Feature selection for portfolio optimization

DEFF Research Database (Denmark)

Bjerring, Thomas Trier; Ross, Omri; Weissensteiner, Alex

2016-01-01

Most portfolio selection rules based on the sample mean and covariance matrix perform poorly out-of-sample. Moreover, there is a growing body of evidence that such optimization rules are not able to beat simple rules of thumb, such as 1/N. Parameter uncertainty has been identified as one major....... While most of the diversification benefits are preserved, the parameter estimation problem is alleviated. We conduct out-of-sample back-tests to show that in most cases different well-established portfolio selection rules applied on the reduced asset universe are able to improve alpha relative...

Model to Estimate Monthly Time Horizons for Application of DEA in Selection of Stock Portfolio and for Maintenance of the Selected Portfolio

Directory of Open Access Journals (Sweden)

José Claudio Isaias

2015-01-01

Full Text Available In the selecting of stock portfolios, one type of analysis that has shown good results is Data Envelopment Analysis (DEA. It, however, has been shown to have gaps regarding its estimates of monthly time horizons of data collection for the selection of stock portfolios and of monthly time horizons for the maintenance of a selected portfolio. To better estimate these horizons, this study proposes a model of mathematical programming binary of minimization of square errors. This model is the paper’s main contribution. The model’s results are validated by simulating the estimated annual return indexes of a portfolio that uses both horizons estimated and of other portfolios that do not use these horizons. The simulation shows that portfolios with both horizons estimated have higher indexes, on average 6.99% per year. The hypothesis tests confirm the statistically significant superiority of the results of the proposed mathematical model’s indexes. The model’s indexes are also compared with portfolios that use just one of the horizons estimated; here the indexes of the dual-horizon portfolios outperform the single-horizon portfolios, though with a decrease in percentage of statistically significant superiority.

Learning to Select Supplier Portfolios for Service Supply Chain.

Science.gov (United States)

Zhang, Rui; Li, Jingfei; Wu, Shaoyu; Meng, Dabin

2016-01-01

The research on service supply chain has attracted more and more focus from both academia and industrial community. In a service supply chain, the selection of supplier portfolio is an important and difficult problem due to the fact that a supplier portfolio may include multiple suppliers from a variety of fields. To address this problem, we propose a novel supplier portfolio selection method based on a well known machine learning approach, i.e., Ranking Neural Network (RankNet). In the proposed method, we regard the problem of supplier portfolio selection as a ranking problem, which integrates a large scale of decision making features into a ranking neural network. Extensive simulation experiments are conducted, which demonstrate the feasibility and effectiveness of the proposed method. The proposed supplier portfolio selection model can be applied in a real corporation easily in the future.

IT Portfolio Selection and IT Synergy

Science.gov (United States)

Cho, Woo Je

2010-01-01

This dissertation consists of three chapters. The primary objectives of this dissertation are: (1) to provide a methodological framework of IT (Information Technology) portfolio management, and (2) to identify the effect of IT synergy on IT portfolio selection of a firm. The first chapter presents a methodological framework for IT project…

Deformed exponentials and portfolio selection

Science.gov (United States)

Rodrigues, Ana Flávia P.; Guerreiro, Igor M.; Cavalcante, Charles Casimiro

In this paper, we present a method for portfolio selection based on the consideration on deformed exponentials in order to generalize the methods based on the gaussianity of the returns in portfolio, such as the Markowitz model. The proposed method generalizes the idea of optimizing mean-variance and mean-divergence models and allows a more accurate behavior for situations where heavy-tails distributions are necessary to describe the returns in a given time instant, such as those observed in economic crises. Numerical results show the proposed method outperforms the Markowitz portfolio for the cumulated returns with a good convergence rate of the weights for the assets which are searched by means of a natural gradient algorithm.

Comparison of Portfolio Selection and Performance: Shari’ah-Compliant and Socially Responsible Investment Portfolios

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Mehmet Asutay

2015-04-01

Full Text Available This study examines the effect of Islamic screening criteria on Shari’ah-compliant portfolio selection and performance compared to Socially Responsible Investment (SRI portfolio. Each portfolio constructed from 15 stocks based on FTSE 100 using data from year 1997. Mean-variance portfolio optimization is employed with some financial ratios added as constraints for the Shari’ah portfolio. Annual expected return of each portfolio from 2008 to 2013 is used to calculate Sharpe’s ratio, Treynor ratio and Jensen’s alpha as the performance measurement tools. Macroeconomic variables are assessed using ordinary least square to examine whether they influence the portfolios’ expected returns or not. The result finds that Shari’ah portfolio has a better performance than SRI from year 2008 to 2010 shown by higher value of the measurement tools. However, from 2011 to 2013, SRI portfolio has better performance than Shari’ah portfolio. 

8th International Conference on Multi-Objective and Goal Programming

CERN Document Server

Tamiz, Mehrdad; Ries, Jana

2010-01-01

This volume shows the state-of-the-art in both theoretical development and application of multiple objective and goal programming. Applications from the fields of supply chain management, financial portfolio selection, financial risk management, insurance, medical imaging, sustainability, nurse scheduling, project management, water resource management, and the interface with data envelopment analysis give a good reflection of current usage. A pleasing variety of techniques are used including models with fuzzy, group-decision, stochastic, interactive, and binary aspects. Additionally, two papers from the upcoming area of multi-objective evolutionary algorithms are included. The book is based on the papers of the 8th International Conference on Multi-Objective and Goal Programming (MOPGP08) which was held in Portsmouth, UK, in September 2008.

Credit Portfolio Selection According to Sectors in Risky Environments: Markowitz Practice

OpenAIRE

Halim Kazan; Kültigin Uludag

2014-01-01

In this study, it was researched that how the rate of repayment of loans will be increased and how the credit risk will be minimized in banking sector, by using Markowitz Portfolio Theory. Construction, textile and wholesale and retail sectors were examined under the central bank data. Portfolio groups were selected and risks( variances of Portfolio groups) were evaluated according to Markowitz portfolio theory. Markowitz portfolio theory is effective than the other portfolio selection instru...

Portfolio Selection with Jumps under Regime Switching

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Lin Zhao

2010-01-01

Full Text Available We investigate a continuous-time version of the mean-variance portfolio selection model with jumps under regime switching. The portfolio selection is proposed and analyzed for a market consisting of one bank account and multiple stocks. The random regime switching is assumed to be independent of the underlying Brownian motion and jump processes. A Markov chain modulated diffusion formulation is employed to model the problem.

Multi-Objective Stochastic Optimization Programs for a Non-Life Insurance Company under Solvency Constraints

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Massimiliano Kaucic

2015-09-01

Full Text Available In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for bi- and tri-objective programming problems. Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based non-life insurance company. Both performance indicators and risk measures are managed as objectives. Results show that this procedure is effective and readily applicable to achieve suitable risk-reward tradeoff analysis.

Portfolio Manager Selection – A Case Study

DEFF Research Database (Denmark)

Christensen, Michael

2017-01-01

Within a delegated portfolio management setting, this paper presents a case study of how the manager selection process can be operationalized in practice. Investors have to pursue a thorough screening of potential portfolio managers in order to discover their quality, and this paper discusses how...

Portfolio selection theory and wildlife management

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JW Hearne

2008-12-01

Full Text Available With a strong commercial incentive driving the increase in game ranching in Southern Africa the need has come for more advanced management tools. In this paper the potential of Portfolio Selection Theory to determine the optimal mix of species on game ranches is explored. Land, or the food it produces, is a resource available to invest. We consider species as investment choices. Each species has its own return and risk profile. The question arises as to what proportion of the resource available should be invested in each species. We show that if the objective is to minimise risk for a given return, then the problem is analogous to the Portfolio Selection Problem. The method is then implemented for a typical game ranch. We show that besides risk and return objectives, it is necessary to include an additional objective so as to ensure sufficient species to maintain the character of a game ranch. Some other points of difference from the classical Portfolio Selection problem are also highlighted and discussed.

Continuous-Time Mean-Variance Portfolio Selection under the CEV Process

OpenAIRE

Ma, Hui-qiang

2014-01-01

We consider a continuous-time mean-variance portfolio selection model when stock price follows the constant elasticity of variance (CEV) process. The aim of this paper is to derive an optimal portfolio strategy and the efficient frontier. The mean-variance portfolio selection problem is formulated as a linearly constrained convex program problem. By employing the Lagrange multiplier method and stochastic optimal control theory, we obtain the optimal portfolio strategy and mean-variance effici...

Generalizable open source urban water portfolio simulation framework demonstrated using a multi-objective risk-based planning benchmark problem.

Science.gov (United States)

Trindade, B. C.; Reed, P. M.

2017-12-01

The growing access and reduced cost for computing power in recent years has promoted rapid development and application of multi-objective water supply portfolio planning. As this trend continues there is a pressing need for flexible risk-based simulation frameworks and improved algorithm benchmarking for emerging classes of water supply planning and management problems. This work contributes the Water Utilities Management and Planning (WUMP) model: a generalizable and open source simulation framework designed to capture how water utilities can minimize operational and financial risks by regionally coordinating planning and management choices, i.e. making more efficient and coordinated use of restrictions, water transfers and financial hedging combined with possible construction of new infrastructure. We introduce the WUMP simulation framework as part of a new multi-objective benchmark problem for planning and management of regionally integrated water utility companies. In this problem, a group of fictitious water utilities seek to balance the use of the mentioned reliability driven actions (e.g., restrictions, water transfers and infrastructure pathways) and their inherent financial risks. Several traits of this problem make it ideal for a benchmark problem, namely the presence of (1) strong non-linearities and discontinuities in the Pareto front caused by the step-wise nature of the decision making formulation and by the abrupt addition of storage through infrastructure construction, (2) noise due to the stochastic nature of the streamflows and water demands, and (3) non-separability resulting from the cooperative formulation of the problem, in which decisions made by stakeholder may substantially impact others. Both the open source WUMP simulation framework and its demonstration in a challenging benchmarking example hold value for promoting broader advances in urban water supply portfolio planning for regions confronting change.

Developing a framework for energy technology portfolio selection

Science.gov (United States)

Davoudpour, Hamid; Ashrafi, Maryam

2012-11-01

Today, the increased consumption of energy in world, in addition to the risk of quick exhaustion of fossil resources, has forced industrial firms and organizations to utilize energy technology portfolio management tools viewed both as a process of diversification of energy sources and optimal use of available energy sources. Furthermore, the rapid development of technologies, their increasing complexity and variety, and market dynamics have made the task of technology portfolio selection difficult. Considering high level of competitiveness, organizations need to strategically allocate their limited resources to the best subset of possible candidates. This paper presents the results of developing a mathematical model for energy technology portfolio selection at a R&D center maximizing support of the organization's strategy and values. The model balances the cost and benefit of the entire portfolio.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

International Nuclear Information System (INIS)

Zhou, Z; Folkert, M; Wang, J

2016-01-01

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

Energy Technology Data Exchange (ETDEWEB)

Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

Portfolio Selection Using Level Crossing Analysis

Science.gov (United States)

Bolgorian, Meysam; Shirazi, A. H.; Jafari, G. R.

Asset allocation is one of the most important and also challenging issues in finance. In this paper using level crossing analysis we introduce a new approach for portfolio selection. We introduce a portfolio index that is obtained based on minimizing the waiting time to receive known return and risk values. By the waiting time, we mean time that a special level is observed in average. The advantage of this approach is that the investors are able to set their goals based on gaining return and knowing the average waiting time and risk value at the same time. As an example we use our model for forming portfolio of stocks in Tehran Stock Exchange (TSE).

Log-Optimal Portfolio Selection Using the Blackwell Approachability Theorem

OpenAIRE

V'yugin, Vladimir

2014-01-01

We present a method for constructing the log-optimal portfolio using the well-calibrated forecasts of market values. Dawid's notion of calibration and the Blackwell approachability theorem are used for computing well-calibrated forecasts. We select a portfolio using this "artificial" probability distribution of market values. Our portfolio performs asymptotically at least as well as any stationary portfolio that redistributes the investment at each round using a continuous function of side in...

Uncertain Portfolio Selection with Background Risk and Liquidity Constraint

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Jia Zhai

2017-01-01

Full Text Available This paper discusses an uncertain portfolio selection problem with consideration of background risk and asset liquidity. In addition, the transaction costs are also considered. The security returns, background asset return, and asset liquidity are estimated by experienced experts instead of historical data. Regarding them as uncertain variables, a mean-risk model with background risk, liquidity, and transaction costs is proposed for portfolio selection and the crisp forms of the model are provided when security returns obey different uncertainty distributions. Moreover, for better understanding of the impact of background risk and liquidity on portfolio selection, some important theorems are proved. Finally, numerical experiments are presented to illustrate the modeling idea.

Portfolio selection with heavy tails

NARCIS (Netherlands)

Hyung, N.; Vries, de C.G.

2007-01-01

Consider the portfolio problem of choosing the mix between stocks and bonds under a downside risk constraint. Typically stock returns exhibit fatter tails than bonds corresponding to their greater downside risk. Downside risk criteria like the safety first criterion therefore often select corner

A proposed selection process in Over-The-Top project portfolio management

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Jemy Vestius Confido

2018-05-01

Full Text Available Purpose: The purpose of this paper is to propose an Over-The-Top (OTT initiative selection process for communication service providers (CSPs entering an OTT business. Design/methodology/approach: To achieve this objective, a literature review was conducted to comprehend the past and current practices of the project (or initiative selection process as mainly suggested in project portfolio management (PPM. This literature was compared with specific situations and the needs of CSPs when constructing an OTT portfolio. Based on the contrast between the conventional project selection process and specific OTT characteristics, a different selection process is developed and tested using group model-building (GMB, which involved an in-depth interview, a questionnaire and a focus group discussion (FGD. Findings: The paper recommends five distinct steps for CSPs to construct an OTT initiative portfolio: candidate list of OTT initiatives, interdependency diagram, evaluation of all interdependent OTT initiatives, evaluation of all non-interdependent OTT initiatives and optimal portfolio of OTT initiatives. Research limitations/implications: The research is empirical, and various OTT services are implemented; the conclusion is derived only from one CSP, which operates as a group. Generalization of this approach will require further empirical tests on different CSPs, OTT players or any firms performing portfolio selection with a degree of interdependency among the projects. Practical implications: Having considered interdependency, the proposed OTT initiative selection steps can be further implemented by portfolio managers for more effective OTT initiative portfolio construction. Originality/value: While the previous literature and common practices suggest ensuring the benefits (mainly financial of individual projects, this research accords higher priority to the success of the overall OTT initiative portfolio and recommends that an evaluation of the overall

Stock portfolio selection using Dempster–Shafer evidence theory

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Gour Sundar Mitra Thakur

2018-04-01

Full Text Available Markowitz’s return–risk model for stock portfolio selection is based on the historical return data of assets. In addition to the effect of historical return, there are many other critical factors which directly or indirectly influence the stock market. We use the fuzzy Delphi method to identify the critical factors initially. Factors having lower correlation coefficients are finally considered for further consideration. The critical factors and historical data are used to apply Dempster–Shafer evidence theory to rank the stocks. Then, a portfolio selection model that prefers stocks with higher rank is proposed. Illustration is done using stocks under Bombay Stock Exchange (BSE. Simulation is done by Ant Colony Optimization. The performance of the outcome is found satisfactory when compared with recent performance of the assets. Keywords: Stock portfolio selection, Ranking, Dempster–Shafer evidence theory, Ant Colony Optimization, Fuzzy Delphi method

Managing the Public Sector Research and Development Portfolio Selection Process: A Case Study of Quantitative Selection and Optimization

Science.gov (United States)

2016-09-01

PUBLIC SECTOR RESEARCH & DEVELOPMENT PORTFOLIO SELECTION PROCESS: A CASE STUDY OF QUANTITATIVE SELECTION AND OPTIMIZATION by Jason A. Schwartz...PUBLIC SECTOR RESEARCH & DEVELOPMENT PORTFOLIO SELECTION PROCESS: A CASE STUDY OF QUANTITATIVE SELECTION AND OPTIMIZATION 5. FUNDING NUMBERS 6...describing how public sector organizations can implement a research and development (R&D) portfolio optimization strategy to maximize the cost

An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

OpenAIRE

Chen, Wei

2014-01-01

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’ evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is ...

Portfolio optimization using fuzzy linear programming

Science.gov (United States)

Pandit, Purnima K.

2013-09-01

Portfolio Optimization (PO) is a problem in Finance, in which investor tries to maximize return and minimize risk by carefully choosing different assets. Expected return and risk are the most important parameters with regard to optimal portfolios. In the simple form PO can be modeled as quadratic programming problem which can be put into equivalent linear form. PO problems with the fuzzy parameters can be solved as multi-objective fuzzy linear programming problem. In this paper we give the solution to such problems with an illustrative example.

Volatility forecasting for low-volatility portfolio selection in the US and the Korean equity markets

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Kim, Saejoon

2018-01-01

We consider the problem of low-volatility portfolio selection which has been the subject of extensive research in the field of portfolio selection. To improve the currently existing techniques that rely purely on past information to select low-volatility portfolios, this paper investigates the use of time series regression techniques that make forecasts of future volatility to select the portfolios. In particular, for the first time, the utility of support vector regression and its enhancements as portfolio selection techniques is provided. It is shown that our regression-based portfolio selection provides attractive outperformances compared to the benchmark index and the portfolio defined by a well-known strategy on the data-sets of the S&P 500 and the KOSPI 200.

The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem

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Aytaç Adalı, Esra; Tuş Işık, Ayşegül

2017-06-01

A decision making process requires the values of conflicting objectives for alternatives and the selection of the best alternative according to the needs of decision makers. Multi-objective optimization methods may provide solution for this selection. In this paper it is aimed to present the laptop selection problem based on MOORA plus full multiplicative form (MULTIMOORA) and multi-objective optimization on the basis of simple ratio analysis (MOOSRA) which are relatively new multi-objective optimization methods. The novelty of this paper is solving this problem with the MULTIMOORA and MOOSRA methods for the first time.

A fuzzy compromise programming approach for the Black-Litterman portfolio selection model

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Mohsen Gharakhani

2013-01-01

Full Text Available In this paper, we examine advanced optimization approach for portfolio problem introduced by Black and Litterman to consider the shortcomings of Markowitz standard Mean-Variance optimization. Black and Litterman propose a new approach to estimate asset return. They present a way to incorporate the investor’s views into asset pricing process. Since the investor’s view about future asset return is always subjective and imprecise, we can represent it by using fuzzy numbers and the resulting model is multi-objective linear programming. Therefore, the proposed model is analyzed through fuzzy compromise programming approach using appropriate membership function. For this purpose, we introduce the fuzzy ideal solution concept based on investor preference and indifference relationships using canonical representation of proposed fuzzy numbers by means of their correspondingα-cuts. A real world numerical example is presented in which MSCI (Morgan Stanley Capital International Index is chosen as the target index. The results are reported for a portfolio consisting of the six national indices. The performance of the proposed models is compared using several financial criteria.

A Multiagent Evolutionary Algorithm for the Resource-Constrained Project Portfolio Selection and Scheduling Problem

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Yongyi Shou

2014-01-01

Full Text Available A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem.

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

Science.gov (United States)

Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

2017-01-01

Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

Two-Stage Fuzzy Portfolio Selection Problem with Transaction Costs

OpenAIRE

Chen, Yanju; Wang, Ye

2015-01-01

This paper studies a two-period portfolio selection problem. The problem is formulated as a two-stage fuzzy portfolio selection model with transaction costs, in which the future returns of risky security are characterized by possibility distributions. The objective of the proposed model is to achieve the maximum utility in terms of the expected value and variance of the final wealth. Given the first-stage decision vector and a realization of fuzzy return, the optimal value expression of the s...

Diversified models for portfolio selection based on uncertain semivariance

Science.gov (United States)

Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

2017-02-01

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

Mean-Coherent Risk and Mean-Variance Approaches in Portfolio Selection : An Empirical Comparison

NARCIS (Netherlands)

Polbennikov, S.Y.; Melenberg, B.

2005-01-01

We empirically analyze the implementation of coherent risk measures in portfolio selection.First, we compare optimal portfolios obtained through mean-coherent risk optimization with corresponding mean-variance portfolios.We find that, even for a typical portfolio of equities, the outcomes can be

Research on regularized mean-variance portfolio selection strategy with modified Roy safety-first principle.

Science.gov (United States)

Atta Mills, Ebenezer Fiifi Emire; Yan, Dawen; Yu, Bo; Wei, Xinyuan

2016-01-01

We propose a consolidated risk measure based on variance and the safety-first principle in a mean-risk portfolio optimization framework. The safety-first principle to financial portfolio selection strategy is modified and improved. Our proposed models are subjected to norm regularization to seek near-optimal stable and sparse portfolios. We compare the cumulative wealth of our preferred proposed model to a benchmark, S&P 500 index for the same period. Our proposed portfolio strategies have better out-of-sample performance than the selected alternative portfolio rules in literature and control the downside risk of the portfolio returns.

Stock Selection for Portfolios Using Expected Utility-Entropy Decision Model

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Jiping Yang

2017-09-01

Full Text Available Yang and Qiu proposed and then recently improved an expected utility-entropy (EU-E measure of risk and decision model. When segregation holds, Luce et al. derived an expected utility term, plus a constant multiplies the Shannon entropy as the representation of risky choices, further demonstrating the reasonability of the EU-E decision model. In this paper, we apply the EU-E decision model to selecting the set of stocks to be included in the portfolios. We first select 7 and 10 stocks from the 30 component stocks of Dow Jones Industrial Average index, and then derive and compare the efficient portfolios in the mean-variance framework. The conclusions imply that efficient portfolios composed of 7(10 stocks selected using the EU-E model with intermediate intervals of the tradeoff coefficients are more efficient than that composed of the sets of stocks selected using the expected utility model. Furthermore, the efficient portfolio of 7(10 stocks selected by the EU-E decision model have almost the same efficient frontier as that of the sample of all stocks. This suggests the necessity of incorporating both the expected utility and Shannon entropy together when taking risky decisions, further demonstrating the importance of Shannon entropy as the measure of uncertainty, as well as the applicability of the EU-E model as a decision-making model.

Comparison of Optimal Portfolios Selected by Multicriterial Model Using Absolute and Relative Criteria Values

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Branka Marasović

2009-03-01

Full Text Available In this paper we select an optimal portfolio on the Croatian capital market by using the multicriterial programming. In accordance with the modern portfolio theory maximisation of returns at minimal risk should be the investment goal of any successful investor. However, contrary to the expectations of the modern portfolio theory, the tests carried out on a number of financial markets reveal the existence of other indicators important in portfolio selection. Considering the importance of variables other than return and risk, selection of the optimal portfolio becomes a multicriterial problem which should be solved by using the appropriate techniques.In order to select an optimal portfolio, absolute values of criteria, like return, risk, price to earning value ratio (P/E, price to book value ratio (P/B and price to sale value ratio (P/S are included in our multicriterial model. However the problem might occur as the mean values of some criteria are significantly different for different sectors and because financial managers emphasize that comparison of the same criteria for different sectors could lead us to wrong conclusions. In the second part of the paper, relative values of previously stated criteria (in relation to mean value of sector are included in model for selecting optimal portfolio. Furthermore, the paper shows that if relative values of criteria are included in multicriterial model for selecting optimal portfolio, return in subsequent period is considerably higher than if absolute values of the same criteria were used.

Continuous-Time Mean-Variance Portfolio Selection under the CEV Process

Directory of Open Access Journals (Sweden)

Hui-qiang Ma

2014-01-01

Full Text Available We consider a continuous-time mean-variance portfolio selection model when stock price follows the constant elasticity of variance (CEV process. The aim of this paper is to derive an optimal portfolio strategy and the efficient frontier. The mean-variance portfolio selection problem is formulated as a linearly constrained convex program problem. By employing the Lagrange multiplier method and stochastic optimal control theory, we obtain the optimal portfolio strategy and mean-variance efficient frontier analytically. The results show that the mean-variance efficient frontier is still a parabola in the mean-variance plane, and the optimal strategies depend not only on the total wealth but also on the stock price. Moreover, some numerical examples are given to analyze the sensitivity of the efficient frontier with respect to the elasticity parameter and to illustrate the results presented in this paper. The numerical results show that the price of risk decreases as the elasticity coefficient increases.

Strategic biopharmaceutical portfolio development: an analysis of constraint-induced implications.

Science.gov (United States)

George, Edmund D; Farid, Suzanne S

2008-01-01

Optimizing the structure and development pathway of biopharmaceutical drug portfolios are core concerns to the developer that come with several attached complexities. These include strategic decisions for the choice of drugs, the scheduling of critical activities, and the possible involvement of third parties for development and manufacturing at various stages for each drug. Additional complexities that must be considered include the impact of making such decisions in an uncertain environment. Presented here is the development of a stochastic multi-objective optimization framework designed to address these issues. The framework harnesses the ability of Bayesian networks to characterize the probabilistic structure of superior decisions via machine learning and evolve them to multi-objective optimality. Case studies that entailed three- and five-drug portfolios alongside a range of cash flow constraints were constructed to derive insight from the framework where results demonstrate that a variety of options exist for formulating nondominated strategies in the objective space considered, giving the manufacturer a range of pursuable options. In all cases limitations on cash flow reduce the potential for generating profits for a given probability of success. For the sizes of portfolio considered, results suggest that naïvely applying strategies optimal for a particular size of portfolio to a portfolio of another size is inappropriate. For the five-drug portfolio the most preferred means for development across the set of optimized strategies is to fully integrate development and commercial activities in-house. For the three-drug portfolio, the preferred means of development involves a mixture of in-house, outsourced, and partnered activities. Also, the size of the portfolio appears to have a larger impact on strategy and the quality of objectives than the magnitude of cash flow constraint.

An artificial bee colony algorithm for uncertain portfolio selection.

Science.gov (United States)

Chen, Wei

2014-01-01

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making

NARCIS (Netherlands)

Michael, Emmerich T. M.; Deutz, A.H.; Li, L.; Asep, Maulana A.; Yevseyeva, I.

2016-01-01

This paper deals with a scenario of decision making where a moderator selects a (sub)set (aka portfolio) of decision alternatives from a larger set. The larger the number of decision makers who agree on a solution in the portfolio the more successful the moderator is. We assume that decision makers

Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

International Nuclear Information System (INIS)

Yu, Zhiyong

2013-01-01

This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right

Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

Energy Technology Data Exchange (ETDEWEB)

Yu, Zhiyong, E-mail: yuzhiyong@sdu.edu.cn [Shandong University, School of Mathematics (China)

2013-12-15

This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right.

ADAPTIVE SELECTION OF AUXILIARY OBJECTIVES IN MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS

Directory of Open Access Journals (Sweden)

I. A. Petrova

2016-05-01

Full Text Available Subject of Research.We propose to modify the EA+RL method, which increases efficiency of evolutionary algorithms by means of auxiliary objectives. The proposed modification is compared to the existing objective selection methods on the example of travelling salesman problem. Method. In the EA+RL method a reinforcement learning algorithm is used to select an objective – the target objective or one of the auxiliary objectives – at each iteration of the single-objective evolutionary algorithm.The proposed modification of the EA+RL method adopts this approach for the usage with a multiobjective evolutionary algorithm. As opposed to theEA+RL method, in this modification one of the auxiliary objectives is selected by reinforcement learning and optimized together with the target objective at each step of the multiobjective evolutionary algorithm. Main Results.The proposed modification of the EA+RL method was compared to the existing objective selection methods on the example of travelling salesman problem. In the EA+RL method and its proposed modification reinforcement learning algorithms for stationary and non-stationary environment were used. The proposed modification of the EA+RL method applied with reinforcement learning for non-stationary environment outperformed the considered objective selection algorithms on the most problem instances. Practical Significance. The proposed approach increases efficiency of evolutionary algorithms, which may be used for solving discrete NP-hard optimization problems. They are, in particular, combinatorial path search problems and scheduling problems.

