU policy aimed at reducing CO 2 emission? What may be the consequences of the enforced emission limits for the economic development and future consumption? To answer these questions a computer-based system has been constructed including a macroeconomic model and a multicriteria optimization tool for two opposing objectives: maximization of the consumption and minimization of the greenhouse gases (GHG) emission. The model contains four production sectors with a special attention devoted to the energy sector, where four technologies are distinguished: the traditional, the modernized traditional and nuclear energy, and the renewable one. The model is the long-horizon one and describes equilibrium trajectories. The multicriteria optimization tool is developed using the reference point approach to support interactive analysis of Pareto optimal outcomes. Computational results are presented for the case of Poland. The welfare costs of the pursuing GHG limiting policy are assessed. Trajectories of the technological changes in time to achieve required limits of CO 2 emission are computed.

Similar to the category, but not the exemplars: A study of generalization.

Reference point approaches have dominated the study of categorization for decades by explaining classification learning in terms of similarity to stored exemplars or averages of exemplars. The most successful reference point models are firmly grounded in the associative learning tradition-treating categorization as a stimulus generalization process based on inverse exponential distance in psychological space augmented by a dimensional selective attention mechanism. We present experiments that pose a significant challenge to popular reference point accounts which explain categorization in terms of stimulus generalization from exemplars, prototypes, or adaptive clusters. DIVA, a similarity-based alternative to the reference point framework, provides a successful account of the human data. These findings suggest that a successful psychology of categorization may need to look beyond stimulus generalization and toward a view of category learning as the induction of a richer model of the data.

Effects of noise exposure of other people on aircraft noise tolerability using a reference point approach

Reactions to aircraft noise resulting from an airport development vary considerably among individuals. This paper examines whether the noise level tolerated by individuals is affected when they compare their own situation to the situations of other people who are more severely affected by the noise. We modeled the individual noise tolerability by adopting the reference point concept and the value function of prospect theory. A questionnaire assessed different levels of noise-affected zones at a hypothetical airport. Individual noise tolerable level was measured by a prerecorded aircraft noise presented via headphones. Decision-making associated with the reported satisfaction level regarding the changes in operational flight frequency settings at the airport. An interview survey was conducted with residents near Manila airport in the Philippines. We applied genetic algorithm to jointly estimate the parameters for noise tolerability model and the value function. Results showed that individuals tolerated more overhead flights when they considered the situations of other people worse affected by the noise than they were. The tendency of noise tolerability increased with the severity of noise exposure situations of other people, but lower reactions to noise escalation. We also observed diminishing sensitivity to changes in noise relative to the noise tolerable level. The aircraft noise tolerability and the consideration of noise exposure situations of other people appeared to be a vital aspect to be considered in airport noise management and policy.

Multi-Objective Portfolio Optimization by Mixed Integer Programming

In this PhD dissertation the mathematical programming methods of operations research for multi-criteria optimization are presented. The PhD dissertation deals with the problem of selection of methods and numerical tools for solving portfolio optimization problems with different objectives. In particular, the research efforts were concentrated on mixed integer programming formulations. The need for solving multi-objective portfolio optimization models by mixed integer programming can be illustrated for the portfolio models with Value-at-Risk (VaR) as a risk measure, as well as, when the number of assets (investment ventures) is one of the optimality criteria. An alternative, multi-objective portfolio optimization problems is formulated with Conditional Value-at-Risk (CVaR) as a risk measure or with symmetric measure of risk - covariance (variance) of return - as in Markowitz portfolio.The portfolio models with CVaR and with covariance (variance) of historical return were being solved with the use of mathematical programming with the continuous variables. The proposed multi-objective portfolio models are constructed with the expected return as a performance measure and the expected worst-case return as a risk measure, using Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). These measures allow the evaluation of worst-case return and shaping of the resulting return distribution through the selection of the optimal portfolio. The mathematical programming models are constructed and solved using weighting, lexicographic and reference point approach. The presented portfolio models are single-, bi- and triple-objectives and the optimization criteria considered are risk, return and number of stocks.The main research problem considered in this Ph.D. dissertation is the way for finding the best multi-objective portfolio formulation with risk. The additional research problem is to find the relation between the optimization results with Value-at-Risk solved by mixed integer programming and the results obtained with the use of linear and quadratic programming portfolio models (Conditional Value-at-Risk, Markowitz).Computational experiments have been conducted for multi-criteria portfolio models of stock exchange investments. The input data for computations consist of historical daily returns of stocks quoted on Warsaw Stock Exchange. The number of selected securities for input data varies from 46 to 240 assets. The historical stocks quotations come from the period from March 10th, 1997 to February 2nd, 2009. This time period includes data from the increase of stock exchange quotations, as well as the economic crisis period. The considered number of data in historical time series is from 500 to 3000 days with assets quoted each day in the whole historical horizon. The portfolios were optimized in an increased time window, which was helpful in evaluating the results of optimization (time-varying optimal portfolio).The multi-criteria portfolio optimization models with Conditional Value-at-Risk (CVaR) as a risk measure can be used to support on-line stock market investments, since the computational times required to find the optimal solution is relatively short, regardless of the size of the input data. The presented models provide a decision maker with a tool for evaluating the relationship between expected and worst-case returns.The results obtained from computational experiments proved, that multi-objective portfolio optimization models with Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) could be used to shape the distribution of portfolio returns in a favorable way for a decision maker. The portfolios obtained with both methods (mixed-integer or linear programming) are often similar results, which shows their capability of solving the corresponding problems. It means that a suboptimal portfolio for the integer program with Value-at-Risk (VaR) as optimality criterion can be found by solving the corresponding linear program for the portfolio problem with Conditional Value-at-Risk (CVaR) as an optimality criterion. The proposed scenario-based portfolio optimization problems under uncertainty, formulated as a single- or multi-objective mixed integer program were solved using commercially available software (AMPL/CPLEX) for mixed integer programming.In addition to the multi-objective approach for portfolio optimization of securities (e.g. stocks) from stock exchanges presented in this dissertation, the selected multi-objective mixed integer programming models are shown for supporting services in medical care institutions, based on an assignment problem.