Portfolios with fuzzy returns: Selection strategies based on semi-infinite programming

Science.gov (United States)

Vercher, Enriqueta

2008-08-01

This paper provides new models for portfolio selection in which the returns on securities are considered fuzzy numbers rather than random variables. The investor's problem is to find the portfolio that minimizes the risk of achieving a return that is not less than the return of a riskless asset. The corresponding optimal portfolio is derived using semi-infinite programming in a soft framework. The return on each asset and their membership functions are described using historical data. The investment risk is approximated by mean intervals which evaluate the downside risk for a given fuzzy portfolio. This approach is illustrated with a numerical example.

The admissible portfolio selection problem with transaction costs and an improved PSO algorithm

Science.gov (United States)

Chen, Wei; Zhang, Wei-Guo

2010-05-01

In this paper, we discuss the portfolio selection problem with transaction costs under the assumption that there exist admissible errors on expected returns and risks of assets. We propose a new admissible efficient portfolio selection model and design an improved particle swarm optimization (PSO) algorithm because traditional optimization algorithms fail to work efficiently for our proposed problem. Finally, we offer a numerical example to illustrate the proposed effective approaches and compare the admissible portfolio efficient frontiers under different constraints.

Risk-aware multi-armed bandit problem with application to portfolio selection.

Science.gov (United States)

Huo, Xiaoguang; Fu, Feng

2017-11-01

Sequential portfolio selection has attracted increasing interest in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision-making under uncertainty, namely the exploration versus exploitation dilemma, and therefore provides a natural connection to portfolio selection. In this paper, we incorporate risk awareness into the classic multi-armed bandit setting and introduce an algorithm to construct portfolio. Through filtering assets based on the topological structure of the financial market and combining the optimal multi-armed bandit policy with the minimization of a coherent risk measure, we achieve a balance between risk and return.

Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

OpenAIRE

Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

2015-01-01

Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

Parametric Portfolio Selection: Evaluating and Comparing to Markowitz Portfolios

Directory of Open Access Journals (Sweden)

Marcelo C. Medeiros

2014-10-01

Full Text Available In this paper we exploit the parametric portfolio optimization in the Brazilian market. Our data consists of monthly returns of 306 Brazilian stocks in the period between 2001 and 2013. We tested the model both in and out of sample and compared the results with the value and equal weighted portfolios and with a Markowitz based portfolio. We performed statistical inference in the parametric optimization using bootstrap techniques in order to build the parameters empirical distributions. Our results showed that the parametric optimization is a very efficient technique out of sample. It consistently showed superior results when compared with the VW, EW and Markowitz portfolios even when transaction costs were included. Finally, we consider the parametric approach to be very flexible to the inclusion of constraints in weights, transaction costs and listing and delisting of stocks.

Project portfolio selection of banking services using COPRAS and Fuzzy-TOPSIS

Directory of Open Access Journals (Sweden)

C.O. Anyaeche

2017-04-01

Full Text Available Portfolio selection is a business process which has helped organisations identify an area of com-petitive advantage and it is a major concern to industrial players in the banking sectors. In order to enhance bank portfolio selection, cost, profitability, time and location are important parameters that decision-makers often consider. This study implements a fuzzy-TOPSIS (Technique for Or-der Preference by Similarity to Ideal Solution framework to evaluate three potential portfolios (automated teller machine gallery, quick service point and branch for a bank using the infor-mation from three decision-makers. An illustrative example of real bank information is used to demonstrate the proposed framework applicability. The complex proportional assessment of al-ternatives (COPRAS method is also used as an evaluation technique and the results are com-pared, which yields that the results from the ranking order of fuzzy-TOPSIS and COPRAS were different. However, there is a consistency between the aggregation of intuition-based, fuzzy-TOPSIS and COPRAS ranks and fuzzy-TOPSIS ranking results. The presented framework is an easy-to-apply tool that improves portfolio selection decision in the banking system.

Portfolio selection using ELECTRE III: Evidence from Tehran Stock Exchange

Directory of Open Access Journals (Sweden)

Aazam Shabani Vezmelai

2015-04-01

Full Text Available Ranking the companies can be a useful guide for investors to select an optimum portfolio. Tehran stock exchange (TSE uses liquidity criterion to rank the companies; however, this study shows that preferences of investors, the criteria they use to evaluate companies’ performances, and the extent to which ranking of companies based on investors’ criteria are in line with the ranking announced by the stock exchange. Since the criteria used for ranking the companies are various and often conflicting and because each multiple criteria technique has its own specific characteristics, various rankings are offered. Therefore, it is required to utilize multiple criteria decision making models to avoid confusion of investors. For this purpose, some companies were selected from 50 top companies listed in 2011 in TSE, which maintained the reliability of their ranks and finally, 20 companies were selected and were ranked based on investors’ criteria using EECTRE III Technique. The obtained ranking was then compared with the ranking offered by stock exchange. Research results indicate that ELECTRE III technique was a useful and efficient method to select a portfolio. Moreover, value-based criteria as well as accounting criteria are suitable and useful bases for investors to select a portfolio.

PROJECT PORTFOLIO SELECTION COMPETENCES RESEARCH INUNIVERSITIES OF LITHUANIA

OpenAIRE

R #363;ta #268;iutien #279;; Evelina Meilien #279;; Bronius Neverauskas

2011-01-01

As a result of the theoretical findings, the paperdemonstrates that projectportfolio selection is crucial project management problem. Successful ProjectPortfolio management requires specific competences.Every project of projectportfolio must be evaluated according to the basedcriteria and parameters.Empirical study was based on framework matrix withfour parameters of projectportfolio selection and only two phases of projectportfolio formation. The r...

Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection*

Science.gov (United States)

Fan, Jianqing; Li, Yingying; Yu, Ke

2012-01-01

Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of portfolios selection among a vast pool of assets, as demonstrated in Fan et al. (2011). The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enables us to better adapt to the local volatilities and local correlations among vast number of assets and to increase significantly the sample size for estimating the volatility matrix. This paper studies the volatility matrix estimation using high-dimensional high-frequency data from the perspective of portfolio selection. Specifically, we propose the use of “pairwise-refresh time” and “all-refresh time” methods based on the concept of “refresh time” proposed by Barndorff-Nielsen et al. (2008) for estimation of vast covariance matrix and compare their merits in the portfolio selection. We establish the concentration inequalities of the estimates, which guarantee desirable properties of the estimated volatility matrix in vast asset allocation with gross exposure constraints. Extensive numerical studies are made via carefully designed simulations. Comparing with the methods based on low frequency daily data, our methods can capture the most recent trend of the time varying volatility and correlation, hence provide more accurate guidance for the portfolio allocation in the next time period. The advantage of using high-frequency data is significant in our simulation and empirical studies, which consist of 50 simulated assets and 30 constituent stocks of Dow Jones Industrial Average index. PMID:23264708

Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection.

Science.gov (United States)

Fan, Jianqing; Li, Yingying; Yu, Ke

2012-01-01

Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of portfolios selection among a vast pool of assets, as demonstrated in Fan et al. (2011). The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enables us to better adapt to the local volatilities and local correlations among vast number of assets and to increase significantly the sample size for estimating the volatility matrix. This paper studies the volatility matrix estimation using high-dimensional high-frequency data from the perspective of portfolio selection. Specifically, we propose the use of "pairwise-refresh time" and "all-refresh time" methods based on the concept of "refresh time" proposed by Barndorff-Nielsen et al. (2008) for estimation of vast covariance matrix and compare their merits in the portfolio selection. We establish the concentration inequalities of the estimates, which guarantee desirable properties of the estimated volatility matrix in vast asset allocation with gross exposure constraints. Extensive numerical studies are made via carefully designed simulations. Comparing with the methods based on low frequency daily data, our methods can capture the most recent trend of the time varying volatility and correlation, hence provide more accurate guidance for the portfolio allocation in the next time period. The advantage of using high-frequency data is significant in our simulation and empirical studies, which consist of 50 simulated assets and 30 constituent stocks of Dow Jones Industrial Average index.

Effective Stock Selection and Portfolio Construction Within US, International, and Emerging Markets

Directory of Open Access Journals (Sweden)

Bijan Beheshti

2018-05-01

Full Text Available In this paper, we explore the ex-post attributes of 120 simulated portfolios across the U.S., International, and Emerging Markets. We estimate expected returns using a given global stock selection model employing Global Equity Rating (GLER and Consensus Temporary Earnings Forecasting (CTEF signals. Our portfolios are constructed under the Markowitz optimization framework and constrained at various tracking error levels. Further, an alpha alignment factor is applied to aid in portfolio construction. As a result of our research, we present the reader with three key findings. First, GLER and CTEF signals employed as the primary inputs to security selection result in portfolios with superior risk adjusted returns relative to the Russell 3000, MSCI AC World ex. US, and MSCI Emerging Markets benchmarks which they are measured against. Second, expanding the investment universe outside the U.S. increases the opportunity set yielding higher risk adjusted performance. Third, the incorporation of an alpha alignment factor within the portfolio construction process improves risk forecasts resulting in ex-post tracking error aligning more closely to ex-ante, and ultimately improving information ratios.

Portfolio selection using genetic algorithms | Yahaya | International ...

African Journals Online (AJOL)

In this paper, one of the nature-inspired evolutionary algorithms – a Genetic Algorithms (GA) was used in solving the portfolio selection problem (PSP). Based on a real dataset from a popular stock market, the performance of the algorithm in relation to those obtained from one of the popular quadratic programming (QP) ...

Fuzzy Investment Portfolio Selection Models Based on Interval Analysis Approach

Directory of Open Access Journals (Sweden)

Haifeng Guo

2012-01-01

Full Text Available This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean-variance (MV portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors' different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investment limit to each stock in the portfolio, which indicates our model is useful in practice.

Noise sensitivity of portfolio selection in constant conditional correlation GARCH models

Science.gov (United States)

Varga-Haszonits, I.; Kondor, I.

2007-11-01

This paper investigates the efficiency of minimum variance portfolio optimization for stock price movements following the Constant Conditional Correlation GARCH process proposed by Bollerslev. Simulations show that the quality of portfolio selection can be improved substantially by computing optimal portfolio weights from conditional covariances instead of unconditional ones. Measurement noise can be further reduced by applying some filtering method on the conditional correlation matrix (such as Random Matrix Theory based filtering). As an empirical support for the simulation results, the analysis is also carried out for a time series of S&P500 stock prices.

2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization

Directory of Open Access Journals (Sweden)

Seyedeh Elham Eftekharian

2017-11-01

Full Text Available Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA. In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm.

Fuzzy Portfolio Selection Problem with Different Borrowing and Lending Rates

OpenAIRE

Chen, Wei; Yang, Yiping; Ma, Hui

2011-01-01

As we know, borrowing and lending risk-free assets arise extensively in the theory and practice of finance. However, little study has ever investigated them in fuzzy portfolio problem. In this paper, the returns of each assets are assumed to be fuzzy variables, then following the mean-variance approach, a new possibilistic portfolio selection model with different interest rates for borrowing and lending is proposed, in which the possibilistic semiabsolute deviation of the return is used to...

Portfolio selection problem: a comparison of fuzzy goal programming and linear physical programming

Directory of Open Access Journals (Sweden)

Fusun Kucukbay

2016-04-01

Full Text Available Investors have limited budget and they try to maximize their return with minimum risk. Therefore this study aims to deal with the portfolio selection problem. In the study two criteria are considered which are expected return, and risk. In this respect, linear physical programming (LPP technique is applied on Bist 100 stocks to be able to find out the optimum portfolio. The analysis covers the period April 2009- March 2015. This period is divided into two; April 2009-March 2014 and April 2014 – March 2015. April 2009-March 2014 period is used as data to find an optimal solution. April 2014-March 2015 period is used to test the real performance of portfolios. The performance of the obtained portfolio is compared with that obtained from fuzzy goal programming (FGP. Then the performances of both method, LPP and FGP are compared with BIST 100 in terms of their Sharpe Indexes. The findings reveal that LPP for portfolio selection problem is a good alternative to FGP.

Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

Science.gov (United States)

Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

2010-10-01

To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.

A Three-Phase Multiobjective Mechanism for Selecting Retail Stores to Close

Directory of Open Access Journals (Sweden)

Rong-Chang Chen

2016-01-01

Full Text Available To operate a successful and growing business, a retail store manager has to make tough decisions about selectively closing underperforming stores. In this paper, we propose using a three-phase multiobjective mechanism to help retail industry practitioners determine which stores to close. In the first phase, a geographic information system (GIS and k-means clustering algorithm are used to divide all the stores into clusters. In the second phase, stores can be strategically selected according to the requirements of the company and the attributes of the stores. In the third phase, a neighborhood-based multiobjective genetic algorithm (NBMOGA is utilized to determine which stores to close. To examine the effectiveness of the proposed three-phase mechanism, a variety of experiments are performed, based partly on a real dataset from a stock-list company in Taiwan. Results from the experiments show that the proposed three-phase mechanism can help efficiently decide which store locations to close. In addition, the neighborhood radius has a considerable influence on the results.

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

Energy Technology Data Exchange (ETDEWEB)

Hashim, M.; Nazam, M.; Yao, L.; Baig, S.A.; Abrar, M.; Zia-ur-Rehman, M.

2017-07-01

The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

Directory of Open Access Journals (Sweden)

Muhammad Hashim

2017-05-01

Full Text Available Purpose:  The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM. Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM with fuzzy coefficients constructed in present research should be helpful for

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

International Nuclear Information System (INIS)

Hashim, M.; Nazam, M.; Yao, L.; Baig, S.A.; Abrar, M.; Zia-ur-Rehman, M.

2017-01-01

The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world

Portfolio selection problem with liquidity constraints under non-extensive statistical mechanics

International Nuclear Information System (INIS)

Zhao, Pan; Xiao, Qingxian

2016-01-01

In this study, we consider the optimal portfolio selection problem with liquidity limits. A portfolio selection model is proposed in which the risky asset price is driven by the process based on non-extensive statistical mechanics instead of the classic Wiener process. Using dynamic programming and Lagrange multiplier methods, we obtain the optimal policy and value function. Moreover, the numerical results indicate that this model is considerably different from the model based on the classic Wiener process, the optimal strategy is affected by the non-extensive parameter q, the increase in the investment in the risky asset is faster at a larger parameter q and the increase in wealth is similar.

[Location selection for Shenyang urban parks based on GIS and multi-objective location allocation model].

Science.gov (United States)

Zhou, Yuan; Shi, Tie-Mao; Hu, Yuan-Man; Gao, Chang; Liu, Miao; Song, Lin-Qi

2011-12-01

Based on geographic information system (GIS) technology and multi-objective location-allocation (LA) model, and in considering of four relatively independent objective factors (population density level, air pollution level, urban heat island effect level, and urban land use pattern), an optimized location selection for the urban parks within the Third Ring of Shenyang was conducted, and the selection results were compared with the spatial distribution of existing parks, aimed to evaluate the rationality of the spatial distribution of urban green spaces. In the location selection of urban green spaces in the study area, the factor air pollution was most important, and, compared with single objective factor, the weighted analysis results of multi-objective factors could provide optimized spatial location selection of new urban green spaces. The combination of GIS technology with LA model would be a new approach for the spatial optimizing of urban green spaces.

Mean-variance portfolio selection for defined-contribution pension funds with stochastic salary.

Science.gov (United States)

Zhang, Chubing

2014-01-01

This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity) solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier.

Mean-Variance Portfolio Selection for Defined-Contribution Pension Funds with Stochastic Salary

OpenAIRE

Chubing Zhang

2014-01-01

This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity) solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier.

Uncertain programming models for portfolio selection with uncertain returns

Science.gov (United States)

Zhang, Bo; Peng, Jin; Li, Shengguo

2015-10-01

In an indeterminacy economic environment, experts' knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.

Reliable Portfolio Selection Problem in Fuzzy Environment: An mλ Measure Based Approach

Directory of Open Access Journals (Sweden)

Yuan Feng

2017-04-01

Full Text Available This paper investigates a fuzzy portfolio selection problem with guaranteed reliability, in which the fuzzy variables are used to capture the uncertain returns of different securities. To effectively handle the fuzziness in a mathematical way, a new expected value operator and variance of fuzzy variables are defined based on the m λ measure that is a linear combination of the possibility measure and necessity measure to balance the pessimism and optimism in the decision-making process. To formulate the reliable portfolio selection problem, we particularly adopt the expected total return and standard variance of the total return to evaluate the reliability of the investment strategies, producing three risk-guaranteed reliable portfolio selection models. To solve the proposed models, an effective genetic algorithm is designed to generate the approximate optimal solution to the considered problem. Finally, the numerical examples are given to show the performance of the proposed models and algorithm.

Supplier Portfolio Selection and Optimum Volume Allocation: A Knowledge Based Method

NARCIS (Netherlands)

Aziz, Romana; Aziz, R.; van Hillegersberg, Jos; Kersten, W.; Blecker, T.; Luthje, C.

2010-01-01

Selection of suppliers and allocation of optimum volumes to suppliers is a strategic business decision. This paper presents a decision support method for supplier selection and the optimal allocation of volumes in a supplier portfolio. The requirements for the method were gathered during a case

Analysis of the portfolio of sites to characterize for selecting a nuclear repository

International Nuclear Information System (INIS)

Keeney, R.L.

1987-01-01

The US Department of Energy has selected three sites, from five nominated, to characterize for a nuclear repository to permanently dispose of nuclear waste. This decision was made without the benefit of an analysis of this portfolio problem. This paper analyzes different portfolios of three sites for simultaneous characterization and strategies for sequential characterization. Characterization of each site, which involves significant subsurface excavation, is now estimated to cost $1 billion. Mainly because of the high characterization costs, sequential characterization strategies are identified which are the equivalent of $1.7-2.0 billion less expensive than the selected DOE simultaneous characterization of the three sites. If three sites are simultaneously characterized, one portfolio is estimated to be the equivalent of $100-400 million better than the selected DOE portfolio. Because of these potential savings and several other complicating factors that may influence the relative desirability of characterization strategies, a thorough analysis of characterization strategies that addresses the likelihood of finding disqualifying conditions during site characterization, uncertainties, and dependencies in forecast site repository costs, preclosure and postclosure health and safety impacts, potential delays of both sequential and simultaneous characterization strategies, and the environmental, socioeconomic, and health and safety impacts of characterization activities is recommended

Application of Markowitz Portfolio Theory by Building Optimal Portfolio on the US Stock Market

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Martin Širůček

2015-01-01

Full Text Available This paper is focused on building investment portfolios by using the Markowitz Portfolio Theory (MPT. Derivation based on the Capital Asset Pricing Model (CAPM is used to calculate the weights of individual securities in portfolios. The calculated portfolios include a portfolio copying the benchmark made using the CAPM model, portfolio with low and high beta coefficients, and a random portfolio. Only stocks were selected for the examined sample from all the asset classes. Stocks in each portfolio are put together according to predefined criteria. All stocks were selected from Dow Jones Industrial Average (DJIA index which serves as a benchmark, too. Portfolios were compared based on their risk and return profiles. The results of this work will provide general recommendations on the optimal approach to choose securities for an investor’s portfolio.

Mean-Variance Portfolio Selection for Defined-Contribution Pension Funds with Stochastic Salary

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Chubing Zhang

2014-01-01

Full Text Available This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier.

Mean-Variance Portfolio Selection for Defined-Contribution Pension Funds with Stochastic Salary

Science.gov (United States)

Zhang, Chubing

2014-01-01

This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity) solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier. PMID:24782667

The regional electricity generation mix in Scotland: A portfolio selection approach incorporating marine technologies

International Nuclear Information System (INIS)

Allan, Grant; Eromenko, Igor; McGregor, Peter; Swales, Kim

2011-01-01

Standalone levelised cost assessments of electricity supply options miss an important contribution that renewable and non-fossil fuel technologies can make to the electricity portfolio: that of reducing the variability of electricity costs, and their potentially damaging impact upon economic activity. Portfolio theory applications to the electricity generation mix have shown that renewable technologies, their costs being largely uncorrelated with non-renewable technologies, can offer such benefits. We look at the existing Scottish generation mix and examine drivers of changes out to 2020. We assess recent scenarios for the Scottish generation mix in 2020 against mean-variance efficient portfolios of electricity-generating technologies. Each of the scenarios studied implies a portfolio cost of electricity that is between 22% and 38% higher than the portfolio cost of electricity in 2007. These scenarios prove to be mean-variance 'inefficient' in the sense that, for example, lower variance portfolios can be obtained without increasing portfolio costs, typically by expanding the share of renewables. As part of extensive sensitivity analysis, we find that Wave and Tidal technologies can contribute to lower risk electricity portfolios, while not increasing portfolio cost. - Research Highlights: → Portfolio analysis of scenarios for Scotland's electricity generating mix in 2020. → Reveals potential inefficiencies of selecting mixes based on levelised cost alone. → Portfolio risk-reducing contribution of Wave and Tidal technologies assessed.

The regional electricity generation mix in Scotland: A portfolio selection approach incorporating marine technologies

Energy Technology Data Exchange (ETDEWEB)

Allan, Grant, E-mail: grant.j.allan@strath.ac.u [Fraser of Allander Institute, Department of Economics, University of Strathclyde, Sir William Duncan Building, 130 Rottenrow, Glasgow G4 0GE (United Kingdom); Eromenko, Igor; McGregor, Peter [Fraser of Allander Institute, Department of Economics, University of Strathclyde, Sir William Duncan Building, 130 Rottenrow, Glasgow G4 0GE (United Kingdom); Swales, Kim [Department of Economics, University of Strathclyde, Sir William Duncan Building, 130 Rottenrow, Glasgow G4 0GE (United Kingdom)

2011-01-15

Standalone levelised cost assessments of electricity supply options miss an important contribution that renewable and non-fossil fuel technologies can make to the electricity portfolio: that of reducing the variability of electricity costs, and their potentially damaging impact upon economic activity. Portfolio theory applications to the electricity generation mix have shown that renewable technologies, their costs being largely uncorrelated with non-renewable technologies, can offer such benefits. We look at the existing Scottish generation mix and examine drivers of changes out to 2020. We assess recent scenarios for the Scottish generation mix in 2020 against mean-variance efficient portfolios of electricity-generating technologies. Each of the scenarios studied implies a portfolio cost of electricity that is between 22% and 38% higher than the portfolio cost of electricity in 2007. These scenarios prove to be mean-variance 'inefficient' in the sense that, for example, lower variance portfolios can be obtained without increasing portfolio costs, typically by expanding the share of renewables. As part of extensive sensitivity analysis, we find that Wave and Tidal technologies can contribute to lower risk electricity portfolios, while not increasing portfolio cost. - Research Highlights: {yields} Portfolio analysis of scenarios for Scotland's electricity generating mix in 2020. {yields} Reveals potential inefficiencies of selecting mixes based on levelised cost alone. {yields} Portfolio risk-reducing contribution of Wave and Tidal technologies assessed.

Continuous-Time Mean-Variance Portfolio Selection: A Stochastic LQ Framework

International Nuclear Information System (INIS)

Zhou, X.Y.; Li, D.

2000-01-01

This paper is concerned with a continuous-time mean-variance portfolio selection model that is formulated as a bicriteria optimization problem. The objective is to maximize the expected terminal return and minimize the variance of the terminal wealth. By putting weights on the two criteria one obtains a single objective stochastic control problem which is however not in the standard form due to the variance term involved. It is shown that this nonstandard problem can be 'embedded' into a class of auxiliary stochastic linear-quadratic (LQ) problems. The stochastic LQ control model proves to be an appropriate and effective framework to study the mean-variance problem in light of the recent development on general stochastic LQ problems with indefinite control weighting matrices. This gives rise to the efficient frontier in a closed form for the original portfolio selection problem

Dynamic Portfolio Strategy Using Clustering Approach.

Science.gov (United States)

Ren, Fei; Lu, Ya-Nan; Li, Sai-Ping; Jiang, Xiong-Fei; Zhong, Li-Xin; Qiu, Tian

2017-01-01

The problem of portfolio optimization is one of the most important issues in asset management. We here propose a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for investment. A portfolio strategy comprises two stages: First, select the portfolios by choosing central and peripheral stocks in the selection horizon using five topological parameters, namely degree, betweenness centrality, distance on degree criterion, distance on correlation criterion and distance on distance criterion. Second, use the portfolios for investment in the investment horizon. The optimal portfolio is chosen by comparing central and peripheral portfolios under different combinations of market conditions in the selection and investment horizons. Market conditions in our paper are identified by the ratios of the number of trading days with rising index to the total number of trading days, or the sum of the amplitudes of the trading days with rising index to the sum of the amplitudes of the total trading days. We find that central portfolios outperform peripheral portfolios when the market is under a drawup condition, or when the market is stable or drawup in the selection horizon and is under a stable condition in the investment horizon. We also find that peripheral portfolios gain more than central portfolios when the market is stable in the selection horizon and is drawdown in the investment horizon. Empirical tests are carried out based on the optimal portfolio strategy. Among all possible optimal portfolio strategies based on different parameters to select portfolios and different criteria to identify market conditions, 65% of our optimal portfolio strategies outperform the random strategy for the Shanghai A-Share market while the proportion is 70% for the Shenzhen A-Share market.

Dynamic Portfolio Strategy Using Clustering Approach.

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Fei Ren

Full Text Available The problem of portfolio optimization is one of the most important issues in asset management. We here propose a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for investment. A portfolio strategy comprises two stages: First, select the portfolios by choosing central and peripheral stocks in the selection horizon using five topological parameters, namely degree, betweenness centrality, distance on degree criterion, distance on correlation criterion and distance on distance criterion. Second, use the portfolios for investment in the investment horizon. The optimal portfolio is chosen by comparing central and peripheral portfolios under different combinations of market conditions in the selection and investment horizons. Market conditions in our paper are identified by the ratios of the number of trading days with rising index to the total number of trading days, or the sum of the amplitudes of the trading days with rising index to the sum of the amplitudes of the total trading days. We find that central portfolios outperform peripheral portfolios when the market is under a drawup condition, or when the market is stable or drawup in the selection horizon and is under a stable condition in the investment horizon. We also find that peripheral portfolios gain more than central portfolios when the market is stable in the selection horizon and is drawdown in the investment horizon. Empirical tests are carried out based on the optimal portfolio strategy. Among all possible optimal portfolio strategies based on different parameters to select portfolios and different criteria to identify market conditions, 65% of our optimal portfolio strategies outperform the random strategy for the Shanghai A-Share market while the proportion is 70% for the Shenzhen A-Share market.

A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products

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Animesh Debnath

2017-07-01

Full Text Available Due to the increasing size of the population, society faces several challenges for sustainable and adequate agricultural production, quality, distribution, and food safety in the strategic project portfolio selection (SPPS. The initial adaptation of strategic portfolio management of genetically modified (GM Agro by-products (Ab-Ps is a huge challenge in terms of processing the agro food product supply-chain practices in an environmentally nonthreatening way. As a solution to the challenges, the socio-economic characteristics for SPPS of GM food purchasing scenarios are studied. Evaluation and selection of the GM agro portfolio management are the dynamic issues due to physical and immaterial criteria involving a hybrid multiple criteria decision making (MCDM approach, combining modified grey Decision-Making Trial and Evaluation Laboratory (DEMATEL, Multi-Attributive Border Approximation area Comparison (MABAC and sensitivity analysis. Evaluation criteria are grouped into social, differential and beneficial clusters, and the modified DEMATEL procedure is used to derive the criteria weights. The MABAC method is applied to rank the strategic project portfolios according to the aggregated preferences of decision makers (DMs. The usefulness of the proposed research framework is validated with a case study. The GM by-products are found to be the best portfolio. Moreover, this framework can unify the policies of agro technological improvement, corporate social responsibility (CSR and agro export promotion.