Multiple objective project portfolio selection based on reference points

The problem of selecting a portfolio of research and development projects from a set of proposed projects subject to resource constraints is formulated as a multiobjective optimization problem. Three categories of objectives are identified, namely quantitative, qualitative and balance/distributional. The first two are linear in the binary decision variables, but the third involves nonlinear measures of discrepancy between desired and actual distributions of activity amongst defined categories. The result is a nonlinear combinatorial multiobjective optimization problem. A form of reference point approach is motivated and developed as a solution method, and a special purpose genetic algorithm is implemented for obtaining the solution numerically. Extensions to interactive approaches for exploration of the efficient frontier are presented and discussed.

EVALUATION OF EXCAVATOR TECHNOLOGIES: APPLICATION OF DATA FUSION BASED MULTIMOORA METHODS

Excavators are quite expensive vehicles. Therefore, there may be huge losses for decision makers if a wrong decision is made during the purchasing process. A good evaluation of excavator alternatives both reduces costs and increases the benefits the excavator for the purchaser. The aim of this study is to prioritise excavator technologies to help decision makers during the purchasing process and to apply three different “data fusion methods” instead of the “theory of dominance” of the original MULTI MOORA method. The MULTI MOORA method is composed of three methods, namely: the ratio analysis as a part of MOORA, Reference Point Theory (the reference point approach as a part of MOORA) and the Full Multiplicative Form. It is used to prioritise excavator technologies in this study. The MULTI - MOORA method combines three results obtained from these three methods using the theory of dominance. Dominance directed graph, Rank position method and Borda count method as data fusion methods are also used to combine these three results instead of the “theory of dominance”. The results from this study show that there is no difference between the data fusion methods and the MULTI MOORA method can be applied to technology evaluation of the excavator alternatives successfully.

A multibody motorcycle model with rigid-ring tyres: formulation and validation

The aim of this paper is the development and validation of a three-dimensional multibody motorcycle model including a rigid-ring tyre model, taking into account both the slopes and elevation of the road surface. In order to achieve accurate assessment of ride and handling performances of a road racing motorcycle, a tyre model capable of reproducing the dynamic response to actual road excitation is required. While a number of vehicle models with such feature are available for car application, the extension to the motorcycle modelling has not been addressed yet. To do so, a novel parametrisation for the general motorcycle kinematics is proposed, using a mixed reference point and relative coordinates approach. The resulting description, developed in terms of dependent coordinates, makes it possible to include the rigid-ring kinematics as well as road elevation and slopes, without affecting computational efficiency. The equations of motion for the whole multibody system are derived symbolically and the constraint equations arising from the dependent coordinate formulation are handled using the position and velocity vector projection technique. The resulting system of equations is integrated in time domain using a standard ordinary differential equation (ODE) algorithm. Finally, the model is validated with respect to experimentally measured data in both time and frequency domains.