Big Data Challenges of High-Dimensional Continuous-Time Mean-Variance Portfolio Selection and a Remedy.

Science.gov (United States)

Chiu, Mei Choi; Pun, Chi Seng; Wong, Hoi Ying

2017-08-01

Investors interested in the global financial market must analyze financial securities internationally. Making an optimal global investment decision involves processing a huge amount of data for a high-dimensional portfolio. This article investigates the big data challenges of two mean-variance optimal portfolios: continuous-time precommitment and constant-rebalancing strategies. We show that both optimized portfolios implemented with the traditional sample estimates converge to the worst performing portfolio when the portfolio size becomes large. The crux of the problem is the estimation error accumulated from the huge dimension of stock data. We then propose a linear programming optimal (LPO) portfolio framework, which applies a constrained ℓ 1 minimization to the theoretical optimal control to mitigate the risk associated with the dimensionality issue. The resulting portfolio becomes a sparse portfolio that selects stocks with a data-driven procedure and hence offers a stable mean-variance portfolio in practice. When the number of observations becomes large, the LPO portfolio converges to the oracle optimal portfolio, which is free of estimation error, even though the number of stocks grows faster than the number of observations. Our numerical and empirical studies demonstrate the superiority of the proposed approach. © 2017 Society for Risk Analysis.

Construction of uncertainty sets for portfolio selection problems

OpenAIRE

Wiechers, Christof

2011-01-01

While modern portfolio theory grounds on the trade-off between portfolio return and portfolio variance to determine the optimal investment decision, postmodern portfolio theory uses downside risk measures instead of the variance. Prominent examples are given by the risk measures Value-at-Risk and its coherent extension, Conditional Value-at-Risk. When avoiding distributional assumptions on the process that generates the risky assets' returns, historical return data or expert knowledge remain ...

An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems

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Fuqing Zhao

2016-01-01

Full Text Available A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.

A Generalized Measure for the Optimal Portfolio Selection Problem and its Explicit Solution

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Zinoviy Landsman

2018-03-01

Full Text Available In this paper, we offer a novel class of utility functions applied to optimal portfolio selection. This class incorporates as special cases important measures such as the mean-variance, Sharpe ratio, mean-standard deviation and others. We provide an explicit solution to the problem of optimal portfolio selection based on this class. Furthermore, we show that each measure in this class generally reduces to the efficient frontier that coincides or belongs to the classical mean-variance efficient frontier. In addition, a condition is provided for the existence of the a one-to-one correspondence between the parameter of this class of utility functions and the trade-off parameter λ in the mean-variance utility function. This correspondence essentially provides insight into the choice of this parameter. We illustrate our results by taking a portfolio of stocks from National Association of Securities Dealers Automated Quotation (NASDAQ.

New Method of Selecting Efficient Project Portfolios in the Presence of Hybrid Uncertainty

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Bogdan Rębiasz

2016-01-01

Full Text Available A new methods of selecting efficient project portfolios in the presence of hybrid uncertainty has been presented. Pareto optimal solutions have been defined by an algorithm for generating project portfolios. The method presented allows us to select efficient project portfolios taking into account statistical and economic dependencies between projects when some of the parameters used in the calculation of effectiveness can be expressed in the form of an interactive possibility distribution and some in the form of a probability distribution. The procedure for processing such hybrid data combines stochastic simulation with nonlinear programming. The interaction between data are modeled by correlation matrices and the interval regression. Economic dependences are taken into account by the equations balancing the production capacity of the company. The practical example presented indicates that an interaction between projects has a significant impact on the results of calculations. (original abstract

Optimal portfolio selection for general provisioning and terminal wealth problems

NARCIS (Netherlands)

van Weert, K.; Dhaene, J.; Goovaerts, M.

2010-01-01

In Dhaene et al. (2005), multiperiod portfolio selection problems are discussed, using an analytical approach to find optimal constant mix investment strategies in a provisioning or a savings context. In this paper we extend some of these results, investigating some specific, real-life situations.

Optimal portfolio selection for general provisioning and terminal wealth problems

NARCIS (Netherlands)

van Weert, K.; Dhaene, J.; Goovaerts, M.

2009-01-01

In Dhaene et al. (2005), multiperiod portfolio selection problems are discussed, using an analytical approach to find optimal constant mix investment strategies in a provisioning or savings context. In this paper we extend some of these results, investigating some specific, real-life situations. The

Continuous-time mean-variance portfolio selection with value-at-risk and no-shorting constraints

Science.gov (United States)

Yan, Wei

2012-01-01

An investment problem is considered with dynamic mean-variance(M-V) portfolio criterion under discontinuous prices which follow jump-diffusion processes according to the actual prices of stocks and the normality and stability of the financial market. The short-selling of stocks is prohibited in this mathematical model. Then, the corresponding stochastic Hamilton-Jacobi-Bellman(HJB) equation of the problem is presented and the solution of the stochastic HJB equation based on the theory of stochastic LQ control and viscosity solution is obtained. The efficient frontier and optimal strategies of the original dynamic M-V portfolio selection problem are also provided. And then, the effects on efficient frontier under the value-at-risk constraint are illustrated. Finally, an example illustrating the discontinuous prices based on M-V portfolio selection is presented.

A New Decision-Making Method for Stock Portfolio Selection Based on Computing with Linguistic Assessment

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Chen-Tung Chen

2009-01-01

Full Text Available The purpose of stock portfolio selection is how to allocate the capital to a large number of stocks in order to bring a most profitable return for investors. In most of past literatures, experts considered the portfolio of selection problem only based on past crisp or quantitative data. However, many qualitative and quantitative factors will influence the stock portfolio selection in real investment situation. It is very important for experts or decision-makers to use their experience or knowledge to predict the performance of each stock and make a stock portfolio. Because of the knowledge, experience, and background of each expert are different and vague, different types of 2-tuple linguistic variable are suitable used to express experts' opinions for the performance evaluation of each stock with respect to criteria. According to the linguistic evaluations of experts, the linguistic TOPSIS and linguistic ELECTRE methods are combined to present a new decision-making method for dealing with stock selection problems in this paper. Once the investment set has been determined, the risk preferences of investor are considered to calculate the investment ratio of each stock in the investment set. Finally, an example is implemented to demonstrate the practicability of the proposed method.

On the Computation of the Efficient Frontier of the Portfolio Selection Problem

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Clara Calvo

2012-01-01

Full Text Available An easy-to-use procedure is presented for improving the ε-constraint method for computing the efficient frontier of the portfolio selection problem endowed with additional cardinality and semicontinuous variable constraints. The proposed method provides not only a numerical plotting of the frontier but also an analytical description of it, including the explicit equations of the arcs of parabola it comprises and the change points between them. This information is useful for performing a sensitivity analysis as well as for providing additional criteria to the investor in order to select an efficient portfolio. Computational results are provided to test the efficiency of the algorithm and to illustrate its applications. The procedure has been implemented in Mathematica.

Stock portfolio selection using Dempster–Shafer evidence theory

OpenAIRE

Mitra Thakur, Gour Sundar; Bhattacharyya, Rupak; Sarkar (Mondal), Seema

2016-01-01

Markowitz’s return–risk model for stock portfolio selection is based on the historical return data of assets. In addition to the effect of historical return, there are many other critical factors which directly or indirectly influence the stock market. We use the fuzzy Delphi method to identify the critical factors initially. Factors having lower correlation coefficients are finally considered for further consideration. The critical factors and historical data are used to apply Dempster–Shafe...

Behavioral optimization models for multicriteria portfolio selection

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Mehlawat Mukesh Kumar

2013-01-01

Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

Portfolio Optimization Using Particle Swarms with Stripes

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Mario Villalobos Arias

2011-04-01

Full Text Available In this paper it is consider the Portfolio Optimization Problem developed by Markowitz [11]. The basic assumption is that the investor tries to maximize his/her profit and at the same time, wants to minimize the risk. This problem is usually solved using a scalarization approach (with one objective. Here it is solved it as a bi-objective  optimization problem. It uses a new version of the algorithm of Particle Swarm Optimization for Multi-Objective Problems to which it implemented a method of the stripes to improve dispersion.

Mean-Variance Portfolio Selection with Margin Requirements

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Yuan Zhou

2013-01-01

Full Text Available We study the continuous-time mean-variance portfolio selection problem in the situation when investors must pay margin for short selling. The problem is essentially a nonlinear stochastic optimal control problem because the coefficients of positive and negative parts of control variables are different. We can not apply the results of stochastic linearquadratic (LQ problem. Also the solution of corresponding Hamilton-Jacobi-Bellman (HJB equation is not smooth. Li et al. (2002 studied the case when short selling is prohibited; therefore they only need to consider the positive part of control variables, whereas we need to handle both the positive part and the negative part of control variables. The main difficulty is that the positive part and the negative part are not independent. The previous results are not directly applicable. By decomposing the problem into several subproblems we figure out the solutions of HJB equation in two disjoint regions and then prove it is the viscosity solution of HJB equation. Finally we formulate solution of optimal portfolio and the efficient frontier. We also present two examples showing how different margin rates affect the optimal solutions and the efficient frontier.

A behavioural approach to financial portfolio selection problem: an empirical study using heuristics

OpenAIRE

Grishina, Nina

2014-01-01

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University The behaviourally based portfolio selection problem with investor's loss aversion and risk aversion biases in portfolio choice under uncertainty are studied. The main results of this work are developed heuristic approaches for the prospect theory and cumulative prospect theory models proposed by Kahneman and Tversky in 1979 and 1992 as well as an empirical comparative analysis of these models ...

Optimal Portfolios in Wishart Models and Effects of Discrete Rebalancing on Portfolio Distribution and Strategy Selection

OpenAIRE

Li, Zejing

2012-01-01

This dissertation is mainly devoted to the research of two problems - the continuous-time portfolio optimization in different Wishart models and the effects of discrete rebalancing on portfolio wealth distribution and optimal portfolio strategy.

Robust portfolio selection based on asymmetric measures of variability of stock returns

Science.gov (United States)

Chen, Wei; Tan, Shaohua

2009-10-01

This paper addresses a new uncertainty set--interval random uncertainty set for robust optimization. The form of interval random uncertainty set makes it suitable for capturing the downside and upside deviations of real-world data. These deviation measures capture distributional asymmetry and lead to better optimization results. We also apply our interval random chance-constrained programming to robust mean-variance portfolio selection under interval random uncertainty sets in the elements of mean vector and covariance matrix. Numerical experiments with real market data indicate that our approach results in better portfolio performance.

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Science.gov (United States)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

Science.gov (United States)

Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

2016-01-01

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

Pareto Optimal Solutions for Network Defense Strategy Selection Simulator in Multi-Objective Reinforcement Learning

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Yang Sun

2018-01-01

Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.

PORTFOLIO SELECTION OF INFORMATION SYSTEMS PROJECTS USING PROMETHEE V WITH C-OPTIMAL CONCEPT

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Jonatas A. de Almeida

2014-05-01

Full Text Available This paper presents a multicriteria decision model for selecting a portfolio of information system (IS projects, which integrates strategic and organizational view within a multicriteria decision structure. The PROMETHEE V method, based on outranking relations is applied, considering the c-optimal concept in order to overcome some scaling problems found in the classical PROMETHEE V approach. Then, a procedure is proposed in order to make a final analysis of the c-optimal portfolios found as a result of using PROMETHEE V. Also, the organizational view is discussed, including some factors that may influence decision making on IS projects to be included in the portfolio, such as adding the company's strategic vision and technical aspects that demonstrate how IS contributes value to a company's business.

Automatic Trading Agent. RMT Based Portfolio Theory and Portfolio Selection

Science.gov (United States)

Snarska, M.; Krzych, J.

2006-11-01

Portfolio theory is a very powerful tool in the modern investment theory. It is helpful in estimating risk of an investor's portfolio, arosen from lack of information, uncertainty and incomplete knowledge of reality, which forbids a perfect prediction of future price changes. Despite of many advantages this tool is not known and not widely used among investors on Warsaw Stock Exchange. The main reason for abandoning this method is a high level of complexity and immense calculations. The aim of this paper is to introduce an automatic decision-making system, which allows a single investor to use complex methods of Modern Portfolio Theory (MPT). The key tool in MPT is an analysis of an empirical covariance matrix. This matrix, obtained from historical data, biased by such a high amount of statistical uncertainty, that it can be seen as random. By bringing into practice the ideas of Random Matrix Theory (RMT), the noise is removed or significantly reduced, so the future risk and return are better estimated and controlled. These concepts are applied to the Warsaw Stock Exchange Simulator {http://gra.onet.pl}. The result of the simulation is 18% level of gains in comparison with respective 10% loss of the Warsaw Stock Exchange main index WIG.

On market timing and portfolio selectivity: modifying the Henriksson-Merton model

OpenAIRE

Goś, Krzysztof

2011-01-01

This paper evaluates selected functionalities of the parametrical Henriksson-Merton test, a tool designed for measuring the market timing and portfolio selectivity capabilities. It also provides a solution to two significant disadvantages of the model: relatively indirect interpretation and vulnerability to parameter insignificance. The model has been put to test on a group of Polish mutual funds in a period of 63 months (January 2004 – March 2009), providing unsatisfa...

Time-Consistent Strategies for a Multiperiod Mean-Variance Portfolio Selection Problem

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Huiling Wu

2013-01-01

Full Text Available It remained prevalent in the past years to obtain the precommitment strategies for Markowitz's mean-variance portfolio optimization problems, but not much is known about their time-consistent strategies. This paper takes a step to investigate the time-consistent Nash equilibrium strategies for a multiperiod mean-variance portfolio selection problem. Under the assumption that the risk aversion is, respectively, a constant and a function of current wealth level, we obtain the explicit expressions for the time-consistent Nash equilibrium strategy and the equilibrium value function. Many interesting properties of the time-consistent results are identified through numerical sensitivity analysis and by comparing them with the classical pre-commitment solutions.

Selection of risk reduction portfolios under interval-valued probabilities

International Nuclear Information System (INIS)

Toppila, Antti; Salo, Ahti

2017-01-01

A central problem in risk management is that of identifying the optimal combination (or portfolio) of improvements that enhance the reliability of the system most through reducing failure event probabilities, subject to the availability of resources. This optimal portfolio can be sensitive with regard to epistemic uncertainties about the failure events' probabilities. In this paper, we develop an optimization model to support the allocation of resources to improvements that mitigate risks in coherent systems in which interval-valued probabilities defined by lower and upper bounds are employed to capture epistemic uncertainties. Decision recommendations are based on portfolio dominance: a resource allocation portfolio is dominated if there exists another portfolio that improves system reliability (i) at least as much for all feasible failure probabilities and (ii) strictly more for some feasible probabilities. Based on non-dominated portfolios, recommendations about improvements to implement are derived by inspecting in how many non-dominated portfolios a given improvement is contained. We present an exact method for computing the non-dominated portfolios. We also present an approximate method that simplifies the reliability function using total order interactions so that larger problem instances can be solved with reasonable computational effort. - Highlights: • Reliability allocation under epistemic uncertainty about probabilities. • Comparison of alternatives using dominance. • Computational methods for generating the non-dominated alternatives. • Deriving decision recommendations that are robust with respect to epistemic uncertainty.

Switching portfolios.

Science.gov (United States)

Singer, Y

1997-08-01

A constant rebalanced portfolio is an asset allocation algorithm which keeps the same distribution of wealth among a set of assets along a period of time. Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight (Cover, 1991; Helmbold et al., 1996; Cover and Ordentlich, 1996). By their nature, these algorithms employ the assumption that high returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the wealth achieved by a constant rebalanced portfolio is much smaller than the wealth achieved by an ad hoc investment strategy that adapts to changes in the market. In this paper we present an efficient portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including the transactions cost models recently investigated in (Blum and Kalai, 1997). We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22-year period. On this data, our algorithm outperforms all the algorithms referenced above, with and without transaction costs.

On the non-stationarity of financial time series: impact on optimal portfolio selection

International Nuclear Information System (INIS)

Livan, Giacomo; Inoue, Jun-ichi; Scalas, Enrico

2012-01-01

We investigate the possible drawbacks of employing the standard Pearson estimator to measure correlation coefficients between financial stocks in the presence of non-stationary behavior, and we provide empirical evidence against the well-established common knowledge that using longer price time series provides better, more accurate, correlation estimates. Then, we investigate the possible consequences of instabilities in empirical correlation coefficient measurements on optimal portfolio selection. We rely on previously published works which provide a framework allowing us to take into account possible risk underestimations due to the non-optimality of the portfolio weights being used in order to distinguish such non-optimality effects from risk underestimations genuinely due to non-stationarities. We interpret such results in terms of instabilities in some spectral properties of portfolio correlation matrices. (paper)

Selecting Large Portfolios of Social Projects in Public Organizations

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Igor Litvinchev

2014-01-01

Full Text Available We address the portfolio selection of social projects in public organizations considering interdependencies (synergies affecting project funds requirements and tasks. A mixed integer linear programming model is proposed incorporating the most relevant aspects of the problem found in the literature. The model supports both complete (all or nothing and partial (a certain amount from a given interval of funding resource allocation policies. Numerical results for large-scale problem instances are presented.

Portfolio Selection Based on Distance between Fuzzy Variables

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Weiyi Qian

2014-01-01

Full Text Available This paper researches portfolio selection problem in fuzzy environment. We introduce a new simple method in which the distance between fuzzy variables is used to measure the divergence of fuzzy investment return from a prior one. Firstly, two new mathematical models are proposed by expressing divergence as distance, investment return as expected value, and risk as variance and semivariance, respectively. Secondly, the crisp forms of the new models are also provided for different types of fuzzy variables. Finally, several numerical examples are given to illustrate the effectiveness of the proposed approach.

Portfolio selection between rational and behavioral theories emergent markets case

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Bouri Abdelfatteh

2012-08-01

Full Text Available The aim of this paper is to explore the determinants of Portfolio Choice under the investors, professionals and academics’ perception. We introduce an approach based on cognitive mapping technique with a series of semi-directive interviews. Among a sample of 30 Tunisian individuals, we propose tow different frameworks: a mean-variance framework and a behavioral framework. Each framework is oriented to capture the effect of some concepts as proposed by the mean-variance portfolio theory and the behavioral portfolio theory on the portfolio choice decision. The originality of this research paper is guaranteed since it traits the behavioral portfolio choice in emergent markets. In the best of our knowledge this is the first study in the Tunisian context that explores such area of research. Ours results show that the Tunisian investors behave as it prescribed by the behavioral portfolio theory. They use some concepts proposed by the rational mean-variance theory of portfolio choice but they are affected by their emotions and some others cognitive bias when constructing and managing they portfolio of assets.

Portfolio optimization in enhanced index tracking with goal programming approach

Science.gov (United States)

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

2014-09-01

Enhanced index tracking is a popular form of passive fund management in stock market. Enhanced index tracking aims to generate excess return over the return achieved by the market index without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio to maximize the mean return and minimize the risk. The objective of this paper is to determine the portfolio composition and performance using goal programming approach in enhanced index tracking and comparing it to the market index. Goal programming is a branch of multi-objective optimization which can handle decision problems that involve two different goals in enhanced index tracking, a trade-off between maximizing the mean return and minimizing the risk. The results of this study show that the optimal portfolio with goal programming approach is able to outperform the Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.

Afrika Statistika ISSN 2316-090X The price of portfolio selection ...

African Journals Online (AJOL)

The price of portfolio selection under tail conditional expectation with ... TCE provides a more conservative measure of risk than VaR for the same level ...... Substituting the new value function and its derivatives with respect to h into the .... the optimal condition is that the discount rate is proxy of the systematic volatility factor.

Mean-Variance Portfolio Selection with a Fixed Flow of Investment in ...

African Journals Online (AJOL)

We consider a mean-variance portfolio selection problem for a fixed flow of investment in a continuous time framework. We consider a market structure that is characterized by a cash account, an indexed bond and a stock. We obtain the expected optimal terminal wealth for the investor. We also obtain a closed-form ...

Multi-objective optimization of cellular scanning strategy in selective laser melting

DEFF Research Database (Denmark)

Ahrari, Ali; Deb, Kalyanmoy; Mohanty, Sankhya

2017-01-01

The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study is to dev......The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study......, the problem is a combination of combinatorial and choice optimization, which makes the problem difficult to solve. On a process simulation domain consisting of 32 cells, our multi-objective evolutionary method is able to find a set of trade-off solutions for the defined conflicting objectives, which cannot...

Two-Stage Fuzzy Portfolio Selection Problem with Transaction Costs

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Yanju Chen

2015-01-01

Full Text Available This paper studies a two-period portfolio selection problem. The problem is formulated as a two-stage fuzzy portfolio selection model with transaction costs, in which the future returns of risky security are characterized by possibility distributions. The objective of the proposed model is to achieve the maximum utility in terms of the expected value and variance of the final wealth. Given the first-stage decision vector and a realization of fuzzy return, the optimal value expression of the second-stage programming problem is derived. As a result, the proposed two-stage model is equivalent to a single-stage model, and the analytical optimal solution of the two-stage model is obtained, which helps us to discuss the properties of the optimal solution. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the effectiveness. The computational results provided by the proposed model show that the more risk-averse investor will invest more wealth in the risk-free security. They also show that the optimal invested amount in risky security increases as the risk-free return decreases and the optimal utility increases as the risk-free return increases, whereas the optimal utility increases as the transaction costs decrease. In most instances the utilities provided by the proposed two-stage model are larger than those provided by the single-stage model.

The Effect of Exit Strategy on Optimal Portfolio Selection with Birandom Returns

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Guohua Cao

2013-01-01

Full Text Available The aims of this paper are to use a birandom variable to denote the stock return selected by some recurring technical patterns and to study the effect of exit strategy on optimal portfolio selection with birandom returns. Firstly, we propose a new method to estimate the stock return and use birandom distribution to denote the final stock return which can reflect the features of technical patterns and investors' heterogeneity simultaneously; secondly, we build a birandom safety-first model and design a hybrid intelligent algorithm to help investors make decisions; finally, we innovatively study the effect of exit strategy on the given birandom safety-first model. The results indicate that (1 the exit strategy affects the proportion of portfolio, (2 the performance of taking the exit strategy is better than when the exit strategy is not taken, if the stop-loss point and the stop-profit point are appropriately set, and (3 the investor using the exit strategy become conservative.

Applying Portfolio Selection: A Case of Indonesia Stock Exchange

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Maria Praptiningsih

2012-01-01

Full Text Available This study has three objectives. First, we investigate whether Modern Portfolio Theory can be applied on the financial decisions that made by investors or individual in order to increase their wealth through investment activities. Second, we examine the real behavior of each asset in terms of capital assets pricing models. Third, we determine whether our portfolio is the best model to produce a higher return in a given level of risk or a lowest risk in a particular level of return. It is found that three different stocks listed in the Indonesia Stock Exchange have a positive relationship with market returns. The reactions of the investor regarding these stocks are not influenced by each other. Lastly, the minimum variance portfolio (MVP point which represents the single portfolio with the lowest possible level of standard deviation, occurs when the expected return of portfolio is approximately 2.2 percent at a standard deviation of 8.8 percent.

Representation Bias, Return Forecast, and Portfolio Selection in the Stock Market of China

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Daping Zhao

2014-01-01

Full Text Available Representation bias means a kind of cognitive tendency, and, for investors, it can affect their behavior in the stock market. Whether the representation bias can help the return forecast and portfolio selection is an interesting problem that is less studied. In this paper, based on the representation bias theory and current markets situation in China, a new hierarchy of stock measurement system is constructed and a corresponding set of criteria is also proposed. On each criterion, we try to measure the influence among stocks with adapted fuzzy AHP. Then the Hausdorff distance is applied to weight and compute the horizontal representation returns. For the forecast returns, according to representation behaviors, there is also a new computation method. Empirical results show that the representation bias information is useful to the return forecast as well as the portfolio selection.

Portfolio Selection with Heavy Tails

NARCIS (Netherlands)

N. Hyung (Namwon); C.G. de Vries (Casper)

2004-01-01

textabstractConsider the portfolio problem of choosing the mix between stocks and bonds under a downside risk constraint. Typically stock returns exhibit fatter tails than bonds corresponding to their greater downside risk. Downside risk criteria like the safety first criterion therefore of ten

Mean-Variance stochastic goal programming for sustainable mutual funds' portfolio selection.

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García-Bernabeu, Ana

2015-11-01

Full Text Available Mean-Variance Stochastic Goal Programming models (MV-SGP provide satisficing investment solutions in uncertain contexts. In this work, an MV-SGP model is proposed for portfolio selection which includes goals with regards to traditional and sustainable assets. The proposed approach is based on a two-step procedure. In the first step, sustainability and/or financial screens are applied to a set of assets (mutual funds previously evaluated with TOPSIS to determine the opportunity set. In a second step, satisficing portfolios of assets are obtained using a Goal Programming approach. Two different goals are considered. The first goal reflects only the purely financial side of the target while the second goal is referred to the sustainable side. Aversion to Risk Absolute (ARA coefficients are estimated and incorporated in our investment decision making approach using two different approaches.

The Effect of Exit Strategy on Optimal Portfolio Selection with Birandom Returns

OpenAIRE

Cao, Guohua; Shan, Dan

2013-01-01

The aims of this paper are to use a birandom variable to denote the stock return selected by some recurring technical patterns and to study the effect of exit strategy on optimal portfolio selection with birandom returns. Firstly, we propose a new method to estimate the stock return and use birandom distribution to denote the final stock return which can reflect the features of technical patterns and investors' heterogeneity simultaneously; secondly, we build a birandom safety-first model and...

Comonotonic approximations for a generalized provisioning problem with application to optimal portfolio selection

NARCIS (Netherlands)

van Weert, K.; Dhaene, J.; Goovaerts, M.

2011-01-01

In this paper we discuss multiperiod portfolio selection problems related to a specific provisioning problem. Our results are an extension of Dhaene et al. (2005) [14], where optimal constant mix investment strategies are obtained in a provisioning and savings context, using an analytical approach

Robust Markowitz mean-variance portfolio selection under ambiguous covariance matrix *

OpenAIRE

Ismail, Amine; Pham, Huyên

2016-01-01

This paper studies a robust continuous-time Markowitz portfolio selection pro\\-blem where the model uncertainty carries on the covariance matrix of multiple risky assets. This problem is formulated into a min-max mean-variance problem over a set of non-dominated probability measures that is solved by a McKean-Vlasov dynamic programming approach, which allows us to characterize the solution in terms of a Bellman-Isaacs equation in the Wasserstein space of probability measures. We provide expli...

Selection of security system design via games of imperfect information and multi-objective genetic algorithm

International Nuclear Information System (INIS)

Lins, Isis Didier; Rêgo, Leandro Chaves; Moura, Márcio das Chagas

2013-01-01

This work analyzes the strategic interaction between a defender and an intelligent attacker by means of a game and reliability framework involving a multi-objective approach and imperfect information so as to support decision-makers in choosing efficiently designed security systems. A multi-objective genetic algorithm is used to determine the optimal security system's configurations representing the tradeoff between the probability of a successful defense and the acquisition and operational costs. Games with imperfect information are considered, in which the attacker has limited knowledge about the actual security system. The types of security alternatives are readily observable, but the number of redundancies actually implemented in each security subsystem is not known. The proposed methodology is applied to an illustrative example considering power transmission lines in the Northeast of Brazil, which are often targets for attackers who aims at selling the aluminum conductors. The empirical results show that the framework succeeds in handling this sort of strategic interaction. -- Highlights: ► Security components must have feasible costs and must be reliable. ► The optimal design of security systems considers a multi-objective approach. ► Games of imperfect information enable the choice of non-dominated configurations. ► MOGA, reliability and games support the entire defender's decision process. ► The selection of effective security systems may discourage attacker's actions

Properties of Risk Measures of Generalized Entropy in Portfolio Selection

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Rongxi Zhou

2017-12-01

Full Text Available This paper systematically investigates the properties of six kinds of entropy-based risk measures: Information Entropy and Cumulative Residual Entropy in the probability space, Fuzzy Entropy, Credibility Entropy and Sine Entropy in the fuzzy space, and Hybrid Entropy in the hybridized uncertainty of both fuzziness and randomness. We discover that none of the risk measures satisfy all six of the following properties, which various scholars have associated with effective risk measures: Monotonicity, Translation Invariance, Sub-additivity, Positive Homogeneity, Consistency and Convexity. Measures based on Fuzzy Entropy, Credibility Entropy, and Sine Entropy all exhibit the same properties: Sub-additivity, Positive Homogeneity, Consistency, and Convexity. These measures based on Information Entropy and Hybrid Entropy, meanwhile, only exhibit Sub-additivity and Consistency. Cumulative Residual Entropy satisfies just Sub-additivity, Positive Homogeneity, and Convexity. After identifying these properties, we develop seven portfolio models based on different risk measures and made empirical comparisons using samples from both the Shenzhen Stock Exchange of China and the New York Stock Exchange of America. The comparisons show that the Mean Fuzzy Entropy Model performs the best among the seven models with respect to both daily returns and relative cumulative returns. Overall, these results could provide an important reference for both constructing effective risk measures and rationally selecting the appropriate risk measure under different portfolio selection conditions.

Sparse and stable Markowitz portfolios.

Science.gov (United States)

Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace

2009-07-28

We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.

Asset Allocation and Optimal Contract for Delegated Portfolio Management

Science.gov (United States)

Liu, Jingjun; Liang, Jianfeng

This article studies the portfolio selection and the contracting problems between an individual investor and a professional portfolio manager in a discrete-time principal-agent framework. Portfolio selection and optimal contracts are obtained in closed form. The optimal contract was composed with the fixed fee, the cost, and the fraction of excess expected return. The optimal portfolio is similar to the classical two-fund separation theorem.

Analysis of the rebalancing frequency in log-optimal portfolio selection

OpenAIRE

Kuhn, Daniel; Luenberger, David G.

2010-01-01

In a dynamic investment situation, the right timing of portfolio revisions and adjustments is essential to sustain long-term growth. A high rebalancing frequency reduces the portfolio performance in the presence of transaction costs, whereas a low rebalancing frequency entails a static investment strategy that hardly reacts to changing market conditions. This article studies a family of portfolio problems in a Black-Scholes type economy which depend parametrically on the rebalancing frequency...

Multi-objective Transmission Planning Paper

DEFF Research Database (Denmark)

Xu, Zhao; Dong, Zhao Yang; Wong, Kit Po

2009-01-01

This paper describes a transmission expansion planning method based on multi-objective optimization (MOOP). The method starts with constructing a candidate pool of feasible expansion plans, followed by selection of the best candidates through MOOP, of which multiple objectives are tackled...

Multiobjective programming and planning

CERN Document Server

Cohon, Jared L

2004-01-01

This text takes a broad view of multiobjective programming, emphasizing the methods most useful for continuous problems. It reviews multiobjective programming methods in the context of public decision-making problems, developing each problem within a context that addresses practical aspects of planning issues. Topics include a review of linear programming, the formulation of the general multiobjective programming problem, classification of multiobjective programming methods, techniques for generating noninferior solutions, multiple-decision-making methods, multiobjective analysis of water reso

Fuzzy Portfolio Selection Problem with Different Borrowing and Lending Rates

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Wei Chen

2011-01-01

the returns of each assets are assumed to be fuzzy variables, then following the mean-variance approach, a new possibilistic portfolio selection model with different interest rates for borrowing and lending is proposed, in which the possibilistic semiabsolute deviation of the return is used to measure investment risk. The conventional probabilistic mean variance model can be transformed to a linear programming problem under possibility distributions. Finally, a numerical example is given to illustrate the modeling idea and the impact of borrowing and lending on optimal decision making.

Maslow Portfolio Selection for Individuals with Low Financial Sustainability

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Zongxin Li

2018-04-01

Full Text Available In this paper, we extend Maslow’s need hierarchy theory and the two-level optimization approach by developing the framework of the Maslow portfolio selection model (MPSM by solving the two optimization problems to meet the need of individuals with low financial sustainability who prefer to satisfy their lower-level (safety need first, and, thereafter, look for higher-level (self-actualization need to maximize the optimal return. We illustrate our proposed model with real American stock data from the S&P index and conduct the out-of-sample analysis to compare the performance of our proposed Variance-CVaR (conditional value-at-risk MPSM with both traditional mean-variance and mean-CVaR models. Our empirical analysis shows that our proposed Variance-CVaR MPSM is not only sustainable, but also obtains the best out-of-sample performance in the sense that the optimal portfolios obtained by using our proposed Variance-CVaR MPSM obtain the highest cumulative returns in the out-of-sample period among the models used in our paper. We note that our proposed model is not only suitable to individuals with low financial sustainability, but also suitable to institutions or investors with high financial sustainability.

Portfolio selection theory and wildlife management

African Journals Online (AJOL)

Theron and Van den Honert (2003) dealt with issues of risk and return in an agricultural context. ... By repeatedly solving (1) with different specified values of R an efficient frontier of portfolio .... the built–in solver of Microsoft® Excel [2].

PORTFOLIO OPTIMIZATION ON CROATIAN CAPITAL MARKET

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Sinisa Bogdan

2013-12-01

Full Text Available Purpose of this paper was to research portfolio optimization problem on Croatian capital market using Markowitz theory. Research systematically investigated the selection of securities, and defined the importance of using fundamental analysis when selecting the best combination of securities. Since fundamental analysis involves a large number of indicators, this paper selected key indicators that enable a complete and quick securities review on the market. This paper clarifies diversification effect and influence of the correlation coefficient on diversification. Two basic types of assets (stocks and cash funds have been chosen to build the optimal portfolio. Cash funds were selected because they represent a form of risk-free investment, while stocks were chosen because of the high level of return which they achieve. At the end of paper, optimal portfolio was calculated with an excellent yield of 1.82% and deviation of 5.77% on a monthly basis which corresponds to the minimum deviation of the selected stocks. Calculated optimal portfolio achieves better expected value than investing in stock index CROBEX, which for the same period achieves the expected result of -0.02%.

Universal portfolios generated by the Bregman divergence

Science.gov (United States)

Tan, Choon Peng; Kuang, Kee Seng

2017-04-01

The Bregman divergence of two probability vectors is a stronger form of the f-divergence introduced by Csiszar. Two versions of the Bregman universal portfolio are presented by exploiting the mean-value theorem. The explicit form of the Bregman universal portfolio generated by a function of a convex polynomial is derived and studied empirically. This portfolio can be regarded as another generalized of the well-known Helmbold portfolio. By running the portfolios on selected stock-price data sets from the local stock exchange, it is shown that it is possible to increase the wealth of the investor by using the portfolios in investment.

Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

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Hanning Chen

2014-01-01

Full Text Available The development of radio frequency identification (RFID technology generates the most challenging RFID network planning (RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA and swarm intelligence (SI for solving multiobjective RNP (MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC, the nondominated sorting genetic algorithm II (NSGA-II, and the multiobjective particle swarm optimization (MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

The Finite and Moving Order Multinomial Universal Portfolio

International Nuclear Information System (INIS)

Tan, Choon Peng; Pang, Sook Theng

2013-01-01

An upper bound for the ratio of wealths of the best constant -rebalanced portfolio to that of the multinomial universal portfolio is derived. The finite- order multinomial universal portfolios can reduce the implementation time and computer-memory requirements for computation. The improved performance of the finite-order portfolios on some selected local stock-price data sets is observed.

Optimal Portfolio Selection Under Concave Price Impact

International Nuclear Information System (INIS)

Ma Jin; Song Qingshuo; Xu Jing; Zhang Jianfeng

2013-01-01

In this paper we study an optimal portfolio selection problem under instantaneous price impact. Based on some empirical analysis in the literature, we model such impact as a concave function of the trading size when the trading size is small. The price impact can be thought of as either a liquidity cost or a transaction cost, but the concavity nature of the cost leads to some fundamental difference from those in the existing literature. We show that the problem can be reduced to an impulse control problem, but without fixed cost, and that the value function is a viscosity solution to a special type of Quasi-Variational Inequality (QVI). We also prove directly (without using the solution to the QVI) that the optimal strategy exists and more importantly, despite the absence of a fixed cost, it is still in a “piecewise constant” form, reflecting a more practical perspective.

Optimal Portfolio Selection Under Concave Price Impact

Energy Technology Data Exchange (ETDEWEB)

Ma Jin, E-mail: jinma@usc.edu [University of Southern California, Department of Mathematics (United States); Song Qingshuo, E-mail: songe.qingshuo@cityu.edu.hk [City University of Hong Kong, Department of Mathematics (Hong Kong); Xu Jing, E-mail: xujing8023@yahoo.com.cn [Chongqing University, School of Economics and Business Administration (China); Zhang Jianfeng, E-mail: jianfenz@usc.edu [University of Southern California, Department of Mathematics (United States)

2013-06-15

In this paper we study an optimal portfolio selection problem under instantaneous price impact. Based on some empirical analysis in the literature, we model such impact as a concave function of the trading size when the trading size is small. The price impact can be thought of as either a liquidity cost or a transaction cost, but the concavity nature of the cost leads to some fundamental difference from those in the existing literature. We show that the problem can be reduced to an impulse control problem, but without fixed cost, and that the value function is a viscosity solution to a special type of Quasi-Variational Inequality (QVI). We also prove directly (without using the solution to the QVI) that the optimal strategy exists and more importantly, despite the absence of a fixed cost, it is still in a 'piecewise constant' form, reflecting a more practical perspective.

Multi-period project portfolio selection under risk considerations and stochastic income

Science.gov (United States)

Tofighian, Ali Asghar; Moezzi, Hamid; Khakzar Barfuei, Morteza; Shafiee, Mahmood

2018-02-01

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.

Reflection during Portfolio-Based Conversations

Science.gov (United States)

Oosterbaan, Anne E.; van der Schaaf, Marieke F.; Baartman, Liesbeth K. J.; Stokking, Karel M.

2010-01-01

This study aims to explore the relationship between the occurrence of reflection (and non-reflection) and thinking activities (e.g., orientating, selecting, analysing) during portfolio-based conversations. Analysis of 21 transcripts of portfolio-based conversations revealed that 20% of the segments were made up of reflection (content reflection…

Multi-objective optimization of inverse planning for accurate radiotherapy

International Nuclear Information System (INIS)

Cao Ruifen; Pei Xi; Cheng Mengyun; Li Gui; Hu Liqin; Wu Yican; Jing Jia; Li Guoli

2011-01-01

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set. (authors)

Portfolio optimization for seed selection in diverse weather scenarios.

Science.gov (United States)

Marko, Oskar; Brdar, Sanja; Panić, Marko; Šašić, Isidora; Despotović, Danica; Knežević, Milivoje; Crnojević, Vladimir

2017-01-01

The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.

Portfolio optimization for seed selection in diverse weather scenarios.

Directory of Open Access Journals (Sweden)

Oskar Marko

Full Text Available The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.

Digital portfolio for learning: A new communication channel for education

Directory of Open Access Journals (Sweden)

Judit Coromina

2011-04-01

Full Text Available Purpose: The Catalonian Government has the intention of introducing the digital portfolio before 2017, an initiative related to new approaches for learning. Taking in consideration the increasing interest for digital portfolio as a new communication channel for education, the article aims are: on the one hand to describe how the digital portfolio works and on the other hand, to identify a list of criteria that should be useful for educative centers to select the best application to create the digital portfolio according to their needs.Design/methodology/approach: Firstly, a theoretical framework for portfolio functioning is described. After, applications to support the digital portfolio are classified. Next, a requirement analysis on an ideal application to support the portfolio is made, according to those phases for the portfolio creation identified in the theoretical framework. Lastly, a list of criteria is established to select the application for creating the digital portfolio.Findings and Originality/value: The article contributes to structure the portfolio creation process in some stages and phases in a wider way that it is described in the literature. In addition, a list of criteria is defined to help educative centers to select the application for managing the portfolio that fits better with their objectives. These criteria have been obtained with an exhaustive methodology.Research limitations/implications: In order to put in practice the identified criteria it is proposed to complete the multi-criteria decision model in a new study. It should include processes to weigh criteria and define normalizations. Afterwards it would be able to analyze the value of the model studying the satisfaction for using it by a sample of educative centers.Practical implications: The list of criteria identified should facilitate the selection of the more adequate application to create the learning portfolio to the educative centers, according to their

Discrete Analysis of Portfolio Selection with Optimal Stopping Time

Directory of Open Access Journals (Sweden)

Jianfeng Liang

2009-01-01

Full Text Available Most of the investments in practice are carried out without certain horizons. There are many factors to drive investment to a stop. In this paper, we consider a portfolio selection policy with market-related stopping time. Particularly, we assume that the investor exits the market once his wealth reaches a given investment target or falls below a bankruptcy threshold. Our objective is to minimize the expected time when the investment target is obtained, at the same time, we guarantee the probability that bankruptcy happens is no larger than a given level. We formulate the problem as a mix integer linear programming model and make analysis of the model by using a numerical example.

Analisis Pembentukan Portofolio Optimal Saham Dengan Pendekatan Optimisasi Multiobjektif Untuk Pengukuran Value at Risk

OpenAIRE

Farkhati, Fiki; Hoyyi, Abdul; Wilandari, Yuciana

2014-01-01

Mean Variance Efficient Portfolio (MVEP) is theory of portfolio which purposed to standard investor because approach has only one objective that minimize portfolio risk. Portfolio with multi-objective optimization that simultaneously maximize portfolio return and minimize portfolio risk with various weighting coefficient k represents risk aversion index. The purpose of this research is analyze proportion each stock in order that is formed optimal portfolio approach multi-objective optimizati...

A multi-attribute decision model for portfolio selection aiming to replace technologies in industrial motor systems

International Nuclear Information System (INIS)

Vanderley Herrero Sola, Antonio; Mota, Caroline Maria de Miranda

2012-01-01

Highlights: ► We propose a multicriteria decision model for technology replacement. ► We prioritize induction motors in order to improve the energy efficiency. ► The best portfolio of options is selected based on decision maker’s utilities. ► The model contribute to surpass some organizational barriers. - Abstract: The energy efficient technologies offered by the market are in constant evolution, but their insertion in the productive sector comes up against organizational barriers, which obstruct decision making in firms. This paper proposes a multicriteria decision model in order to replace technologies in industrial energy systems, regarding organizational barriers for energy efficiency. The proposed model is applied in industrial motor systems, using Multi-Attribute Utility Theory (MAUT), in order to select the best portfolio of options based on the decision maker’s utilities. Portfolios of options from the prioritized set of motors compiled by the operational area of the studied industry are analyzed, including diverse suppliers and different classes of motors. The results show that it is essential to structure the proposed model in two steps, beginning with the operational level, to ensure that important technologies for the production system are prioritized, thus preserving the interests of the organization and improving the efficiency of industrial energy systems.

Solving portfolio selection problems with minimum transaction lots based on conditional-value-at-risk

Science.gov (United States)

Setiawan, E. P.; Rosadi, D.

2017-01-01

Portfolio selection problems conventionally means ‘minimizing the risk, given the certain level of returns’ from some financial assets. This problem is frequently solved with quadratic or linear programming methods, depending on the risk measure that used in the objective function. However, the solutions obtained by these method are in real numbers, which may give some problem in real application because each asset usually has its minimum transaction lots. In the classical approach considering minimum transaction lots were developed based on linear Mean Absolute Deviation (MAD), variance (like Markowitz’s model), and semi-variance as risk measure. In this paper we investigated the portfolio selection methods with minimum transaction lots with conditional value at risk (CVaR) as risk measure. The mean-CVaR methodology only involves the part of the tail of the distribution that contributed to high losses. This approach looks better when we work with non-symmetric return probability distribution. Solution of this method can be found with Genetic Algorithm (GA) methods. We provide real examples using stocks from Indonesia stocks market.

Equity portfolio optimization: A DEA based methodology applied to the Zagreb Stock Exchange

Directory of Open Access Journals (Sweden)

Margareta Gardijan

2015-10-01

Full Text Available Most strategies for selection portfolios focus on utilizing solely market data and implicitly assume that stock markets communicate all relevant information to all market stakeholders, and that these markets cannot be influenced by investor activities. However convenient, this is a limited approach, especially when applied to small and illiquid markets such as the Croatian market, where such assumptions are hardly realistic. Thus, there is a demand for including other sources of data, such as financial reports. Research poses the question of whether financial ratios as criteria for stock selection are of any use to Croatian investors. Financial and market data from selected publicly companies listed on the Croatian capital market are used. A two-stage portfolio selection strategy is applied, where the first stage involves selecting stocks based on the respective Data Envelopment Analysis (DEA efficiency scores. DEA models are becoming popular in stock portfolio selection given that the methodology includes numerous models that provide a great flexibility in selecting inputs and outputs, which in turn are considered as criteria for portfolio selection. Accordingly, there is much room for improvement of the current proposed strategies for selecting portfolios. In the second stage, two portfolio-weighting strategies are applied using equal proportions and score-weighting. To show whether these strategies create outstanding out–of–sample portfolios in time, time-dependent DEA Window Analysis is applied using a reference time of one year, and portfolio returns are compared with the market portfolio for each period. It is found that the financial data are a significant indicator of the future performance of a stock and a DEA-based portfolio strategy outperforms market return.

Parametric Portfolio Policies with Common Volatility Dynamics

DEFF Research Database (Denmark)

Ergemen, Yunus Emre; Taamouti, Abderrahim

A parametric portfolio policy function is considered that incorporates common stock volatility dynamics to optimally determine portfolio weights. Reducing dimension of the traditional portfolio selection problem significantly, only a number of policy parameters corresponding to first- and second......-order characteristics are estimated based on a standard method-of-moments technique. The method, allowing for the calculation of portfolio weight and return statistics, is illustrated with an empirical application to 30 U.S. industries to study the economic activity before and after the recent financial crisis....

Continuous Time Portfolio Selection under Conditional Capital at Risk

Directory of Open Access Journals (Sweden)

Gordana Dmitrasinovic-Vidovic

2010-01-01

Full Text Available Portfolio optimization with respect to different risk measures is of interest to both practitioners and academics. For there to be a well-defined optimal portfolio, it is important that the risk measure be coherent and quasiconvex with respect to the proportion invested in risky assets. In this paper we investigate one such measure—conditional capital at risk—and find the optimal strategies under this measure, in the Black-Scholes continuous time setting, with time dependent coefficients.

A dynamic decision model for portfolio investment and assets management

Institute of Scientific and Technical Information of China (English)

QIAN Edward Y.; FENG Ying; HIGGISION James

2005-01-01

This paper addresses a dynamic portfolio investment problem. It discusses how we can dynamically choose candidate assets, achieve the possible maximum revenue and reduce the risk to the minimum level. The paper generalizes Markowitz's portfolio selection theory and Sharpe's rule for investment decision. An analytical solution is presented to show how an institutional or individual investor can combine Markowitz's portfolio selection theory, generalized Sharpe's rule and Value-at-Risk(VaR) to find candidate assets and optimal level of position sizes for investment (dis-investment). The result shows that the generalized Markowitz's portfolio selection theory and generalized Sharpe's rule improve decision making for investment.

Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

Science.gov (United States)

Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

2015-06-18

Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

Differentiability properties of the efficient (u,q2)-set in the Markowitz portfolio selection method

NARCIS (Netherlands)

Kriens, J.; Strijbosch, L.W.G.; Vörös, J.

1994-01-01

The set of efficient (Rho2)-combinations in the (Rho2)-plane of the Markowitz portfolio selection method consists of a series of strictly convex parabola. In the transition points from one parabola to the next one, the curve may be indifferentiable. The article gives necessary and sufficient

Universal portfolios in stochastic portfolio theory

OpenAIRE

Wong, Ting-Kam Leonard

2015-01-01

Consider a family of portfolio strategies with the aim of achieving the asymptotic growth rate of the best one. The idea behind Cover's universal portfolio is to build a wealth-weighted average which can be viewed as a buy-and-hold portfolio of portfolios. When an optimal portfolio exists, the wealth-weighted average converges to it by concentration of wealth. Working under a discrete time and pathwise setup, we show under suitable conditions that the distribution of wealth in the family sati...

A robust multi-objective global supplier selection model under currency fluctuation and price discount

Science.gov (United States)

Zarindast, Atousa; Seyed Hosseini, Seyed Mohamad; Pishvaee, Mir Saman

2017-06-01

Robust supplier selection problem, in a scenario-based approach has been proposed, when the demand and exchange rates are subject to uncertainties. First, a deterministic multi-objective mixed integer linear programming is developed; then, the robust counterpart of the proposed mixed integer linear programming is presented using the recent extension in robust optimization theory. We discuss decision variables, respectively, by a two-stage stochastic planning model, a robust stochastic optimization planning model which integrates worst case scenario in modeling approach and finally by equivalent deterministic planning model. The experimental study is carried out to compare the performances of the three models. Robust model resulted in remarkable cost saving and it illustrated that to cope with such uncertainties, we should consider them in advance in our planning. In our case study different supplier were selected due to this uncertainties and since supplier selection is a strategic decision, it is crucial to consider these uncertainties in planning approach.

Mean-variance portfolio selection and efficient frontier for defined contribution pension schemes

OpenAIRE

Hoejgaard, B.; Vigna, E.

2007-01-01

We solve a mean-variance portfolio selection problem in the accumulation phase of a defined contribution pension scheme. The efficient frontier, which is found for the 2 asset case as well as the n + 1 asset case, gives the member the possibility to decide his own risk/reward profile. The mean-variance approach is then compared to other investment strategies adopted in DC pension schemes, namely the target-based approach and the lifestyle strategy. The comparison is done both in a theoretical...

The regional electricity generation mix in Scotland. A portfolio selection approach incorporating marine technologies

Energy Technology Data Exchange (ETDEWEB)

Allan, Grant; Eromenko, Igor; McGregor, Peter [Fraser of Allander Institute, Department of Economics, University of Strathclyde, Sir William Duncan Building, 130 Rottenrow, Glasgow G4 0GE (United Kingdom); Swales, Kim [Department of Economics, University of Strathclyde, Sir William Duncan Building, 130 Rottenrow, Glasgow G4 0GE (United Kingdom)

2011-01-15

Standalone levelised cost assessments of electricity supply options miss an important contribution that renewable and non-fossil fuel technologies can make to the electricity portfolio: that of reducing the variability of electricity costs, and their potentially damaging impact upon economic activity. Portfolio theory applications to the electricity generation mix have shown that renewable technologies, their costs being largely uncorrelated with non-renewable technologies, can offer such benefits. We look at the existing Scottish generation mix and examine drivers of changes out to 2020. We assess recent scenarios for the Scottish generation mix in 2020 against mean-variance efficient portfolios of electricity-generating technologies. Each of the scenarios studied implies a portfolio cost of electricity that is between 22% and 38% higher than the portfolio cost of electricity in 2007. These scenarios prove to be mean-variance 'inefficient' in the sense that, for example, lower variance portfolios can be obtained without increasing portfolio costs, typically by expanding the share of renewables. As part of extensive sensitivity analysis, we find that Wave and Tidal technologies can contribute to lower risk electricity portfolios, while not increasing portfolio cost. (author)

An efficient heuristic method for dynamic portfolio selection problem under transaction costs and uncertain conditions

Science.gov (United States)

Najafi, Amir Abbas; Pourahmadi, Zahra

2016-04-01

Selecting the optimal combination of assets in a portfolio is one of the most important decisions in investment management. As investment is a long term concept, looking into a portfolio optimization problem just in a single period may cause loss of some opportunities that could be exploited in a long term view. Hence, it is tried to extend the problem from single to multi-period model. We include trading costs and uncertain conditions to this model which made it more realistic and complex. Hence, we propose an efficient heuristic method to tackle this problem. The efficiency of the method is examined and compared with the results of the rolling single-period optimization and the buy and hold method which shows the superiority of the proposed method.

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

Performance of finite order distribution-generated universal portfolios

Science.gov (United States)

Pang, Sook Theng; Liew, How Hui; Chang, Yun Fah

2017-04-01

A Constant Rebalanced Portfolio (CRP) is an investment strategy which reinvests by redistributing wealth equally among a set of stocks. The empirical performance of the distribution-generated universal portfolio strategies are analysed experimentally concerning 10 higher volume stocks from different categories in Kuala Lumpur Stock Exchange. The time interval of study is from January 2000 to December 2015, which includes the credit crisis from September 2008 to March 2009. The performance of the finite-order universal portfolio strategies has been shown to be better than Constant Rebalanced Portfolio with some selected parameters of proposed universal portfolios.

On application of vector optimization in the problem of formation of portfolio of counterparties

Science.gov (United States)

Gorbich, A. L.; Medvedeva, M. A.; Medvedev, M. A.

2016-12-01

For the effective functioning of any enterprise it is necessary to choose the right partners: suppliers of raw material, buyers of finished products, with which the company interacts in the course of their business. However, the presence on the market of big amounts of enterprises makes the choice the most appropriate among them very difficult and requires the ability to objectively assess of the possible partners, based on multilateral analysis of their activities. This analysis can be carried out based on the solution of multiobjective problems of mathematical programming by using the methods of vector optimization. The work considers existing methods of selection of counterparties, as well as the theoretical foundations for the proposed methodology. It also describes a computer program that analyzes the raw data for contractors and allows choosing the best portfolio of suppliers of enterprise. The feature of selection of counterparties is that today's market has a large number of enterprises in similar activities. Successful choice of contractor will help to avoid unpleasant situations and financial losses, as well as to find a reliable partner in his person for the implementation of the production strategy of the company.

Different Variants of Fundamental Portfolio

Directory of Open Access Journals (Sweden)

Tarczyński Waldemar

2014-06-01

Full Text Available The paper proposes the fundamental portfolio of securities. This portfolio is an alternative for the classic Markowitz model, which combines fundamental analysis with portfolio analysis. The method’s main idea is based on the use of the TMAI1 synthetic measure and, in limiting conditions, the use of risk and the portfolio’s rate of return in the objective function. Different variants of fundamental portfolio have been considered under an empirical study. The effectiveness of the proposed solutions has been related to the classic portfolio constructed with the help of the Markowitz model and the WIG20 market index’s rate of return. All portfolios were constructed with data on rates of return for 2005. Their effectiveness in 2006- 2013 was then evaluated. The studied period comprises the end of the bull market, the 2007-2009 crisis, the 2010 bull market and the 2011 crisis. This allows for the evaluation of the solutions’ flexibility in various extreme situations. For the construction of the fundamental portfolio’s objective function and the TMAI, the study made use of financial and economic data on selected indicators retrieved from Notoria Serwis for 2005.

Reliability optimization using multiobjective ant colony system approaches

International Nuclear Information System (INIS)

Zhao Jianhua; Liu Zhaoheng; Dao, M.-T.

2007-01-01

The multiobjective ant colony system (ACS) meta-heuristic has been developed to provide solutions for the reliability optimization problem of series-parallel systems. This type of problems involves selection of components with multiple choices and redundancy levels that produce maximum benefits, and is subject to the cost and weight constraints at the system level. These are very common and realistic problems encountered in conceptual design of many engineering systems. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective ACS algorithm offers distinct advantages to these problems compared with alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multiobjective formulation of local moves and the dynamic penalty method, the multiobjective ACSRAP, allows us to obtain an optimal design solution very frequently and more quickly than with some other heuristic approaches. The proposed algorithm was successfully applied to an engineering design problem of gearbox with multiple stages

Portfolio optimization using median-variance approach

Science.gov (United States)

Wan Mohd, Wan Rosanisah; Mohamad, Daud; Mohamed, Zulkifli

2013-04-01

Optimization models have been applied in many decision-making problems particularly in portfolio selection. Since the introduction of Markowitz's theory of portfolio selection, various approaches based on mathematical programming have been introduced such as mean-variance, mean-absolute deviation, mean-variance-skewness and conditional value-at-risk (CVaR) mainly to maximize return and minimize risk. However most of the approaches assume that the distribution of data is normal and this is not generally true. As an alternative, in this paper, we employ the median-variance approach to improve the portfolio optimization. This approach has successfully catered both types of normal and non-normal distribution of data. With this actual representation, we analyze and compare the rate of return and risk between the mean-variance and the median-variance based portfolio which consist of 30 stocks from Bursa Malaysia. The results in this study show that the median-variance approach is capable to produce a lower risk for each return earning as compared to the mean-variance approach.

Comparing the Selection and Placement of Best Management Practices in Improving Water Quality Using a Multiobjective Optimization and Targeting Method

Directory of Open Access Journals (Sweden)

Li-Chi Chiang

2014-03-01

Full Text Available Suites of Best Management Practices (BMPs are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA and a watershed model (Soil and Water Assessment Tool—SWAT. For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS, and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.

Online Learning of Commission Avoidant Portfolio Ensembles

OpenAIRE

Uziel, Guy; El-Yaniv, Ran

2016-01-01

We present a novel online ensemble learning strategy for portfolio selection. The new strategy controls and exploits any set of commission-oblivious portfolio selection algorithms. The strategy handles transaction costs using a novel commission avoidance mechanism. We prove a logarithmic regret bound for our strategy with respect to optimal mixtures of the base algorithms. Numerical examples validate the viability of our method and show significant improvement over the state-of-the-art.

Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

Directory of Open Access Journals (Sweden)

Na Tian

2015-01-01

Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

Selecting the Best of Portfolio Using OWA Operator Weights in Cross Efficiency-Evaluation

OpenAIRE

Sanei, Masoud; Banihashemi, Shokoofeh

2014-01-01

The present study is an attempt toward evaluating the performance of portfolios and asset selection using cross-efficiency evaluation. Cross-efficiency evaluation is an effective way of ranking decision making units (DMUs) in data envelopment analysis (DEA). The most widely used approach is to evaluate the efficiencies in each row or column in the cross-efficiency matrix with equal weights into an average cross-efficiency score for each DMU and consider it as the overall performance measureme...

Conflicts in Coalitions: A Stability Analysis of Robust Multi-City Regional Water Supply Portfolios

Science.gov (United States)

Gold, D.; Trindade, B. C.; Reed, P. M.; Characklis, G. W.

2017-12-01

Regional cooperation among water utilities can improve the robustness of urban water supply portfolios to deeply uncertain future conditions such as those caused by climate change or population growth. Coordination mechanisms such as water transfers, coordinated demand management, and shared infrastructure, can improve the efficiency of resource allocation and delay the need for new infrastructure investments. Regionalization does however come at a cost. Regionally coordinated water supply plans may be vulnerable to any emerging instabilities in the regional coalition. If one or more regional actors does not cooperate or follow the required regional actions in a time of crisis, the overall system performance may degrade. Furthermore, when crafting regional water supply portfolios, decision makers must choose a framework for measuring the performance of regional policies based on the evaluation of the objective values for each individual actor. Regional evaluations may inherently favor one actor's interests over those of another. This work focuses on four interconnected water utilities in the Research Triangle region of North Carolina for which robust regional water supply portfolios have previously been designed using multi-objective optimization to maximize the robustness of the worst performing utility across several objectives. This study 1) examines the sensitivity of portfolio performance to deviations from prescribed actions by individual utilities, 2) quantifies the implications of the regional formulation used to evaluate robustness for the portfolio performance of each individual utility and 3) elucidates the inherent regional tensions and conflicts that exist between utilities under this regionalization scheme through visual diagnostics of the system under simulated drought scenarios. Results of this analysis will help inform the creation of future regional water supply portfolios and provide insight into the nature of multi-actor water supply systems.

Portfolio Optimization

OpenAIRE

Issagali, Aizhan; Alshimbayeva, Damira; Zhalgas, Aidana

2015-01-01

In this paper Portfolio Optimization techniques were used to determine the most favorable investment portfolio. In particular, stock indices of three companies, namely Microsoft Corporation, Christian Dior Fashion House and Shevron Corporation were evaluated. Using this data the amounts invested in each asset when a portfolio is chosen on the efficient frontier were calculated. In addition, the Portfolio with minimum variance, tangency portfolio and optimal Markowitz portfolio are presented.

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

Science.gov (United States)

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case

Essays on Rational Portfolio Theory

DEFF Research Database (Denmark)

Nielsen, Simon Ellersgaard

market prices, we findonly a very modest improvement in portfolio wealth over the corresponding strategy whichonly trades in bonds and stocks. Optimal Hedge Tracking Portfolios in a Limit Order Book. In this paper we developa control theoretic solution to the manner in which a portfolio manager optimally...... shouldtrack a targeted D, given that he wishes to hedge a short position in European call optionsthe underlying of which is traded in a limit order book. Specifically, we are interested in theinterplay between posting limit and market orders respectively: when should the portfoliomanager do what (and at what......’s theory of optimal portfolio selection for wealth maximisingagents. In this paper we present a systematic analysis of the optimal asset allocation in aderivative-free market for the Heston model, the 3/2 model, and a Fong Vasicek type model.Under the assumption that the market price of risk...

Markowitz portfolio optimization model employing fuzzy measure

Science.gov (United States)

Ramli, Suhailywati; Jaaman, Saiful Hafizah

2017-04-01

Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.

Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

Science.gov (United States)

Tsaur, Ruey-Chyn

2015-02-01

In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.

Aircraft technology portfolio optimization using ant colony optimization

Science.gov (United States)

Villeneuve, Frederic J.; Mavris, Dimitri N.

2012-11-01

Technology portfolio selection is a combinatorial optimization problem often faced with a large number of combinations and technology incompatibilities. The main research question addressed in this article is to determine if Ant Colony Optimization (ACO) is better suited than Genetic Algorithms (GAs) and Simulated Annealing (SA) for technology portfolio optimization when incompatibility constraints between technologies are present. Convergence rate, capability to find optima, and efficiency in handling of incompatibilities are the three criteria of comparison. The application problem consists of finding the best technology portfolio from 29 aircraft technologies. The results show that ACO and GAs converge faster and find optima more easily than SA, and that ACO can optimize portfolios with technology incompatibilities without using penalty functions. This latter finding paves the way for more use of ACO when the number of constraints increases, such as in the technology and concept selection for complex engineering systems.

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty.

Science.gov (United States)

Chen, Xudong; Xu, Zhongwen; Yao, Liming; Ma, Ning

2018-03-05

This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

Non-convex multi-objective optimization

CERN Document Server

Pardalos, Panos M; Žilinskas, Julius

2017-01-01

Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in...

Methods of Choosing an Optimal Portfolio of Projects

OpenAIRE

Yakovlev, A.; Chernenko, M.

2016-01-01

This paper presents an analysis of existing methods for a portfolio of project optimization. The necessity for their improvement is shown. It is suggested to assess the portfolio of projects on the basis of the amount in the difference between the results and costs during development and implementation of selected projects and the losses caused by non-implementation or delayed implementation of projects that were not included in the portfolio. Consideration of capital and current costs compon...

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty

Directory of Open Access Journals (Sweden)

Xudong Chen

2018-03-01

Full Text Available This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

Optimal consumption—portfolio problem with CVaR constraints

International Nuclear Information System (INIS)

Zhang, Qingye; Gao, Yan

2016-01-01

The optimal portfolio selection is a fundamental issue in finance, and its two most important ingredients are risk and return. Merton's pioneering work in dynamic portfolio selection emphasized only the expected utility of the consumption and the terminal wealth. To make the optimal portfolio strategy be achievable, risk control over bankruptcy during the investment horizon is an indispensable ingredient. So, in this paper, we consider the consumption-portfolio problem coupled with a dynamic risk control. More specifically, different from the existing literature, we impose a dynamic relative CVaR constraint on it. By the stochastic dynamic programming techniques, we derive the corresponding Hamilton–Jacobi–Bellman (HJB) equation. Moreover, by the Lagrange multiplier method, the closed form solution is provided when the utility function is a logarithmic one. At last, an illustrative empirical study is given. The results show the distinct difference of the portfolio strategies with and without the CVaR constraints: the proportion invested in the risky assets is reduced over time with CVaR constraint instead of being constant without CVaR constraints. This can provide a good decision-making reference for the investors.

The Optimal Portfolio Selection Model under g-Expectation

Directory of Open Access Journals (Sweden)

Li Li

2014-01-01

complicated and sophisticated, the optimal solution turns out to be surprisingly simple, the payoff of a portfolio of two binary claims. Also I give the economic meaning of my model and the comparison with that one in the work of Jin and Zhou, 2008.

PORTFOLIO ANALYSIS BASED ON THE EXAMPLE OF ZAGREB STOCK EXCHANGE

Directory of Open Access Journals (Sweden)

Sinisa Bogdan

2010-06-01

Full Text Available In this paper we analyze the portfolio that was selected from the Zagreb Stock Exchange and also try to assess its risks and its future offerings that are relevant in making the decisions about investments. Through the work we will explain the importance of diversification and how the very diversification reduces risk. We will also analyze the systemic risk of individual stocks within the portfolio and the systemic risk of the given portfolio and explain its importance. Through regression analysis we will analyze the securities with the highest and lowest systemic risk and will clarify the results. At the end we will explain the correlation in the selected portfolio and point out the importance of the correlation and diversification itself.

NPD project portfolio selection using reinvestment strategy in competitive environment

Directory of Open Access Journals (Sweden)

Alireza Ghassemi

2018-01-01

Full Text Available This study aims to design a new model for selecting most fitting new product development projects in a pool of projects. To catch the best model, we assume new products will be introduced to the competitive markets. Also, we suppose the revenue yielded by completed projects can be reinvested on implementation of other projects. Other sources of financing are borrowing loans from banks and initial capital of the firm. These limited resources determine most evaluated projects to be performed. Several types of interactions among different projects are considered to make the chosen projects more like a portfolio. In addition, some numerical examples from the real world are provided to demonstrate the applicability of the proposed model. These examples show how the particular considerations in the suggested model affect the results.

Application of Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA Method for Bank Branch Location Selection

Directory of Open Access Journals (Sweden)

Ali Gorener

2013-04-01

Full Text Available Location selection problem in banking is an important issue for the commercial success in competitive environment. There is a strategic fit between the location selection decision and overall performance of a new branch. Providing physical service in requested location as well as alternative distribution channels to meet profitable client needs is the current problematic to achieve the competitive advantage over the rivalry in financial system. In this paper, an integrated model has been developed to support in the decision of branch location selection for a new bank branch. Analytic Hierarchy Process (AHP technique has been conducted to prioritize of evaluation criteria, and multi-objective optimization on the basis of ratio analysis (MOORA method has been applied to rank location alternatives of bank branch.   

portfolio optimization based on nonparametric estimation methods

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mahsa ghandehari

2017-03-01

Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.

Managing risk and expected financial return from selective expansion of operating room capacity: mean-variance analysis of a hospital's portfolio of surgeons.

Science.gov (United States)

Dexter, Franklin; Ledolter, Johannes

2003-07-01

Surgeons using the same amount of operating room (OR) time differ in their achieved hospital contribution margins (revenue minus variable costs) by >1000%. Thus, to improve the financial return from perioperative facilities, OR strategic decisions should selectively focus additional OR capacity and capital purchasing on a few surgeons or subspecialties. These decisions use estimates of each surgeon's and/or subspecialty's contribution margin per OR hour. The estimates are subject to uncertainty (e.g., from outliers). We account for the uncertainties by using mean-variance portfolio analysis (i.e., quadratic programming). This method characterizes the problem of selectively expanding OR capacity based on the expected financial return and risk of different portfolios of surgeons. The assessment reveals whether the choices, of which surgeons have their OR capacity expanded, are sensitive to the uncertainties in the surgeons' contribution margins per OR hour. Thus, mean-variance analysis reduces the chance of making strategic decisions based on spurious information. We also assess the financial benefit of using mean-variance portfolio analysis when the planned expansion of OR capacity is well diversified over at least several surgeons or subspecialties. Our results show that, in such circumstances, there may be little benefit from further changing the portfolio to reduce its financial risk. Surgeon and subspecialty specific hospital financial data are uncertain, a fact that should be taken into account when making decisions about expanding operating room capacity. We show that mean-variance portfolio analysis can incorporate this uncertainty, thereby guiding operating room management decision-making and reducing the chance of a strategic decision being made based on spurious information.

A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

Directory of Open Access Journals (Sweden)

Zhiming Song

2015-01-01

Full Text Available As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

A class of multi-period semi-variance portfolio for petroleum exploration and development

Science.gov (United States)

Guo, Qiulin; Li, Jianzhong; Zou, Caineng; Guo, Yujuan; Yan, Wei

2012-10-01

Variance is substituted by semi-variance in Markowitz's portfolio selection model. For dynamic valuation on exploration and development projects, one period portfolio selection is extended to multi-period. In this article, a class of multi-period semi-variance exploration and development portfolio model is formulated originally. Besides, a hybrid genetic algorithm, which makes use of the position displacement strategy of the particle swarm optimiser as a mutation operation, is applied to solve the multi-period semi-variance model. For this class of portfolio model, numerical results show that the mode is effective and feasible.

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

International Nuclear Information System (INIS)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong; Choi, Jae Ho

2009-01-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of); Choi, Jae Ho [Samsung Techwin Co., Ltd., Changwon (Korea, Republic of)

2009-07-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with {epsilon}-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

Portfolio optimization with structured products under return constraint

Directory of Open Access Journals (Sweden)

Baweja Meena

2015-01-01

Full Text Available A new approach for optimizing risk in a portfolio of financial instruments involving structured products is presented. This paper deals with a portfolio selection model which uses optimization methodology to minimize conditional Value-at-Risk (CVaR under return constraint. It focuses on minimizing CVaR rather than on minimizing value-at-Risk VaR, as portfolios with low CVaR necessarily have low VaR as well. We consider a simple investment problem where besides stocks and bonds, the investor can also include structured products into the investment portfolio. Due to possible intermediate payments from structured product, we have to deal with a re-investment problem modeled as a linear optimization problem.

Can One Portfolio Measure the Six ACGME General Competencies?

Science.gov (United States)

Jarvis, Robert M.; O'Sullivan, Patricia S.; McClain, Tina; Clardy, James A.

2004-01-01

Objective: To determine that portfolios, useable by any program, can provide needed evidence of resident performance within the ACGME general competencies. Methods: Eighteen residents constructed portfolios with selected entries from thirteen psychiatric skills. Two raters assessed whether entries reflected resident performance within the general…

Growth Optimal Portfolio Selection Under Proportional Transaction Costs with Obligatory Diversification

International Nuclear Information System (INIS)

Duncan, T.; Pasik Duncan, B.; Stettner, L.

2011-01-01

A continuous time long run growth optimal or optimal logarithmic utility portfolio with proportional transaction costs consisting of a fixed proportional cost and a cost proportional to the volume of transaction is considered. The asset prices are modeled as exponent of diffusion with jumps whose parameters depend on a finite state Markov process of economic factors. An obligatory portfolio diversification is introduced, accordingly to which it is required to invest at least a fixed small portion of our wealth in each asset.

Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm

Directory of Open Access Journals (Sweden)

Mohammad Ali Barati

2016-04-01

Full Text Available Multi-period models of portfolio selection have been developed in the literature with respect to certain assumptions. In this study, for the first time, the portfolio selection problem has been modeled based on mean-semi variance with transaction cost and minimum transaction lots considering functional constraints and fuzzy parameters. Functional constraints such as transaction cost and minimum transaction lots were included. In addition, the returns on assets parameters were considered as trapezoidal fuzzy numbers. An efficient genetic algorithm (GA was designed, results were analyzed using numerical instances and sensitivity analysis were executed. In the numerical study, the problem was solved based on the presence or absence of each mode of constraints including transaction costs and minimum transaction lots. In addition, with the use of sensitivity analysis, the results of the model were presented with the variations of minimum expected rate of programming periods.

System for selecting a postponement strategy portfolio for supply chains

Directory of Open Access Journals (Sweden)

Luiz Eduardo Simão

2015-03-01

Full Text Available The stagnation of the economy has increased competition and uncertainty in the industrial sector. Trends such as the increase in the proliferation of the variety of products and the requirement for customization of products has contributed to difficulties in forecasting demand, due to increased uncertainty of demand for final products. In this new competitive environment, it is no longer possible to use the traditional “one size fits all” supply chain process, with unique policies for all products because this practice can lead to significant profitability losses due to the increase in stock levels and lost sales. However, research on supply chains has given relatively little attention to the need to use different, segmented supply chain strategies as well as to develop and manage these multiple supply chains strategies simultaneously. Thus, this paper aims to present an approach for selecting a portfolio of postponement strategies based on segmentation of supply chain, based on analysis of the demand profile (volume-variety analysis and a tool to assist in the selection of postponement strategies driven by the customer-product sector and their respective propositions of value.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Institute of Scientific and Technical Information of China (English)

Guo Jiansheng; Wang Zutong; Zheng Mingfa; Wang Ying

2014-01-01

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

Management of Portfolio Investment Held by Pension Funds

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Dan Armeanu

2008-09-01

Full Text Available As a result of the fact that pension funds are financial intermediaries, the value of their assets and liabilities is influenced by changing conditions in financial markets. The market image of a pension fund (and hence its perceived value are closely tied to the “financial health” of the fund. Setting up and managing complex investment portfolios requires that pension administrators use scientific models of portfolio selection and optimization based on the risk-expected return relationship. Most investment portfolios are modified in time as result of changing stock prices and investment policy objectives. Having established investment policy guidelines, the administrators of pension funds have to determine the structure of their portfolios so that the latter meet legal requirements.

The electricity portfolio simulation model (EPSim) technical description.

Energy Technology Data Exchange (ETDEWEB)

Drennen, Thomas E.; Klotz, Richard (Hobart and William Smith Colleges, Geneva, NY)

2005-09-01

Stakeholders often have competing interests when selecting or planning new power plants. The purpose of developing this preliminary Electricity Portfolio Simulation Model (EPSim) is to provide a first cut, dynamic methodology and approach to this problem, that can subsequently be refined and validated, that may help energy planners, policy makers, and energy students better understand the tradeoffs associated with competing electricity portfolios. EPSim allows the user to explore competing electricity portfolios annually from 2002 to 2025 in terms of five different criteria: cost, environmental impacts, energy dependence, health and safety, and sustainability. Four additional criteria (infrastructure vulnerability, service limitations, policy needs and science and technology needs) may be added in future versions of the model. Using an analytic hierarchy process (AHP) approach, users or groups of users apply weights to each of the criteria. The default energy assumptions of the model mimic Department of Energy's (DOE) electricity portfolio to 2025 (EIA, 2005). At any time, the user can compare alternative portfolios to this reference case portfolio.

Project Portfolio Management: An Investigation of One Air Force Product Center

National Research Council Canada - National Science Library

Edmunds, Bryan D

2005-01-01

.... This research focuses on the portfolio management (project selection and resource allocation) part of the CTRRP. The purpose of this research effort was to investigate the use of portfolio management within the Air Force...

Mean-variance portfolio selection and efficient frontier for defined contribution pension schemes

DEFF Research Database (Denmark)

Højgaard, Bjarne; Vigna, Elena

We solve a mean-variance portfolio selection problem in the accumulation phase of a defined contribution pension scheme. The efficient frontier, which is found for the 2 asset case as well as the n + 1 asset case, gives the member the possibility to decide his own risk/reward profile. The mean...... as a mean-variance optimization problem. It is shown that the corresponding mean and variance of the final fund belong to the efficient frontier and also the opposite, that each point on the efficient frontier corresponds to a target-based optimization problem. Furthermore, numerical results indicate...... that the largely adopted lifestyle strategy seems to be very far from being efficient in the mean-variance setting....

Multi-objective evacuation routing optimization for toxic cloud releases

International Nuclear Information System (INIS)

Gai, Wen-mei; Deng, Yun-feng; Jiang, Zhong-an; Li, Jing; Du, Yan

2017-01-01

This paper develops a model for assessing the risks associated with the evacuation process in response to potential chemical accidents, based on which a multi-objective evacuation routing model for toxic cloud releases is proposed taking into account that the travel speed on each arc will be affected by disaster extension. The objectives of the evacuation routing model are to minimize travel time and individual evacuation risk along a path respectively. Two heuristic algorithms are proposed to solve the multi-objective evacuation routing model. Simulation results show the effectiveness and feasibility of the model and algorithms presented in this paper. And, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency route selection in other cases (fires, nuclear accidents). - Highlights: • A model for assessing and visualizing the risks is developed. • A multi-objective evacuation routing model is proposed for toxic cloud releases. • A modified Dijkstra algorithm is designed to obtain an solution of the model. • Two heuristic algorithms have been developed as the optimization tool.

Backtesting Portfolio Value-at-Risk with Estimated Portfolio Weights

OpenAIRE

Pei Pei

2010-01-01

This paper theoretically and empirically analyzes backtesting portfolio VaR with estimation risk in an intrinsically multivariate framework. For the first time in the literature, it takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating the portfolio weights as well as that from estimating the multivariate dynamic model of asset returns make the existing methods in a univariate framew...

Land-Use Portfolio Modeler, Version 1.0

Science.gov (United States)

Taketa, Richard; Hong, Makiko

2010-01-01

Natural hazards pose significant threats to the public safety and economic health of many communities throughout the world. Community leaders and decision-makers continually face the challenges of planning and allocating limited resources to invest in protecting their communities against catastrophic losses from natural-hazard events. Public efforts to assess community vulnerability and encourage loss-reduction measures through mitigation often focused on either aggregating site-specific estimates or adopting standards based upon broad assumptions about regional risks. The site-specific method usually provided the most accurate estimates, but was prohibitively expensive, whereas regional risk assessments were often too general to be of practical use. Policy makers lacked a systematic and quantitative method for conducting a regional-scale risk assessment of natural hazards. In response, Bernknopf and others developed the portfolio model, an intermediate-scale approach to assessing natural-hazard risks and mitigation policy alternatives. The basis for the portfolio-model approach was inspired by financial portfolio theory, which prescribes a method of optimizing return on investment while reducing risk by diversifying investments in different security types. In this context, a security type represents a unique combination of features and hazard-risk level, while financial return is defined as the reduction in losses resulting from an investment in mitigation of chosen securities. Features are selected for mitigation and are modeled like investment portfolios. Earth-science and economic data for the features are combined and processed in order to analyze each of the portfolios, which are then used to evaluate the benefits of mitigating the risk in selected locations. Ultimately, the decision maker seeks to choose a portfolio representing a mitigation policy that maximizes the expected return-on-investment, while minimizing the uncertainty associated with that return

Risk-averse portfolio selection of renewable electricity generator investments in Brazil: An optimised multi-market commercialisation strategy

International Nuclear Information System (INIS)

Maier, Sebastian; Street, Alexandre; McKinnon, Ken

2016-01-01

Investment decisions in renewable energy sources such as small hydro, wind power, biomass and solar are frequently made in the context of enormous uncertainty surrounding both intermittent generation and the highly volatile electricity spot prices that are used for clearing of trades. This paper presents a new portfolio-based approach for selecting long-term investments in small-scale renewable energy projects and matching contracts for the sale of the resulting electricity. Using this approach, we have formulated a stochastic optimisation model that maximises a holding company's risk-averse measure of value. Using an illustrative example representative of investment decisions within the Brazilian electricity system, we investigate the sensitivity of the optimised portfolio composition and commercialisation strategy to contract prices in the free contracting environment and to the decision maker's attitude towards risk. The numerical results demonstrate it is possible to reduce significantly financial risks, such as the price-quantity risk, not only by exploiting the complementarity of the considered renewable sources generation profiles, but also by selecting the optimal mix of commercialisation contracts from different markets. We find that the multi-market strategy generally results in appreciably higher optimal value than single-market strategies and can be applied to a wide range of renewable generators and contracts. - Highlights: • Gives a portfolio-based multi-market, multi-asset approach to renewable investment. • Details how to model currently used contract types in each of the Brazilian markets. • Presents a test case using realistic contract and real renewable data from Brazil. • Shows that the approach controls financial risks and boosts optimal values. • Explains how relative contract prices and attitude to risk affect optimal decisions.

Continuous-Time Portfolio Selection and Option Pricing under Risk-Minimization Criterion in an Incomplete Market

Directory of Open Access Journals (Sweden)

Xinfeng Ruan

2013-01-01

Full Text Available We study option pricing with risk-minimization criterion in an incomplete market where the dynamics of the risky underlying asset are governed by a jump diffusion equation. We obtain the Radon-Nikodym derivative in the minimal martingale measure and a partial integrodifferential equation (PIDE of European call option. In a special case, we get the exact solution for European call option by Fourier transformation methods. Finally, we employ the pricing kernel to calculate the optimal portfolio selection by martingale methods.

Making practice transparent through e-portfolio.

Science.gov (United States)

Stewart, Sarah M

2013-12-01

Midwives are required to maintain a professional portfolio as part of their statutory requirements. Some midwives are using open social networking tools and processes to develop an e-portfolio. However, confidentiality of patient and client data and professional reputation have to be taken into consideration when using online public spaces for reflection. There is little evidence about how midwives use social networking tools for ongoing learning. It is uncertain how reflecting in an e-portfolio with an audience impacts on learning outcomes. This paper investigates ways in which reflective midwifery practice be carried out using e-portfolio in open, social networking platforms using collaborative processes. Using an auto-ethnographic approach I explored my e-portfolio and selected posts that had attracted six or more comments. I used thematic analysis to identify themes within the textual conversations in the posts and responses posted by readers. The analysis identified that my collaborative e-portfolio had four themes: to provide commentary and discuss issues; to reflect and process learning; to seek advice, brainstorm and process ideas for practice, projects and research, and provide evidence of professional development. E-portfolio using open social networking tools and processes is a viable option for midwives because it facilitates collaborative reflection and shared learning. However, my experience shows that concerns about what people think, and client confidentiality does impact on the nature of open reflection and learning outcomes. I conclude this paper with a framework for managing midwifery statutory obligations using online public spaces and social networking tools. Copyright © 2013 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

A Polynomial Optimization Approach to Constant Rebalanced Portfolio Selection

NARCIS (Netherlands)

Takano, Y.; Sotirov, R.

2010-01-01

We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on

A polynomial optimization approach to constant rebalanced portfolio selection

NARCIS (Netherlands)

Takano, Y.; Sotirov, R.

2012-01-01

We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on

Approximating multi-objective scheduling problems

NARCIS (Netherlands)

Dabia, S.; Talbi, El-Ghazali; Woensel, van T.; Kok, de A.G.

2013-01-01

In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions that represent the interactions between the different objectives. Due to

Adaptive scalarization methods in multiobjective optimization

CERN Document Server

Eichfelder, Gabriele

2008-01-01

This book presents adaptive solution methods for multiobjective optimization problems based on parameter dependent scalarization approaches. Readers will benefit from the new adaptive methods and ideas for solving multiobjective optimization.

NON-CONVENTIONAL MACHINING PROCESSES SELECTION USING MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS METHOD

Directory of Open Access Journals (Sweden)

MILOŠ MADIĆ

2015-11-01

Full Text Available The role of non-conventional machining processes (NCMPs in today’s manufacturing environment has been well acknowledged. For effective utilization of the capabilities and advantages of different NCMPs, selection of the most appropriate NCMP for a given machining application requires consideration of different conflicting criteria. The right choice of the NCMP is critical to the success and competitiveness of the company. As the NCMP selection problem involves consideration of different conflicting criteria, of different relative importance, the multi-criteria decision making (MCDM methods are very useful in systematical selection of the most appropriate NCMP. This paper presents the application of a recent MCDM method, i.e., the multi-objective optimization on the basis of ratio analysis (MOORA method to solve NCMP selection which has been defined considering different performance criteria of four most widely used NCMPs. In order to determine the relative significance of considered quality criteria a pair-wise comparison matrix of the analytic hierarchy process was used. The results obtained using the MOORA method showed perfect correlation with those obtained by the technique for order preference by similarity to ideal solution (TOPSIS method which proves the applicability and potentiality of this MCDM method for solving complex NCMP selection problems.

Optimal Investment Under Transaction Costs: A Threshold Rebalanced Portfolio Approach

Science.gov (United States)

Tunc, Sait; Donmez, Mehmet Ali; Kozat, Suleyman Serdar

2013-06-01

We study optimal investment in a financial market having a finite number of assets from a signal processing perspective. We investigate how an investor should distribute capital over these assets and when he should reallocate the distribution of the funds over these assets to maximize the cumulative wealth over any investment period. In particular, we introduce a portfolio selection algorithm that maximizes the expected cumulative wealth in i.i.d. two-asset discrete-time markets where the market levies proportional transaction costs in buying and selling stocks. We achieve this using "threshold rebalanced portfolios", where trading occurs only if the portfolio breaches certain thresholds. Under the assumption that the relative price sequences have log-normal distribution from the Black-Scholes model, we evaluate the expected wealth under proportional transaction costs and find the threshold rebalanced portfolio that achieves the maximal expected cumulative wealth over any investment period. Our derivations can be readily extended to markets having more than two stocks, where these extensions are pointed out in the paper. As predicted from our derivations, we significantly improve the achieved wealth over portfolio selection algorithms from the literature on historical data sets.

METHODICAL BASES OF MANAGEMENT OF INSURANCE PORTFOLIO

Directory of Open Access Journals (Sweden)

Serdechna Yulia

2018-01-01

Full Text Available Introduction. Despite the considerable arsenal of developments in the issues of assessing the management of the insurance portfolio remains unresolved. In order to detail, specify and further systematize the indicators for the indicated evaluation, the publications of scientists are analyzed. The purpose of the study is to analyze existing methods by which it is possible to formulate and manage the insurance portfolio in order to achieve its balance, which will contribute to ensuring the financial reliability of the insurance company. Results. The description of the essence of the concept of “management of insurance portfolio”, as the application of actuarial methods and techniques to the combination of various insurance risks offered for insurance or are already part of the insurance portfolio, allowing to adjust the size and structure of the portfolio in order to ensure its financial stability, achievement the maximum level of income of an insurance organization, preservation of the value of its equity and financial security of insurance liabilities. It is determined that the main methods by which the insurer’s insurance portfolio can be formed and managed is the selection of risks; reinsurance operations that ensure diversification of risks; formation and placement of insurance reserves, which form the financial basis of insurance activities. The method of managing an insurance portfolio, which can be both active and passive, is considered. Conclusions. It is determined that the insurance portfolio is the basis on which all the activities of the insurer are based and which determines its financial stability. The combination of methods and technologies applied to the insurance portfolio is a management method that can be both active and passive and has a number of specific methods through which the insurer’s insurance portfolio can be formed and managed. It is substantiated that each insurance company aims to form an efficient and

An improved fast and elitist multi-objective genetic algorithm-ANSGA-II for multi-objective optimization of inverse radiotherapy treatment planning

International Nuclear Information System (INIS)

Cao Ruifen; Li Guoli; Song Gang; Zhao Pan; Lin Hui; Wu Aidong; Huang Chenyu; Wu Yican

2007-01-01

Objective: To provide a fast and effective multi-objective optimization algorithm for inverse radiotherapy treatment planning system. Methods: Non-dominated Sorting Genetic Algorithm-NSGA-II is a representative of multi-objective evolutionary optimization algorithms and excels the others. The paper produces ANSGA-II that makes use of advantage of NSGA-II, and uses adaptive crossover and mutation to improve its flexibility; according the character of inverse radiotherapy treatment planning, the paper uses the pre-known knowledge to generate individuals of every generation in the course of optimization, which enhances the convergent speed and improves efficiency. Results: The example of optimizing average dose of a sheet of CT, including PTV, OAR, NT, proves the algorithm could find satisfied solutions in several minutes. Conclusions: The algorithm could provide clinic inverse radiotherapy treatment planning system with selection of optimization algorithms. (authors)

Mean-variance model for portfolio optimization with background risk based on uncertainty theory

Science.gov (United States)

Zhai, Jia; Bai, Manying

2018-04-01

The aim of this paper is to develop a mean-variance model for portfolio optimization considering the background risk, liquidity and transaction cost based on uncertainty theory. In portfolio selection problem, returns of securities and assets liquidity are assumed as uncertain variables because of incidents or lacking of historical data, which are common in economic and social environment. We provide crisp forms of the model and a hybrid intelligent algorithm to solve it. Under a mean-variance framework, we analyze the portfolio frontier characteristic considering independently additive background risk. In addition, we discuss some effects of background risk and liquidity constraint on the portfolio selection. Finally, we demonstrate the proposed models by numerical simulations.

Improved multi-objective clustering algorithm using particle swarm optimization.

Science.gov (United States)

Gong, Congcong; Chen, Haisong; He, Weixiong; Zhang, Zhanliang

2017-01-01

Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

A multiobjective approach to the genetic code adaptability problem.

Science.gov (United States)

de Oliveira, Lariza Laura; de Oliveira, Paulo S L; Tinós, Renato

2015-02-19

The organization of the canonical code has intrigued researches since it was first described. If we consider all codes mapping the 64 codes into 20 amino acids and one stop codon, there are more than 1.51×10(84) possible genetic codes. The main question related to the organization of the genetic code is why exactly the canonical code was selected among this huge number of possible genetic codes. Many researchers argue that the organization of the canonical code is a product of natural selection and that the code's robustness against mutations would support this hypothesis. In order to investigate the natural selection hypothesis, some researches employ optimization algorithms to identify regions of the genetic code space where best codes, according to a given evaluation function, can be found (engineering approach). The optimization process uses only one objective to evaluate the codes, generally based on the robustness for an amino acid property. Only one objective is also employed in the statistical approach for the comparison of the canonical code with random codes. We propose a multiobjective approach where two or more objectives are considered simultaneously to evaluate the genetic codes. In order to test our hypothesis that the multiobjective approach is useful for the analysis of the genetic code adaptability, we implemented a multiobjective optimization algorithm where two objectives are simultaneously optimized. Using as objectives the robustness against mutation with the amino acids properties polar requirement (objective 1) and robustness with respect to hydropathy index or molecular volume (objective 2), we found solutions closer to the canonical genetic code in terms of robustness, when compared with the results using only one objective reported by other authors. Using more objectives, more optimal solutions are obtained and, as a consequence, more information can be used to investigate the adaptability of the genetic code. The multiobjective approach

HEURISTIC APPROACHES FOR PORTFOLIO OPTIMIZATION

OpenAIRE

Manfred Gilli, Evis Kellezi

2000-01-01

The paper first compares the use of optimization heuristics to the classical optimization techniques for the selection of optimal portfolios. Second, the heuristic approach is applied to problems other than those in the standard mean-variance framework where the classical optimization fails.

Customer portfolios

DEFF Research Database (Denmark)

Clarke, Ann Højbjerg; Freytag, Per Vagn; Zolkiewski, Judith

2017-01-01

gives managers a tool to help to cope with the dynamic aspects of the customer portfolio. Recognition of the importance of communication to the process, the development of trust and the role of legitimacy also provides areas that managers can focus upon in their relationship management processes......Purpose The purpose of this paper is to extend the discussion about customer portfolios beyond simple identification of models and how they can be used for balanced resource allocation to a discussion about how portfolios should take into account views from relationship partners and how they should...... that helps improve the understanding of how customer portfolio models can actually be applied from a relational perspective. Findings The key aspects of the conceptual framework relate to how alignment of the relationships in the portfolio is achieved. Critical to this are the interaction spaces...

Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

Science.gov (United States)

Sahraei, S.; Asadzadeh, M.

2017-12-01

Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

Portfolio analysis based on the example of Zagreb Stock Exchange

OpenAIRE

Bogdan, Sinisa; Baresa, Suzana; Ivanovic, Sasa

2010-01-01

In this paper we analyze the portfolio that was selected from the Zagreb Stock Exchange and also try to assess its risks and its future offerings that are relevant in making the decisions about investments. Through the work we will explain the importance of diversification and how the very diversification reduces risk. We will also analyze the systemic risk of individual stocks within the portfolio and the systemic risk of the given portfolio and explain its importance. Through regression ana...

Cross sectional moments and portfolio returns: Evidence for select emerging markets

Directory of Open Access Journals (Sweden)

Sanjay Sehgal

2016-09-01

Full Text Available Research does not indicate a consensus on the relationship between idiosyncratic volatility and asset returns. Moreover, the role of cross sectional higher order moments in predicting market returns is relatively unexplored. We show that the cross sectional volatility measure suggested by Garcia et al. is highly correlated with alternative measures of idiosyncratic volatility constructed as variance of errors from the capital asset pricing model and the Fama French model. We find that cross sectional moments help in predicting aggregate market returns in some sample countries and also provide information for portfolio formation, which is more consistent for portfolios sorted on sensitivity to cross sectional skewness.

Sustainability-Oriented Financial Resource Allocation in a Project Portfolio through Multi-Criteria Decision-Making

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Nomeda Dobrovolskienė

2016-05-01

Full Text Available Modern portfolio theory attempts to maximize the expected return of a portfolio for a given level of portfolio risk, or equivalently minimize risk for a given level of expected return. The reality, however, shows that, when selecting projects to a portfolio and allocating resources in the portfolio, an increasing number of organizations take into account other aspects as well. As a result of the sole purpose (risk-return, it offers only a partial solution for a sustainable organization. Existing project portfolio selection and resource allocation methods and models do not consider sustainability. Therefore, the aim of this article is to develop a sustainability-oriented model of financial resource allocation in a project portfolio by integrating a composite sustainability index of a project into Markowitz’s classical risk-return scheme (mean-variance model. The model was developed by applying multi-criteria decision-making methods. The practicability of the model was tested by an empirical study in a selected construction company. The proposed sustainability-oriented financial resource allocation model could be used in allocating financial resources in any type of business. The use of the model would not only help organisations to manage risk and achieve higher return but would also allow carrying out sustainable projects, thereby promoting greater environmental responsibility and giving more consideration to the wellbeing of future generations. Moreover, the model allows quantifying the impact of the integration of sustainability into financial resource allocation on the return of a portfolio.

The Effects of Portfolio Assessment on Writing of EFL Students

Science.gov (United States)

Nezakatgoo, Behzad

2011-01-01

The primary focus of this study was to determine the effect of portfolio assessment on final examination scores of EFL students' writing skill. To determine the impact of portfolio-based writing assessment 40 university students who enrolled in composition course were initially selected and divided randomly into two experimental and control…

77 FR 55903 - Confirmation, Portfolio Reconciliation, Portfolio Compression, and Swap Trading Relationship...

Science.gov (United States)

2012-09-11

... Vol. 77 Tuesday, No. 176 September 11, 2012 Part II Commodity Futures Trading Commission 17 CFR Part 23 Confirmation, Portfolio Reconciliation, Portfolio Compression, and Swap Trading Relationship... FUTURES TRADING COMMISSION 17 CFR Part 23 RIN 3038-AC96 Confirmation, Portfolio Reconciliation, Portfolio...

Solving stochastic multiobjective vehicle routing problem using probabilistic metaheuristic

Directory of Open Access Journals (Sweden)

Gannouni Asmae

2017-01-01

closed form expression. This novel approach is based on combinatorial probability and can be incorporated in a multiobjective evolutionary algorithm. (iiProvide probabilistic approaches to elitism and diversification in multiobjective evolutionary algorithms. Finally, The behavior of the resulting Probabilistic Multi-objective Evolutionary Algorithms (PrMOEAs is empirically investigated on the multi-objective stochastic VRP problem.

Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria

Science.gov (United States)

Kowalczuk, Zdzisław; Białaszewski, Tomasz

2018-01-01

A novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals and applied during parental crossover in the processes of evolutionary multi-objective optimization (EMOO). The article introduces the principles of the genetic-gender approach (GGA) and virtual gender approach (VGA), which are not just evolutionary techniques, but constitute a completely new rule (philosophy) for use in solving MOO tasks. The proposed approaches are validated against principal representatives of the EMOO algorithms of the state of the art in solving benchmark problems in the light of recognized EC performance criteria. The research shows the superiority of the gender approach in terms of effectiveness, reliability, transparency, intelligibility and MOO problem simplification, resulting in the great usefulness and practicability of GGA and VGA. Moreover, an important feature of GGA and VGA is that they alleviate the 'curse' of dimensionality typical of many engineering designs.

Risk modelling in portfolio optimization

Science.gov (United States)

Lam, W. H.; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi

2013-09-01

Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio.

Comparative evaluation of fuzzy logic and genetic algorithms models for portfolio optimization

Directory of Open Access Journals (Sweden)

Heidar Masoumi Soureh

2017-03-01

Full Text Available Selection of optimum methods which have appropriate speed and precision for planning and de-cision-making has always been a challenge for investors and managers. One the most important concerns for them is investment planning and optimization for acquisition of desirable wealth under controlled risk with the best return. This paper proposes a model based on Markowitz the-orem by considering the aforementioned limitations in order to help effective decisions-making for portfolio selection. Then, the model is investigated by fuzzy logic and genetic algorithms, for the optimization of the portfolio in selected active companies listed in Tehran Stock Exchange over the period 2012-2016 and the results of the above models are discussed. The results show that the two studied models had functional differences in portfolio optimization, its tools and the possibility of supplementing each other and their selection.

Optimization of China's generating portfolio and policy implications based on portfolio theory

International Nuclear Information System (INIS)

Zhu, Lei; Fan, Ying

2010-01-01

This paper applies portfolio theory to evaluate China's 2020-medium-term plans for generating technologies and its generating portfolio. With reference to the risk of relevant generating-cost streams, the paper discusses China's future development of efficient (Pareto optimal) generating portfolios that enhance energy security in different scenarios, including CO 2 -emission-constrained scenarios. This research has found that the future adjustment of China's planned 2020 generating portfolio can reduce the portfolio's cost risk through appropriate diversification of generating technologies, but a price will be paid in the form of increased generating cost. In the CO 2 -emission-constrained scenarios, the generating-cost risk of China's planned 2020 portfolio is even greater than that of the 2005 portfolio, but increasing the proportion of nuclear power in the generating portfolio can reduce the cost risk effectively. For renewable-power generation, because of relatively high generating costs, it will be necessary to obtain stronger policy support to promote renewable-power development.

Portfolio Optimization of Nanomaterial Use in Clean Energy Technologies.

Science.gov (United States)

Moore, Elizabeth A; Babbitt, Callie W; Gaustad, Gabrielle; Moore, Sean T

2018-04-03

While engineered nanomaterials (ENMs) are increasingly incorporated in diverse applications, risks of ENM adoption remain difficult to predict and mitigate proactively. Current decision-making tools do not adequately account for ENM uncertainties including varying functional forms, unique environmental behavior, economic costs, unknown supply and demand, and upstream emissions. The complexity of the ENM system necessitates a novel approach: in this study, the adaptation of an investment portfolio optimization model is demonstrated for optimization of ENM use in renewable energy technologies. Where a traditional investment portfolio optimization model maximizes return on investment through optimal selection of stock, ENM portfolio optimization maximizes the performance of energy technology systems by optimizing selective use of ENMs. Cumulative impacts of multiple ENM material portfolios are evaluated in two case studies: organic photovoltaic cells (OPVs) for renewable energy and lithium-ion batteries (LIBs) for electric vehicles. Results indicate ENM adoption is dependent on overall performance and variance of the material, resource use, environmental impact, and economic trade-offs. From a sustainability perspective, improved clean energy applications can help extend product lifespans, reduce fossil energy consumption, and substitute ENMs for scarce incumbent materials.

Improved multi-objective clustering algorithm using particle swarm optimization.

Directory of Open Access Journals (Sweden)

Congcong Gong

Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

A comparison of portfolio selection models via application on ISE 100 index data

Science.gov (United States)

Altun, Emrah; Tatlidil, Hüseyin

2013-10-01

Markowitz Model, a classical approach to portfolio optimization problem, relies on two important assumptions: the expected return is multivariate normally distributed and the investor is risk averter. But this model has not been extensively used in finance. Empirical results show that it is very hard to solve large scale portfolio optimization problems with Mean-Variance (M-V)model. Alternative model, Mean Absolute Deviation (MAD) model which is proposed by Konno and Yamazaki [7] has been used to remove most of difficulties of Markowitz Mean-Variance model. MAD model don't need to assume that the probability of the rates of return is normally distributed and based on Linear Programming. Another alternative portfolio model is Mean-Lower Semi Absolute Deviation (M-LSAD), which is proposed by Speranza [3]. We will compare these models to determine which model gives more appropriate solution to investors.

A Risk-Free Protection Index Model for Portfolio Selection with Entropy Constraint under an Uncertainty Framework

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Jianwei Gao

2017-02-01

Full Text Available This paper aims to develop a risk-free protection index model for portfolio selection based on the uncertain theory. First, the returns of risk assets are assumed as uncertain variables and subject to reputable experts’ evaluations. Second, under this assumption, combining with the risk-free interest rate we define a risk-free protection index (RFPI, which can measure the protection degree when the loss of risk assets happens. Third, note that the proportion entropy serves as a complementary means to reduce the risk by the preset diversification requirement. We put forward a risk-free protection index model with an entropy constraint under an uncertainty framework by applying the RFPI, Huang’s risk index model (RIM, and mean-variance-entropy model (MVEM. Furthermore, to solve our portfolio model, an algorithm is given to estimate the uncertain expected return and standard deviation of different risk assets by applying the Delphi method. Finally, an example is provided to show that the risk-free protection index model performs better than the traditional MVEM and RIM.

Multi-objective optimization approach for air traffic flow management

Directory of Open Access Journals (Sweden)

Fadil Rabie

2017-01-01

The decision-making stage was then performed with the aid of data clustering techniques to reduce the sizeof the Pareto-optimal set and obtain a smaller representation of the multi-objective design space, there by making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

Portfolio Diversification in the South-East European Equity Markets

Directory of Open Access Journals (Sweden)

Zaimovic Azra

2017-04-01

Full Text Available Diversification potential enables investors to manage their risk and decrease risk exposure. Good diversification policy is a safety net that prevents a portfolio from losing its value. A well-diversified portfolio consists of different categories of property with low correlations, while highly correlated markets have the feature of low possibilities for diversification. The biggest riddle in the world of investments is to find the optimal portfolio within a set of available assets with limited capital. There are numerous studies and mathematical models that deal with portfolio investment strategies. These strategies take advantage of diversification by spreading risk over several financial assets. Modern portfolio theory seeks to find the optimal model with the best results. This paper tries to identify relationships between returns of companies traded in South-East European equity markets. A Markowitz mean-variance (MV portfolio optimization method is used to identify possibilities for diversification among these markets and world leading capital markets. This research also offers insight into to the level of integration of South-East European equity markets. Principal component analysis (PCA is used to determine components that describe the strong patterns and co-movements of the dataset. Finally, we combined MV efficient frontier and equity, which represent PCA components, to draw conclusions. Our findings show that PC analysis substantially simplifies asset selection process in portfolio management. The results of the paper have practical applications for portfolio investors.

Venture capital and efficiency of portfolio companies

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A. Thillai Rajan

2010-12-01

Full Text Available Venture Capital (VC has emerged as the dominant source of finance for entrepreneurial and early stage businesses, and the Indian VC industry in particular has clocked the fastest growth rate globally. Academic literature reveals that VC funded companies show superior performance to non VC funded companies. However, given that venture capitalists (VCs select and fund only the best companies, how much credit can they take for the performance of the companies they fund? Do the inherent characteristics of the firm result in superior performance or do VCs contribute to the performance of the portfolio company after they have entered the firm? A panel that comprised VCs, an entrepreneur and an academic debated these and other research questions on the inter-relationships between VC funding and portfolio firm performance. Most empirical literature indicates that the value addition effect dominates the selection effect in accounting for the superior performance of VC funded companies. The panel discussion indicates that the context as well as the experience of the General Partners in the VC firms can influence the way VCs contribute to the efficiency of their portfolio companies.

Analysis Of Dynamic Portfolio Allocation Of Indonesian LQ45 During 2005 – 2011 Following The Markowitz Theowry

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Agustini Hamid

2016-09-01

Full Text Available The research observed that equity portfolio and investment managers were facing challenges in determining the optimum portfolio, especially during the turbulent times. As a result, they needed to implement portfolio management strategies to overcome the risk associated with stock return volatility in turbulence periods. This research focused on selecting stocks from the LQ-45 index during 2005-2011 using The Markowitz theory combining the Solver Linear Programming. The portfolio selection method which has been introduced by Markowitz (1952 used variance or standard deviation as a risk measurement. The result of this research proves that the composition of the portfolio is not the same in the different period. In the bearish period, the composition of the optimum portfolio is dominated by the banking sector and manufacture sector. In the bullish period, the optimum portfolio is dominated by the commodity stocks.

The Impact of Transaction Costs on Rebalancing an Investment Portfolio in Portfolio Optimization

OpenAIRE

B. Marasović; S. Pivac; S. V. Vukasović

2015-01-01

Constructing a portfolio of investments is one of the most significant financial decisions facing individuals and institutions. In accordance with the modern portfolio theory maximization of return at minimal risk should be the investment goal of any successful investor. In addition, the costs incurred when setting up a new portfolio or rebalancing an existing portfolio must be included in any realistic analysis. In this paper rebalancing an investment portfolio in the pr...

Multiperiod Telser’s Safety-First Portfolio Selection with Regime Switching

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Chuangwei Lin

2018-01-01

Full Text Available This paper investigates a multiperiod Telser’s safety-first portfolio selection model with regime switching where the returns of the assets are assumed to depend on the market states modulated by a discrete-time Markov chain. The investor aims to maximize the expected terminal wealth and does not want the probability of the terminal wealth to fall short of a disaster level to exceed a predetermined number called the risk control level. Referring to Tchebycheff inequality, we modify Telser’s safety-first model to the case that aims to maximize the expected terminal wealth subject to a constraint where the upper bound of the disaster probability is less than the risk control level. By the Lagrange multiplier technique and the embedding method, we study in detail the existence of the optimal strategy and derive the closed-form optimal strategy. Finally, by mathematical and numerical analysis, we analyze the effects of the disaster level, the risk control level, the transition matrix of the Markov chain, the expected excess return, and the variance of the risky return.

Application of Markowitz Portfolio Theory by Building Optimal Portfolio on the US Stock Market

OpenAIRE

Širůček, Martin; Křen, Lukáš

2015-01-01

ŠIRŮČEK MARTIN, KŘEN LUKÁŠ. 2015. Application of Markowitz Portfolio Theory by Building Optimal Portfolio on the US Stock Market. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63(4): 1375–1386. This paper is focused on building investment portfolios by using the Markowitz Portfolio Theory (MPT). Derivation based on the Capital Asset Pricing Model (CAPM) is used to calculate the weights of individual securities in portfolios. The calculated portfolios include a po...

A case study of a multiobjective recombinative genetic algorithm with coevolutionary sharing

NARCIS (Netherlands)

Neef, R.M.; Thierens, D.; Arciszewski, H.F.R.

1999-01-01

We present a multiobjective genetic algorithm that incorporates various genetic algorithm techniques that have been proven to be efficient and robust in their problem domain. More specifically, we integrate rank based selection, adaptive niching through coevolutionary sharing, elitist recombination,

Leptokurtic portfolio theory

Science.gov (United States)

Kitt, R.; Kalda, J.

2006-03-01

The question of optimal portfolio is addressed. The conventional Markowitz portfolio optimisation is discussed and the shortcomings due to non-Gaussian security returns are outlined. A method is proposed to minimise the likelihood of extreme non-Gaussian drawdowns of the portfolio value. The theory is called Leptokurtic, because it minimises the effects from “fat tails” of returns. The leptokurtic portfolio theory provides an optimal portfolio for investors, who define their risk-aversion as unwillingness to experience sharp drawdowns in asset prices. Two types of risks in asset returns are defined: a fluctuation risk, that has Gaussian distribution, and a drawdown risk, that deals with distribution tails. These risks are quantitatively measured by defining the “noise kernel” — an ellipsoidal cloud of points in the space of asset returns. The size of the ellipse is controlled with the threshold parameter: the larger the threshold parameter, the larger return are accepted for investors as normal fluctuations. The return vectors falling into the kernel are used for calculation of fluctuation risk. Analogously, the data points falling outside the kernel are used for the calculation of drawdown risks. As a result the portfolio optimisation problem becomes three-dimensional: in addition to the return, there are two types of risks involved. Optimal portfolio for drawdown-averse investors is the portfolio minimising variance outside the noise kernel. The theory has been tested with MSCI North America, Europe and Pacific total return stock indices.

Concurrent credit portfolio losses.

Science.gov (United States)

Sicking, Joachim; Guhr, Thomas; Schäfer, Rudi

2018-01-01

We consider the problem of concurrent portfolio losses in two non-overlapping credit portfolios. In order to explore the full statistical dependence structure of such portfolio losses, we estimate their empirical pairwise copulas. Instead of a Gaussian dependence, we typically find a strong asymmetry in the copulas. Concurrent large portfolio losses are much more likely than small ones. Studying the dependences of these losses as a function of portfolio size, we moreover reveal that not only large portfolios of thousands of contracts, but also medium-sized and small ones with only a few dozens of contracts exhibit notable portfolio loss correlations. Anticipated idiosyncratic effects turn out to be negligible. These are troublesome insights not only for investors in structured fixed-income products, but particularly for the stability of the financial sector. JEL codes: C32, F34, G21, G32, H81.

Mean-Variance portfolio optimization when each asset has individual uncertain exit-time

Directory of Open Access Journals (Sweden)

Reza Keykhaei

2016-12-01

Full Text Available The standard Markowitz Mean-Variance optimization model is a single-period portfolio selection approach where the exit-time (or the time-horizon is deterministic. ‎In this paper we study the Mean-Variance portfolio selection problem ‎with ‎uncertain ‎exit-time ‎when ‎each ‎has ‎individual uncertain ‎xit-time‎, ‎which generalizes the Markowitz's model‎. ‎‎‎‎‎‎We provide some conditions under which the optimal portfolio of the generalized problem is independent of the exit-times distributions. Also, ‎‎it is shown that under some general circumstances, the sets of optimal portfolios‎ ‎in the generalized model and the standard model are the same‎.

Linear versus quadratic portfolio optimization model with transaction cost

Science.gov (United States)

Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah

2014-06-01

Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.

A Closed-Form Solution for Robust Portfolio Selection with Worst-Case CVaR Risk Measure

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Le Tang

2014-01-01

Full Text Available With the uncertainty probability distribution, we establish the worst-case CVaR (WCCVaR risk measure and discuss a robust portfolio selection problem with WCCVaR constraint. The explicit solution, instead of numerical solution, is found and two-fund separation is proved. The comparison of efficient frontier with mean-variance model is discussed and finally we give numerical comparison with VaR model and equally weighted strategy. The numerical findings indicate that the proposed WCCVaR model has relatively smaller risk and greater return and relatively higher accumulative wealth than VaR model and equally weighted strategy.

Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Najafi, Hamidreza; Najafi, Behzad [K. N. Toosi University of Technology, Department of Mechanical Engineering, Tehran (Iran)

2010-06-15

In the present paper, a plate and frame heat exchanger is considered. Multi-objective optimization using genetic algorithm is developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coefficient. Vividly, considered objective functions are conflicting and no single solution can satisfy both objectives simultaneously. Multi-objective optimization procedure yields a set of optimal solutions, called Pareto front, each of which is a trade-off between objectives and can be selected by the user, regarding the application and the project's limits. The presented work takes care of numerous geometric parameters in the presence of logical constraints. A sensitivity analysis is also carried out to study the effects of different geometric parameters on the considered objective functions. Modeling the system and implementing the multi-objective optimization via genetic algorithm has been performed by MATLAB. (orig.)

Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks

Directory of Open Access Journals (Sweden)

Foroogh Ghasemi

2018-05-01

Full Text Available An organization’s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio’s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause–effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause–effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio’s projects and making strategic decisions.

Optimal portfolio selection for cashflows with bounded capital at risk

NARCIS (Netherlands)

Vyncke, D.; Goovaerts, M.J.; Dhaene, J.L.M.; Vanduffel, S.

2005-01-01

We consider a continuous-time Markowitz type portfolio problem that consists of minimizing the discounted cost of a given cash-fl ow under the constraint of a restricted Capital at Risk. In a Black-Scholes setting, upper and lower bounds are obtained by means of simple analytical expressions that

Image de-noising based on mathematical morphology and multi-objective particle swarm optimization

Science.gov (United States)

Dou, Liyun; Xu, Dan; Chen, Hao; Liu, Yicheng

2017-07-01

To overcome the problem of image de-noising, an efficient image de-noising approach based on mathematical morphology and multi-objective particle swarm optimization (MOPSO) is proposed in this paper. Firstly, constructing a series and parallel compound morphology filter based on open-close (OC) operation and selecting a structural element with different sizes try best to eliminate all noise in a series link. Then, combining multi-objective particle swarm optimization (MOPSO) to solve the parameters setting of multiple structural element. Simulation result shows that our algorithm can achieve a superior performance compared with some traditional de-noising algorithm.

78 FR 21045 - Confirmation, Portfolio Reconciliation, Portfolio Compression, and Swap Trading Relationship...

Science.gov (United States)

2013-04-09

... COMMODITY FUTURES TRADING COMMISSION 17 CFR Part 23 RIN 3038-AC96 Confirmation, Portfolio Reconciliation, Portfolio Compression, and Swap Trading Relationship Documentation Requirements for Swap Dealers... CFTC published final rules setting forth requirements for swap confirmation, portfolio reconciliation...

A General Framework for Portfolio Theory. Part I: theory and various models

OpenAIRE

Maier-Paape, Stanislaus; Zhu, Qiji Jim

2017-01-01

Utility and risk are two often competing measurements on the investment success. We show that efficient trade-off between these two measurements for investment portfolios happens, in general, on a convex curve in the two dimensional space of utility and risk. This is a rather general pattern. The modern portfolio theory of Markowitz [H. Markowitz, Portfolio Selection, 1959] and its natural generalization, the capital market pricing model, [W. F. Sharpe, Mutual fund performance , 1966] are spe...

Multiobjective Optimization for Electronic Circuit Design in Time and Frequency Domains

Directory of Open Access Journals (Sweden)

J. Dobes

2013-04-01

Full Text Available The multiobjective optimization provides an extraordinary opportunity for the finest design of electronic circuits because it allows to mathematically balance contradictory requirements together with possible constraints. In this paper, an original and substantial improvement of an existing method for the multiobjective optimization known as GAM (Goal Attainment Method is suggested. In our proposal, the GAM algorithm itself is combined with a procedure that automatically provides a set of parameters -- weights, coordinates of the reference point -- for which the method generates noninferior solutions uniformly spread over an appropriately selected part of the Pareto front. Moreover, the resulting set of obtained solutions is then presented in a suitable graphic form so that the solution representing the most satisfactory tradeoff can be easily chosen by the designer. Our system generates various types of plots that conveniently characterize results of up to four-dimensional problems. Technically, the procedures of the multiobjective optimization were created as a software add-on to the CIA (Circuit Interactive Analyzer program. This way enabled us to utilize many powerful features of this program, including the sensitivity analyses in time and frequency domains. As a result, the system is also able to perform the multiobjective optimization in the time domain and even highly nonlinear circuits can be significantly improved by our program. As a demonstration of this feature, a multiobjective optimization of a C-class power amplifier in the time domain is thoroughly described in the paper. Further, a four-dimensional optimization of a video amplifier is demonstrated with an original graphic representation of the Pareto front, and also some comparison with the weighting method is done. As an example of improving noise properties, a multiobjective optimization of a low-noise amplifier is performed, and the results in the frequency domain are shown

Combinatorial Optimization in Project Selection Using Genetic Algorithm

Science.gov (United States)

Dewi, Sari; Sawaluddin

2018-01-01

This paper discusses the problem of project selection in the presence of two objective functions that maximize profit and minimize cost and the existence of some limitations is limited resources availability and time available so that there is need allocation of resources in each project. These resources are human resources, machine resources, raw material resources. This is treated as a consideration to not exceed the budget that has been determined. So that can be formulated mathematics for objective function (multi-objective) with boundaries that fulfilled. To assist the project selection process, a multi-objective combinatorial optimization approach is used to obtain an optimal solution for the selection of the right project. It then described a multi-objective method of genetic algorithm as one method of multi-objective combinatorial optimization approach to simplify the project selection process in a large scope.

A scalable coevolutionary multi-objective particle swarm optimizer

Directory of Open Access Journals (Sweden)

Xiangwei Zheng

2010-11-01

Full Text Available Multi-Objective Particle Swarm Optimizers (MOPSOs are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and ?-dominance based MOPSO (CEPSO is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs are decomposed in terms of their decision variables and are optimized by cooperative coevolutionary subswarms, and a uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share an external archive based on ?-dominance, which is also used as a leader set. Collaborators are selected from the archive and used to construct context vectors in order to evaluate particles in a subswarm. CEPSO is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems, but the number of function evaluations only increases linearly with respect to the number of decision variables. Therefore, CEPSO is competitive in solving various MOPs.

Optimisation of the securities portfolio as a part of the risk management process

Directory of Open Access Journals (Sweden)

Srečko Devjak

2004-01-01

Full Text Available Securities of Slovene companies are listed at the Ljubljana Stock Exchange. Market capitalisation at the Ljubljana Stock Exchange has been growing since 1996 due to new listings of equities. On the basis of financial data time series for listed equities, the financial investor can calculate a risk for each individual security with a selected risk measure and can determine an optimal portfolio, subject to selected constraints. In this paper, we shall consequently determine an optimal portfolio of equities for the financial investor, investing his assets only in selected equities listed at the Ljubljana Stock Exchange. Selecting an appropriate risk measure is especially important for a commercial bank in a risk management process. Commercial banks can use internal models in the risk management process and for the purpose of capital charges as well. An optimal portfolio will be calculated, using a non-linear mathematical model.

Program and Portfolio Tradeoffs Under Uncertainty Using Epoch-Era Analysis: A Case Application to Carrier Strike Group Design

Science.gov (United States)

2015-05-01

Modern Portfolio Theory (MPT) • Joint EEA and MPT Method... Modern Portfolio Theory (MPT) for Engineering Portfolios Consistencies • Value elicitation from stakeholders • Modeling of asset value • Founded...correlation • Asset availability is dynamic • Costs may accompany diversification Select elements of Modern Portfolio Theory can improve the design

Measuring Treasury Bond Portfolio Risk and Portfolio Optimization with a Non-Gaussian Multivariate Model

Science.gov (United States)

Dong, Yijun

The research about measuring the risk of a bond portfolio and the portfolio optimization was relatively rare previously, because the risk factors of bond portfolios are not very volatile. However, this condition has changed recently. The 2008 financial crisis brought high volatility to the risk factors and the related bond securities, even if the highly rated U.S. treasury bonds. Moreover, the risk factors of bond portfolios show properties of fat-tailness and asymmetry like risk factors of equity portfolios. Therefore, we need to use advanced techniques to measure and manage risk of bond portfolios. In our paper, we first apply autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model with multivariate normal tempered stable (MNTS) distribution innovations to predict risk factors of U.S. treasury bonds and statistically demonstrate that MNTS distribution has the ability to capture the properties of risk factors based on the goodness-of-fit tests. Then based on empirical evidence, we find that the VaR and AVaR estimated by assuming normal tempered stable distribution are more realistic and reliable than those estimated by assuming normal distribution, especially for the financial crisis period. Finally, we use the mean-risk portfolio optimization to minimize portfolios' potential risks. The empirical study indicates that the optimized bond portfolios have better risk-adjusted performances than the benchmark portfolios for some periods. Moreover, the optimized bond portfolios obtained by assuming normal tempered stable distribution have improved performances in comparison to the optimized bond portfolios obtained by assuming normal distribution.

Optimization of a dynamic supply portfolio considering risks and discount’s constraints

Directory of Open Access Journals (Sweden)

Masoud Rabbani

2014-01-01

Full Text Available Purpose: Nowadays finding reliable suppliers in the global supply chains has become so important for success, because reliable suppliers would lead to a reliable supply and besides that orders of customer are met effectively . Yet, there is little empirical evidence to support this view, hence the purpose of this paper is to fill this need by considering risk in order to find the optimum supply portfolio. Design/methodology/approach: This paper proposes a multi objective model for the supplier selection portfolio problem that uses conditional value at risk (CVaR criteria to control the risks of delayed, disrupted and defected supplies via scenario analysis. Also we consider discount’s constraints which are common assumptions in supplier selection problems. The proposed approach is capable of determining the optimal supply portfolio by calculating value-at-risk and minimizing conditional value-at-risk. In this study the Reservation Level driven Tchebycheff Procedure (RLTP which is one of the reference point methods, is used to solve small size of our model through coding in GAMS. As our model is NP-hard; a meta-heuristic approach, Non-dominated Sorting Genetic Algorithm (NSGA which is one of the most efficient methods for optimizing multi objective models, is applied to solve large scales of our model. Findings and Originality/value: In order to find a dynamic supply portfolio, we developed a Mixed Integer Linear Programming (MILP model which contains two objectives. One objective minimizes the cost and the other minimizes the risks of delayed, disrupted and defected supplies. CVaR is used as the risk controlling method which emphases on low-probability, high-consequence events. Discount option as a common offer from suppliers is also implanted in the proposed model. Our findings show that the proposed model can help in optimization of a dynamic supplier selection portfolio with controlling the corresponding risks for large scales of real word

Dynamic Portfolio Selection on Croatian Financial Markets: MGARCH Approach

OpenAIRE

Škrinjarić, Tihana; Šego, Boško

2016-01-01

Background: Investors on financial markets are interested in finding trading strategies which could enable them to beat the market. They always look for best possibilities to achieve above-average returns and manage risks successfully. MGARCH methodology (Multivariate Generalized Autoregressive Conditional Heteroskedasticity) makes it possible to model changing risks and return dynamics on financial markets on a daily basis. The results could be used in order to enhance portfolio formation an...

An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.

Science.gov (United States)

Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun

2017-09-01

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

Multiobjective optimization in bioinformatics and computational biology.

Science.gov (United States)

Handl, Julia; Kell, Douglas B; Knowles, Joshua

2007-01-01

This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique.

Hydrogen patent portfolios in the automotive industry - the search for promising storage methods

NARCIS (Netherlands)

Bakker, S.

2010-01-01

In the development of hydrogen vehicle technologies, the automotive industry adopts a portfolio approach; a multitude of technological options is developed for hydrogen storage and conversion. Patent portfolios of car manufacturers are used as indicators of the variation and selection dynamics of

Comprehensive Education Portfolio with a Career Focus

Science.gov (United States)

Kruger, Evonne J.; Holtzman, Diane M.; Dagavarian, Debra A.

2013-01-01

There are many types of student portfolios used within academia: the prior learning portfolio, credentialing portfolio, developmental portfolio, capstone portfolio, individual course portfolio, and the comprehensive education portfolio. The comprehensive education portfolio (CEP), as used by the authors, is a student portfolio, developed over…

Academic portfolio in the digital era: organizing and maintaining a portfolio using reference managers.

Science.gov (United States)

Bhargava, Puneet; Patel, Vatsal B; Iyer, Ramesh S; Moshiri, Mariam; Robinson, Tracy J; Lall, Chandana; Heller, Matthew T

2015-02-01

The academic portfolio has become an integral part of the promotions process. Creating and maintaining an academic portfolio in paper-based or web-based formats can be a cumbersome and time-consuming task. In this article, we describe an alternative way to efficiently organize an academic portfolio using a reference manager software, and discuss some of the afforded advantages. The reference manager software Papers (Mekentosj, Amsterdam, The Netherlands) was used to create an academic portfolio. The article outlines the key steps in creating and maintaining a digital academic portfolio. Using reference manager software (Papers), we created an academic portfolio that allows the user to digitally organize clinical, teaching, and research accomplishments in an indexed library enabling efficient updating, rapid retrieval, and easy sharing. To our knowledge, this is the first digital portfolio of its kind.

Portfolio evaluation of health programs: a reply to Sendi et al.

Science.gov (United States)

Bridges, John F P; Terris, Darcey D

2004-05-01

Sendi et al. (Soc. Sci. Med. 57 (2003) 2207) extend previous research on cost-effectiveness analysis to the evaluation of a portfolio of interventions with risky outcomes using a "second best" approach that can identify improvements in efficiency in the allocation of resources. This method, however, cannot be used to directly identify the optimal solution to the resource allocation problem. Theoretically, a stricter adherence to the foundations of portfolio theory would permit direct optimization in portfolio selection, however, when we include uncertainty in our analysis in addition to the traditional concept of risk (which is often mislabelled uncertainty) complexities are introduced that create significant hurdles in the development of practical applications of portfolio theory for health care policy decision making.

A Case Study of a Multiobjective Elitist Recombinative Genetic Algorithm with Coevolutionary Sharing

NARCIS (Netherlands)

Neef, R.M.; Thierens, D.; Arciszewski, H.F.R.

1999-01-01

We present a multiobjective genetic algorithm that incorporates various genetic algorithm techniques that have been proven to be efficient and robust in their problem domain. More specifically, we integrate rank based selection, adaptive niching through coevolutionary sharing, elitist recombination,

Portfolio langagier en francais (Language Portfolios in French).

Science.gov (United States)

Laplante, Bernard; Christiansen, Helen

2001-01-01

Suggests that first-year college students learning French should create a language portfolio that contains documents that illustrate what they have learned in French, along with a brief statement of what linguistic skill the document demonstrates. The goal of the portfolio is to make students more aware of their own learning, their strengths, and…

Multi-Objective Nonlinear Model Predictive Control: Lexicographic Method

OpenAIRE

Zheng, Tao; Wu, Gang; Liu, Guang-Hong; Ling, Qing

2010-01-01

In this chapter, to avoid the disadvantages of weight coefficients in multi-objective dynamic optimization, lexicographic (completely stratified) and partially stratified frameworks of multi-objective controller are proposed. The lexicographic framework is absolutely prioritydriven and the partially stratified framework is a modification of it, they both can solve the multi-objective control problem with the concept of priority for objective’s relative importance, while the latter one is mo...

Multi-objective convex programming problem arising in multivariate ...

African Journals Online (AJOL)

user

Multi-objective convex programming problem arising in ... However, although the consideration of multiple objectives may seem a novel concept, virtually any nontrivial ..... Solving multiobjective programming problems by discrete optimization.

A methodology for the valuation and selection of adaptable technology portfolios and its application to small and medium airports

Science.gov (United States)

Pinon, Olivia J.

The increase in the types of airspace users (large aircraft, small and regional jets, very light jets, unmanned aerial vehicles, etc.), as well as the very limited number of future new airport development projects are some of the factors that will characterize the next decades in air transportation. These factors, associated with a persistent growth in air traffic will worsen the current gridlock situation experienced at some major airports. As airports are becoming the major capacity bottleneck to continued growth in air traffic, it is therefore primordial to make the most efficient use of the current, and very often, underutilized airport infrastructure. This research thus proposes to address the increase in air traffic demand and resulting capacity issues by considering the implementation of operational concepts and technologies at underutilized airports. However, there are many challenges associated with sustaining the development of this type of airports. First, the need to synchronize evolving technologies with airports’ needs and investment capabilities is paramount. Additionally, it was observed that the evolution of secondary airports, and their needs, is tightly linked to the environment in which they operate. In particular, sensitivity of airports to changes in the dynamics of their environment is important, therefore requiring that the factors that drive the need for technology acquisition be identified and characterized. Finally, the difficulty to evaluate risk and make financially viable decisions, particularly when investing in new technologies, cannot be ignored. This research provides a methodology that addresses these challenges and ensures the sustainability of airport capacity-enhancement investments in a continuously changing environment. In particular, it is articulated around the need to provide decision makers with the capability to valuate and select adaptable technology portfolios to ensure airport financial viability. Hence, the four

Concordance measures and second order stochastic dominance-portfolio efficiency analysis

Czech Academy of Sciences Publication Activity Database

Kopa, Miloš; Tichý, T.

2012-01-01

Roč. 15, č. 4 (2012), s. 110-120 ISSN 1212-3609 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : dependency * concordance * portfolio selection * second order stochastic dominance Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.633, year: 2012 http://library.utia.cas.cz/separaty/2013/E/kopa-concordance measures and second order stochastic dominance- portfolio efficiency analysis.pdf

Multiobjective pressurized water reactor reload core design by nondominated genetic algorithm search

International Nuclear Information System (INIS)

Parks, G.T.

1996-01-01

The design of pressurized water reactor reload cores is not only a formidable optimization problem but also, in many instances, a multiobjective problem. A genetic algorithm (GA) designed to perform true multiobjective optimization on such problems is described. Genetic algorithms simulate natural evolution. They differ from most optimization techniques by searching from one group of solutions to another, rather than from one solution to another. New solutions are generated by breeding from existing solutions. By selecting better (in a multiobjective sense) solutions as parents more often, the population can be evolved to reveal the trade-off surface between the competing objectives. An example illustrating the effectiveness of this novel method is presented and analyzed. It is found that in solving a reload design problem the algorithm evaluates a similar number of loading patterns to other state-of-the-art methods, but in the process reveals much more information about the nature of the problem being solved. The actual computational cost incurred depends on the core simulator used; the GA itself is code independent

Do portfolios have a future?

Science.gov (United States)

Driessen, Erik

2017-03-01

While portfolios have seen an unprecedented surge in popularity, they have also become the subject of controversy: learners often perceive little gain from writing reflections as part of their portfolios; scholars question the ethics of such obligatory reflection; and students, residents, teachers and scholars alike condemn the bureaucracy surrounding portfolio implementation in competency-based education. It could be argued that mass adoption without careful attention to purpose and format may well jeopardize portfolios' viability in health sciences education. This paper explores this proposition by addressing the following three main questions: (1) Why do portfolios meet with such resistance from students and teachers, while educators love them?; (2) Is it ethical to require students to reflect and then grade their reflections?; (3) Does competency-based education empower or hamper the learner during workplace-based learning? Twenty-five years of portfolio reveal a clear story: without mentoring, portfolios have no future and are nothing short of bureaucratic hurdles in our competency-based education programs. Moreover, comprehensive portfolios, which are integrated into the curriculum and much more diverse in content than reflective portfolios, can serve as meaningful patient charts, providing doctor and patient with useful information to discuss well-being and treatment. In this sense, portfolios are also learner charts that comprehensively document progress in a learning trajectory which is lubricated by meaningful dialogue between learner and mentor in a trusting relationship to foster learning. If we are able to make such comprehensive and meaningful use of portfolios, then, yes, portfolios do have a bright future in medical education.

Using multiobjective tradeoff sets and Multivariate Regression Trees to identify critical and robust decisions for long term water utility planning

Science.gov (United States)

Smith, R.; Kasprzyk, J. R.; Balaji, R.

2017-12-01

In light of deeply uncertain factors like future climate change and population shifts, responsible resource management will require new types of information and strategies. For water utilities, this entails potential expansion and efficient management of water supply infrastructure systems for changes in overall supply; changes in frequency and severity of climate extremes such as droughts and floods; and variable demands, all while accounting for conflicting long and short term performance objectives. Multiobjective Evolutionary Algorithms (MOEAs) are emerging decision support tools that have been used by researchers and, more recently, water utilities to efficiently generate and evaluate thousands of planning portfolios. The tradeoffs between conflicting objectives are explored in an automated way to produce (often large) suites of portfolios that strike different balances of performance. Once generated, the sets of optimized portfolios are used to support relatively subjective assertions of priorities and human reasoning, leading to adoption of a plan. These large tradeoff sets contain information about complex relationships between decisions and between groups of decisions and performance that, until now, has not been quantitatively described. We present a novel use of Multivariate Regression Trees (MRTs) to analyze tradeoff sets to reveal these relationships and critical decisions. Additionally, when MRTs are applied to tradeoff sets developed for different realizations of an uncertain future, they can identify decisions that are robust across a wide range of conditions and produce fundamental insights about the system being optimized.

A multi-objective optimization approach for the selection of working fluids of geothermal facilities: Economic, environmental and social aspects.

Science.gov (United States)

Martínez-Gomez, Juan; Peña-Lamas, Javier; Martín, Mariano; Ponce-Ortega, José María

2017-12-01

The selection of the working fluid for Organic Rankine Cycles has traditionally been addressed from systematic heuristic methods, which perform a characterization and prior selection considering mainly one objective, thus avoiding a selection considering simultaneously the objectives related to sustainability and safety. The objective of this work is to propose a methodology for the optimal selection of the working fluid for Organic Rankine Cycles. The model is presented as a multi-objective approach, which simultaneously considers the economic, environmental and safety aspects. The economic objective function considers the profit obtained by selling the energy produced. Safety was evaluated in terms of individual risk for each of the components of the Organic Rankine Cycles and it was formulated as a function of the operating conditions and hazardous properties of each working fluid. The environmental function is based on carbon dioxide emissions, considering carbon dioxide mitigation, emission due to the use of cooling water as well emissions due material release. The methodology was applied to the case of geothermal facilities to select the optimal working fluid although it can be extended to waste heat recovery. The results show that the hydrocarbons represent better solutions, thus among a list of 24 working fluids, toluene is selected as the best fluid. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investigation on multi-objective performance optimization algorithm application of fan based on response surface method and entropy method

Science.gov (United States)

Zhang, Li; Wu, Kexin; Liu, Yang

2017-12-01

A multi-objective performance optimization method is proposed, and the problem that single structural parameters of small fan balance the optimization between the static characteristics and the aerodynamic noise is solved. In this method, three structural parameters are selected as the optimization variables. Besides, the static pressure efficiency and the aerodynamic noise of the fan are regarded as the multi-objective performance. Furthermore, the response surface method and the entropy method are used to establish the optimization function between the optimization variables and the multi-objective performances. Finally, the optimized model is found when the optimization function reaches its maximum value. Experimental data shows that the optimized model not only enhances the static characteristics of the fan but also obviously reduces the noise. The results of the study will provide some reference for the optimization of multi-objective performance of other types of rotating machinery.

Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation

International Nuclear Information System (INIS)

Zio, E.; Bazzo, R.

2011-01-01

Reliability-based and risk-informed design, operation, maintenance and regulation lead to multiobjective (multicriteria) optimization problems. In this context, the Pareto Front and Set found in a multiobjective optimality search provide a family of solutions among which the decision maker has to look for the best choice according to his or her preferences. Efficient visualization techniques for Pareto Front and Set analyses are needed for helping decision makers in the selection task. In this paper, we consider the multiobjective optimization of system redundancy allocation and use the recently introduced Level Diagrams technique for graphically representing the resulting Pareto Front and Set. Each objective and decision variable is represented on separate diagrams where the points of the Pareto Front and Set are positioned according to their proximity to ideally optimal points, as measured by a metric of normalized objective values. All diagrams are synchronized across all objectives and decision variables. On the basis of the analysis of the Level Diagrams, we introduce a procedure for reducing the number of solutions in the Pareto Front; from the reduced set of solutions, the decision maker can more easily identify his or her preferred solution.

Development of a computer code system for selecting off-site protective action in radiological accidents based on the multiobjective optimization method

International Nuclear Information System (INIS)

Ishigami, Tsutomu; Oyama, Kazuo

1989-09-01

This report presents a new method to support selection of off-site protective action in nuclear reactor accidents, and provides a user's manual of a computer code system, PRASMA, developed using the method. The PRASMA code system gives several candidates of protective action zones of evacuation, sheltering and no action based on the multiobjective optimization method, which requires objective functions and decision variables. We have assigned population risks of fatality, injury and cost as the objective functions, and distance from a nuclear power plant characterizing the above three protective action zones as the decision variables. (author)

Mitigating Pollution of Hazardous Materials from WEEE of China: Portfolio Selection for a sustainable future based on Multi-Criteria Decision Making

DEFF Research Database (Denmark)

An, Da; Yang, Yu; Chai, Xilong

2015-01-01

In order to solve the environmental contaminations and human health problems caused by the inappropriate treatment of waste electrical and electronic equipment (WEEE) in China, sustainable e-waste treatment has emerged in China's WEEE recycling industry. This study aims to develop a multi......-criteria decision making method by integrating interval Analytic Hierarchy Process and interval VIKOR method for China's stakeholders to select the most efficacious portfolio for solving the severe problems caused by the informal e-waste recycling and promote the development of China's WEEE recycling industry...... in a sustainable approach. An illustrative case in Guiyu has been studied by the developed method, and the results show that the portfolio of supporting the informal peddlers for legal transition, investing on infrastructure for WEEE recycling, training and education on China's residents, and restricting...

Teacher Portfolios.

Science.gov (United States)

Wolfe-Quintero, Kate; Brown, James Dean

1998-01-01

A portfolio of achievements, experiences, and reflections can help English-as-a-Second-Language teachers attain professional development goals and offer administrators greater insight for making informed hiring and job-performance decisions. This paper focuses on what teacher portfolios are, what their contents should be, and what their uses are…

Adaptive Gradient Multiobjective Particle Swarm Optimization.

Science.gov (United States)

Han, Honggui; Lu, Wei; Zhang, Lu; Qiao, Junfei

2017-10-09

An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance in this paper. In this AGMOPSO algorithm, the stocktickerMOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Meanwhile, the self-adaptive flight parameters mechanism, according to the diversity information of the particles, is then established to balance the convergence and diversity of AGMOPSO. Attributed to the stocktickerMOG method and the self-adaptive flight parameters mechanism, this AGMOPSO algorithm not only has faster convergence speed and higher accuracy, but also its solutions have better diversity. Additionally, the convergence is discussed to confirm the prerequisite of any successful application of AGMOPSO. Finally, with regard to the computation performance, the proposed AGMOPSO algorithm is compared with some other multiobjective particle swarm optimization algorithms and two state-of-the-art multiobjective algorithms. The results demonstrate that the proposed AGMOPSO algorithm can find better spread of solutions and have faster convergence to the true Pareto-optimal front.

Dynamic Portfolio Selection on Croatian Financial Markets: MGARCH Approach

Directory of Open Access Journals (Sweden)

Škrinjarić Tihana

2016-09-01

Full Text Available Background: Investors on financial markets are interested in finding trading strategies which could enable them to beat the market. They always look for best possibilities to achieve above-average returns and manage risks successfully. MGARCH methodology (Multivariate Generalized Autoregressive Conditional Heteroskedasticity makes it possible to model changing risks and return dynamics on financial markets on a daily basis. The results could be used in order to enhance portfolio formation and restructuring over time.

Multiobjective Optimization Involving Quadratic Functions

Directory of Open Access Journals (Sweden)

Oscar Brito Augusto

2014-01-01

Full Text Available Multiobjective optimization is nowadays a word of order in engineering projects. Although the idea involved is simple, the implementation of any procedure to solve a general problem is not an easy task. Evolutionary algorithms are widespread as a satisfactory technique to find a candidate set for the solution. Usually they supply a discrete picture of the Pareto front even if this front is continuous. In this paper we propose three methods for solving unconstrained multiobjective optimization problems involving quadratic functions. In the first, for biobjective optimization defined in the bidimensional space, a continuous Pareto set is found analytically. In the second, applicable to multiobjective optimization, a condition test is proposed to check if a point in the decision space is Pareto optimum or not and, in the third, with functions defined in n-dimensional space, a direct noniterative algorithm is proposed to find the Pareto set. Simple problems highlight the suitability of the proposed methods.

Multi-objective genetic algorithm for solving N-version program design problem

Energy Technology Data Exchange (ETDEWEB)

Yamachi, Hidemi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan) and Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamachi@nit.ac.jp; Tsujimura, Yasuhiro [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: tujimr@nit.ac.jp; Kambayashi, Yasushi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: yasushi@nit.ac.jp; Yamamoto, Hisashi [Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamamoto@cc.tmit.ac.jp

2006-09-15

N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.

Multi-objective genetic algorithm for solving N-version program design problem

International Nuclear Information System (INIS)

Yamachi, Hidemi; Tsujimura, Yasuhiro; Kambayashi, Yasushi; Yamamoto, Hisashi

2006-01-01

N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost

Decentralized portfolio management

OpenAIRE

Coutinho, Paulo; Tabak, Benjamin Miranda

2003-01-01

We use a mean-variance model to analyze the problem of decentralized portfolio management. We find the solution for the optimal portfolio allocation for a head trader operating in n different markets, which is called the optimal centralized portfolio. However, as there are many traders specialized in different markets, the solution to the problem of optimal decentralized allocation should be different from the centralized case. In this paper we derive conditions for the solutions to be equiva...

The Experimental Study on.the Risk Convergence of the Entrusted Portfolios of NSSF

Institute of Scientific and Technical Information of China (English)

QU Bao-zhong; PANG Qing

2008-01-01

This paper makes use of statistical tools of parameter correlation,multi-parameter regression,and does experimental analysis on issues of risk diversification of portfolios entrusted by National Social Security Fund (NSSF).The issues are industry related investment fieIds distribution,the trend of capitalization movement,and investment style factors in stock selection.The results show that there are risk problems with portfolios entrusted by NSSF,which include similar investment fields distribution trend,little difference among portfolios,and high risk preference degree.

The standard for portfolio management

CERN Document Server

2017-01-01

The Standard for Portfolio Management – Fourth Edition has been updated to best reflect the current state of portfolio management. It describe the principles that drive accepted good portfolio management practices in today’s organizations. It also expands the description of portfolio management to reflect its relation to organizational project management and the organization.

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

DEFF Research Database (Denmark)

Zhang, Chunyu; Ding, Yi; Wu, Qiuwei

2013-01-01

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...

A Maximum Entropy Method for a Robust Portfolio Problem

Directory of Open Access Journals (Sweden)

Yingying Xu

2014-06-01

Full Text Available We propose a continuous maximum entropy method to investigate the robustoptimal portfolio selection problem for the market with transaction costs and dividends.This robust model aims to maximize the worst-case portfolio return in the case that allof asset returns lie within some prescribed intervals. A numerical optimal solution tothe problem is obtained by using a continuous maximum entropy method. Furthermore,some numerical experiments indicate that the robust model in this paper can result in betterportfolio performance than a classical mean-variance model.

Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios

Directory of Open Access Journals (Sweden)

Neslihan Fidan Keçeci

2016-10-01

Full Text Available The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG package, which has precoded modules for optimization with SSD constraints, mean-variance and minimum variance portfolio optimization. We have done in-sample and out-of-sample simulations for portfolios of stocks from the Dow Jones, S&P 100 and DAX indices. The considered portfolios’ SSD dominate the Dow Jones, S&P 100 and DAX indices. Simulation demonstrated a superior performance of portfolios with SD constraints, versus mean-variance and minimum variance portfolios.

Portfolio Effects of Renewable Energies - Basics, Models, Exemplary Results

Energy Technology Data Exchange (ETDEWEB)

Wiese, Andreas; Herrmann, Matthias

2007-07-01

The combination of sites and technologies to so-called renewable energy portfolios, which are being developed and implemented under the same financing umbrella, is currently the subject of intense discussion in the finance world. The resulting portfolio effect may allow the prediction of a higher return with the same risk or the same return with a lower risk - always in comparison with the investment in a single project. Models are currently being developed to analyse this subject and derive the portfolio effect. In particular, the effect of the spatial distribution, as well as the effects of using different technologies, suppliers and cost assumptions with different level of uncertainties, are of importance. Wind parks, photovoltaic, biomass, biogas and hydropower are being considered. The status of the model development and first results are being presented in the current paper. In a first example, the portfolio effect has been calculated and analysed using selected parameters for a wind energy portfolio of 39 sites distributed over Europe. Consequently it has been shown that the predicted yield, with the predetermined probabilities between 75 to 90%, is 3 - 8% higher than the sum of the yields for the individual wind parks using the same probabilities. (auth)

Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies

International Nuclear Information System (INIS)

Safari, Jalal

2012-01-01

This paper proposes a variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP). Most researchers about redundancy allocation problem (RAP) have focused on single objective optimization, while there has been some limited research which addresses multi-objective optimization. Also all mathematical multi-objective models of general RAP assume that the type of redundancy strategy for each subsystem is predetermined and known a priori. In general, active redundancy has traditionally received greater attention; however, in practice both active and cold-standby redundancies may be used within a particular system design. The choice of redundancy strategy then becomes an additional decision variable. Thus, the proposed model and solution method are to select the best redundancy strategy, type of components, and levels of redundancy for each subsystem that maximizes the system reliability and minimize total system cost under system-level constraints. This problem belongs to the NP-hard class. This paper presents a second-generation Multiple-Objective Evolutionary Algorithm (MOEA), named NSGA-II to find the best solution for the given problem. The proposed algorithm demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker (DM) with a complete picture of the optimal solution space. After finding the Pareto front, a procedure is used to select the best solution from the Pareto front. Finally, the advantages of the presented multi-objective model and of the proposed algorithm are illustrated by solving test problems taken from the literature and the robustness of the proposed NSGA-II is discussed.

Power magnetic devices a multi-objective design approach

CERN Document Server

Sudhoff, Scott D

2014-01-01

Presents a multi-objective design approach to the many power magnetic devices in use today Power Magnetic Devices: A Multi-Objective Design Approach addresses the design of power magnetic devices-including inductors, transformers, electromagnets, and rotating electric machinery-using a structured design approach based on formal single- and multi-objective optimization. The book opens with a discussion of evolutionary-computing-based optimization. Magnetic analysis techniques useful to the design of all the devices considered in the book are then set forth. This material is then used for ind

e-Portfolios Enhancing Students' Self-Directed Learning: A Systematic Review of Influencing Factors

Science.gov (United States)

Beckers, Jorrick; Dolmans, Diana; Van Merriënboer, Jeroen

2016-01-01

e-Portfolios have become increasingly popular among educators as learning tools. Some research even shows that e-portfolios can be utilised to facilitate the development of skills for self-directed learning. Such skills include self-assessment of performance, formulation of learning goals, and selection of future tasks. However, it is not yet…

Post-modern portfolio theory supports diversification in an investment portfolio to measure investment's performance

OpenAIRE

Rasiah, Devinaga

2012-01-01

This study looks at the Post-Modern Portfolio Theory that maintains greater diversification in an investment portfolio by using the alpha and the beta coefficient to measure investment performance. Post-Modern Portfolio Theory appreciates that investment risk should be tied to each investor's goals and the outcome of this goal did not symbolize economic of the financial risk. Post-Modern Portfolio Theory's downside measure generated a noticeable distinction between downside and upside volatil...

Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic

DEFF Research Database (Denmark)

Govindan, Kannan; Jafarian, Ahmad; Nourbakhsh, Vahid

2015-01-01

simultaneously considering the sustainable OAP in the sustainable SCND as a strategic decision. The proposed supply chain network is composed of five echelons including suppliers classified in different classes, plants, distribution centers that dispatch products via two different ways, direct shipment......, a novel multi-objective hybrid approach called MOHEV with two strategies for its best particle selection procedure (BPSP), minimum distance, and crowding distance is proposed. MOHEV is constructed through hybridization of two multi-objective algorithms, namely the adapted multi-objective electromagnetism...

Utility portfolio diversification

International Nuclear Information System (INIS)

Griffes, P.H.

1990-01-01

This paper discusses portfolio analysis as a method to evaluate utility supply decisions. Specifically a utility is assumed to increase the value of its portfolio of assets whenever it invests in a new supply technology. This increase in value occurs because the new asset either enhances the return or diversifies the risks of the firm's portfolio of assets. This evaluation method is applied to two supply innovations in the electric utility industry: jointly-owned generating plants and supply contracts with independent power producers (IPPs)

Controller tuning with evolutionary multiobjective optimization a holistic multiobjective optimization design procedure

CERN Document Server

Reynoso Meza, Gilberto; Sanchis Saez, Javier; Herrero Durá, Juan Manuel

2017-01-01

This book is devoted to Multiobjective Optimization Design (MOOD) procedures for controller tuning applications, by means of Evolutionary Multiobjective Optimization (EMO). It presents developments in tools, procedures and guidelines to facilitate this process, covering the three fundamental steps in the procedure: problem definition, optimization and decision-making. The book is divided into four parts. The first part, Fundamentals, focuses on the necessary theoretical background and provides specific tools for practitioners. The second part, Basics, examines a range of basic examples regarding the MOOD procedure for controller tuning, while the third part, Benchmarking, demonstrates how the MOOD procedure can be employed in several control engineering problems. The fourth part, Applications, is dedicated to implementing the MOOD procedure for controller tuning in real processes.

Cointegration and Causality Analysis on Developed Asian Markets for Risk Management and Portfolio Selection

Directory of Open Access Journals (Sweden)

Aldrin Herwany

2008-09-01

This study assesses the cointegration and causal relations among seven developed Asian markets, i.e., Tokyo, Hong Kong, Korea, Taiwan, Shanghai, Singapore, and Kuala Lumpur stock exchanges, using more frequent time series data. It employs the recently developed techniques for investigating unit roots, cointegration, time-varying volatility, and causality in variance. For estimating portfolio market risk, this study employs Value-at-Risk with delta normal approach. The results would recommend whether fund managers are able to diversify their portfolio in these developed stock markets either in long run or in short run.

Multiobjective optimization of a steering linkage

Energy Technology Data Exchange (ETDEWEB)

Sleesonsom, S.; Bureerat, S. [Sustainable and Infrastructure Research and Development Center, Dept. of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen (Thailand)

2016-08-15

In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rack-and-pinion steering linkages satisfying both steering error and turning radius criteria.

Multiobjective optimization of a steering linkage

International Nuclear Information System (INIS)

Sleesonsom, S.; Bureerat, S.

2016-01-01

In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rack-and-pinion steering linkages satisfying both steering error and turning radius criteria

Teaching Portfolio

DEFF Research Database (Denmark)

Pedersen, Christian Fischer

The present teaching portfolio has been submitted for evaluation in partial fulfillment of the requirements of the teacher training programme for Assistant Professors at Department of Engineering, Aarhus University, Denmark.......The present teaching portfolio has been submitted for evaluation in partial fulfillment of the requirements of the teacher training programme for Assistant Professors at Department of Engineering, Aarhus University, Denmark....

MOONS: a multi-object optical and near-infrared spectrograph for the VLT

NARCIS (Netherlands)

Cirasuolo, M.; Afonso, J.; Bender, R.; Bonifacio, P.; Evans, C.; Kaper, L.; Oliva, Ernesto; Vanzi, Leonardo; Abreu, Manuel; Atad-Ettedgui, Eli; Babusiaux, Carine; Bauer, Franz E.; Best, Philip; Bezawada, Naidu; Bryson, Ian R.; Cabral, Alexandre; Caputi, Karina; Centrone, Mauro; Chemla, Fanny; Cimatti, Andrea; Cioni, Maria-Rosa; Clementini, Gisella; Coelho, João.; Daddi, Emanuele; Dunlop, James S.; Feltzing, Sofia; Ferguson, Annette; Flores, Hector; Fontana, Adriano; Fynbo, Johan; Garilli, Bianca; Glauser, Adrian M.; Guinouard, Isabelle; Hammer, Jean-François; Hastings, Peter R.; Hess, Hans-Joachim; Ivison, Rob J.; Jagourel, Pascal; Jarvis, Matt; Kauffman, G.; Lawrence, A.; Lee, D.; Li Causi, G.; Lilly, S.; Lorenzetti, D.; Maiolino, R.; Mannucci, F.; McLure, R.; Minniti, D.; Montgomery, D.; Muschielok, B.; Nandra, K.; Navarro, R.; Norberg, P.; Origlia, L.; Padilla, N.; Peacock, J.; Pedicini, F.; Pentericci, L.; Pragt, J.; Puech, M.; Randich, S.; Renzini, A.; Ryde, N.; Rodrigues, M.; Royer, F.; Saglia, R.; Sánchez, A.; Schnetler, H.; Sobral, D.; Speziali, R.; Todd, S.; Tolstoy, E.; Torres, M.; Venema, L.; Vitali, F.; Wegner, M.; Wells, M.; Wild, V.; Wright, G.

MOONS is a new conceptual design for a Multi-Object Optical and Near-infrared Spectrograph for the Very Large Telescope (VLT), selected by ESO for a Phase A study. The baseline design consists of ~1000 fibers deployable over a field of view of ~500 square arcmin, the largest patrol field offered by

Optimal portfolio selection in a Lévy market with uncontrolled cash flow and only risky assets

Science.gov (United States)

Zeng, Yan; Li, Zhongfei; Wu, Huiling

2013-03-01

This article considers an investor who has an exogenous cash flow evolving according to a Lévy process and invests in a financial market consisting of only risky assets, whose prices are governed by exponential Lévy processes. Two continuous-time portfolio selection problems are studied for the investor. One is a benchmark problem, and the other is a mean-variance problem. The first problem is solved by adopting the stochastic dynamic programming approach, and the obtained results are extended to the second problem by employing the duality theory. Closed-form solutions of these two problems are derived. Some existing results are found to be special cases of our results.

Portfolio optimization with mean-variance model

Science.gov (United States)

Hoe, Lam Weng; Siew, Lam Weng

2016-06-01

Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.

The Use of Academic Portfolio in the Learning and Assessment of Physics Students

Directory of Open Access Journals (Sweden)

Meng Kay Ling

2016-05-01

Full Text Available The purpose of this research paper is to examine the use of portfolios in the teaching and learning of physics at a Singapore private college. The paper starts with a short introduction of the types of students and the purpose of using academic portfolios in their learning and assessment. Some ideas of how portfolios can be used in the local context will also be discussed. It is necessary for teachers to know how to incorporate portfolio assessment in their daily lesson plans. At the same time, students who are studying physics at the college should also know how to use portfolios to their academic advantage. The paper also highlights three of the relevant work artifacts that can be included into the physics portfolios. The three work samples are concept-maps, internet research reports and newspaper articles reports. Concept-maps are useful tools to help students establish the connections between concepts. Internet research reports serve as important means for students to know more about how some scientific devices or technology use physics in the operations. Newspaper articles reports allow students to understand the real impact of physics on the lives of people. Subsequent sections of the paper discuss about the organizational flow of the portfolio, the timeline, the selection process, the portfolio checklist and assessment rubrics, the positive influences of using portfolios, the issues to consider and also the potential problems that physics teachers may face in implementing portfolios. These sections present the important framework which teachers can use as references for their portfolio initiatives in schools.

Lost opportunities and future avenues to reconcile hydropower and sediment transport in the Mekong Basin through optimal sequencing of dam portfolios.

Science.gov (United States)

Castelletti, A.; Schmitt, R. J. P.; Bizzi, S.; Kondolf, G. M.

2017-12-01

Dams are essential to meet growing water and energy demands. While dams cumulatively impact downstream rivers on network-scales, dam development is mostly based on ad-hoc economic and environmental assessments of single dams. Here, we provide evidence that replacing this ad-hoc approach with early strategic planning of entire dam portfolios can greatly reduce conflicts between economic and environmental objectives of dams. In the Mekong Basin (800,000km2), 123 major dam sites (status-quo: 56 built and under construction) could generate 280,000 GWh/yr of hydropower. Cumulatively, dams risk interrupting the basin's sediment dynamics with severe impacts on livelihoods and eco-systems. To evaluate cumulative impacts and benefits of the ad-hoc planned status-quo portfolio, we combine the CASCADE sediment connectivity model with data on hydropower production and sediment trapping at each dam site. We couple CASCADE to a multi-objective genetic algorithm (BORG) identifying a) portfolios resulting in an optimal trade-off between cumulative sediment trapping and hydropower production and b) an optimal development sequence for each portfolio. We perform this analysis first for the pristine basin (i.e., without pre-existing dams) and then starting from the status-quo portfolio, deriving policy recommendations for which dams should be prioritized in the near future. The status-quo portfolio creates a sub-optimal trade-off between hydropower and sediment trapping, exploiting 50 % of the basin's hydro-electric potential and trapping 60 % of the sediment load. Alternative optimal portfolios could have produced equivalent hydropower for 30 % sediment trapping. Imminent development of mega-dams in the lower basin will increase hydropower production by 20 % but increase sediment trapping to >90 %. In contrast, following an optimal development sequence can still increase hydropower by 30 % with limited additional sediment trapping by prioritizing dams in upper parts of the basin. Our

Households' portfolio choices

NARCIS (Netherlands)

Hochgürtel, S.

1998-01-01

This thesis presents four topics on households' portfolio choices. Empirically, households do not hold well-diversified wealth portfolios. In particular, they refrain from putting their savings into risky assets. We explore several ways that might help explaining this observation. Using Dutch

MULTI-OBJECTIVE OPTIMISATION OF LASER CUTTING USING CUCKOO SEARCH ALGORITHM

Directory of Open Access Journals (Sweden)

M. MADIĆ

2015-03-01

Full Text Available Determining of optimal laser cutting conditions for improving cut quality characteristics is of great importance in process planning. This paper presents multi-objective optimisation of the CO2 laser cutting process considering three cut quality characteristics such as surface roughness, heat affected zone (HAZ and kerf width. It combines an experimental design by using Taguchi’s method, modelling the relationships between the laser cutting factors (laser power, cutting speed, assist gas pressure and focus position and cut quality characteristics by artificial neural networks (ANNs, formulation of the multiobjective optimisation problem using weighting sum method, and solving it by the novel meta-heuristic cuckoo search algorithm (CSA. The objective is to obtain optimal cutting conditions dependent on the importance order of the cut quality characteristics for each of four different case studies presented in this paper. The case studies considered in this study are: minimisation of cut quality characteristics with equal priority, minimisation of cut quality characteristics with priority given to surface roughness, minimisation of cut quality characteristics with priority given to HAZ, and minimisation of cut quality characteristics with priority given to kerf width. The results indicate that the applied CSA for solving the multi-objective optimisation problem is effective, and that the proposed approach can be used for selecting the optimal laser cutting factors for specific production requirements.

Methodical approaches to assessment of quality of the bank loan portfolio

Directory of Open Access Journals (Sweden)

Tysyachna Yunna S.

2014-01-01

Full Text Available The goal of the article lies in the study of basic methodical approaches to assessment of the quality of the bank loan portfolio, identification of specific features of their practical application and justification of selection of the most appropriate for the modern economic conditions. The article considers three main groups of methods of assessment of the quality of the bank loan portfolio: expert evaluation methods and statistical and analytical methods. It goes without saying that in order to obtain an objective assessment of quality of the bank loan portfolio it is necessary to apply a complex approach, however, due to some advantages and shortcomings of the studied methods, the author marks expediency of building an integral indicator, taxonomic in particular, in order to obtain a complex, objective and efficient assessment of the bank loan portfolio. Prospects of further studies in this direction are assessment of the quality of the loan portfolio of the first group banks by the size of their assets through building integral taxonomic indicators and identification, on this basis, of factors that influence the quality of the loan portfolio with the aim of improvement of the mechanism of management of the bank lending activity.

Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Mengjun Ming

2017-05-01

Full Text Available Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES in both isolated-island and grid-connected modes. In each mode, the optimal design aims to find suitable configurations of photovoltaic (PV panels, wind turbines, batteries and diesel generators in HRES such that the system cost and the fuel emission are minimized, and the system reliability/renewable ability (corresponding to different modes is maximized. To effectively solve this multi-objective problem (MOP, the multi-objective evolutionary algorithm based on decomposition (MOEA/D using localized penalty-based boundary intersection (LPBI method is proposed. The algorithm denoted as MOEA/D-LPBI is demonstrated to outperform its competitors on the HRES model as well as a set of benchmarks. Moreover, it effectively obtains a good approximation of Pareto optimal HRES configurations. By further considering a decision maker’s preference, the most satisfied configuration of the HRES can be identified.

Evaluation of an established learning portfolio.

Science.gov (United States)

Vance, Gillian; Williamson, Alyson; Frearson, Richard; O'Connor, Nicole; Davison, John; Steele, Craig; Burford, Bryan

2013-02-01

The trainee-held learning portfolio is integral to the foundation programme in the UK. In the Northern Deanery, portfolio assessment is standardised through the Annual Review of Competence Progression (ARCP) process. In this study we aimed to establish how current trainees evaluate portfolio-based learning and ARCP, and how these attitudes may have changed since the foundation programme was first introduced. Deanery-wide trainee attitudes were surveyed by an electronic questionnaire in 2009 and compared with perceptions recorded during the pilot phase (2004-2005).  Many trainees continue to view the e-portfolio negatively. Indeed, significantly fewer trainees in 2009 thought that the e-portfolio was a 'good idea' or a 'worthwhile investment of time' than in 2005. Trainees remain unconvinced about the educational value of the e-portfolio: fewer trainees in 2009 regarded it as a tool that might help focus on training or recognise individual strengths and weaknesses. Issues around unnecessary bureaucracy persist. Current trainees tend to understand how to use the e-portfolio, but many did not know how much, or what evidence to collect. Few supervisors were reported to provide useful guidance on the portfolio. ARCP encouraged portfolio completion but did not give meaningful feedback to drive future learning.   Continued support is needed for both trainees and supervisors in portfolio-building skills and in using the e-portfolio as an educational tool. Trainee-tailored feedback is needed to ensure that portfolio-based assessment promotes lifelong, self-directed and reflective learners. © Blackwell Publishing Ltd 2013.

Genetic algorithms and fuzzy multiobjective optimization

CERN Document Server

Sakawa, Masatoshi

2002-01-01

Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...

Economic Analysis on Value Chain of Taxi Fleet with Battery-Swapping Mode Using Multiobjective Genetic Algorithm

Directory of Open Access Journals (Sweden)

Guobao Ning

2012-01-01

Full Text Available This paper presents an economic analysis model on value chain of taxi fleet with battery-swapping mode in a pilot city. In the model, economic benefits of charging-swapping station group, taxi company, and taxi driver in the region have been taken into consideration. Thus, the model is a multiobjective function and multiobjective genetic algorithm is used to solve this problem. According to the real data collected from the pilot city, the multiobjective genetic algorithm is tested as an effective method to solve this problem. Furthermore, the effects of price of electricity, price of battery package, life cycle of battery package, cost of battery-swapping devices and infrastructure, and driving mileage per day on the benefits of value holders are analyzed, which provide theoretical and practical reference for the deployment of electric vehicles, for the national subsidy criteria adjusment, technological innovation instruction, commercial mode selection, and infrastructure construction.

Multi-Period Mean-Variance Portfolio Selection with Uncertain Time Horizon When Returns Are Serially Correlated

Directory of Open Access Journals (Sweden)

Ling Zhang

2012-01-01

Full Text Available We study a multi-period mean-variance portfolio selection problem with an uncertain time horizon and serial correlations. Firstly, we embed the nonseparable multi-period optimization problem into a separable quadratic optimization problem with uncertain exit time by employing the embedding technique of Li and Ng (2000. Then we convert the later into an optimization problem with deterministic exit time. Finally, using the dynamic programming approach, we explicitly derive the optimal strategy and the efficient frontier for the dynamic mean-variance optimization problem. A numerical example with AR(1 return process is also presented, which shows that both the uncertainty of exit time and the serial correlations of returns have significant impacts on the optimal strategy and the efficient frontier.

Making sense with ePortfolios

DEFF Research Database (Denmark)

Poulsen, Bo Klindt; Dimsits, Miriam

2017-01-01

of the statements from the students concerning their understanding of ePortfolio processes are fundamentally questions of how to make sense of the ePortfolio tool, both in their professional and personal lives. This calls for a didactical stance with the teachers who use ePortfolios, based on empowerment through......This article discusses the question of making sense out of working with ePortfolio in adult education. The article presents the results of a small-scale survey among adults in continuing education who have worked with ePortfolio as the central didactic principle. It is argued that many...... meaning-making, in order for ePortfolios to make sense. It is suggested that two relevant didactic perspectives for making sense of the world can be found in theories of biographicity and metaphor work. Moreover, a strong didactic stance that supports sense-making must be based on a strong teacher role...

A portfolio risk analysis on electricity supply planning

International Nuclear Information System (INIS)

Huang, Y.-H.; Wu, J.-H.

2008-01-01

Conventional electricity planning selects from a range of alternative technologies based on the least-cost method without assessing cost-related risks. The current approach to determining energy generation portfolios creates a preference for fossil fuel. Consequently, this preference results in increased exposure to recent fluctuations in fossil fuel prices, particularly for countries heavily depend on imported energy. This paper applies portfolio theory in conventional electricity planning with Taiwan as a case study. The model objective is to minimize the 'risk-weighted present value of total generation cost'. Both the present value of generating cost and risk (variance of the generating cost) are considered. Risk of generating cost is introduced for volatile fuel prices and uncertainty of technological change and capital cost reduction. The impact of risk levels on the portfolio of power generation technologies is also examined to provide some valuable policy suggestions. Study results indicate that replacing fossil fuel with renewable energy helps reduce generating cost risk. However, due to limited renewable development potential in Taiwan, there is an upper bound of 15% on the maximum share of renewable energy in the generating portfolio. In the meantime, reevaluating the current nuclear energy policy for reduced exposure to fossil fuel price fluctuations is worthwhile

A Diagnostic Assessment of Evolutionary Multiobjective Optimization for Water Resources Systems

Science.gov (United States)

Reed, P.; Hadka, D.; Herman, J.; Kasprzyk, J.; Kollat, J.

2012-04-01

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.

Characteristics of Omega-Optimized Portfolios at Different Levels of Threshold Returns

Directory of Open Access Journals (Sweden)

Renaldas Vilkancas

2014-12-01

Full Text Available There is little literature considering effects that the loss-gain threshold used for dividing good and bad outcomes by all downside (upside risk measures has on portfolio optimization and performance. The purpose of this study is to assess the performance of portfolios optimized with respect to the Omega function developed by Keating and Shadwick at different levels of the threshold returns. The most common choices of the threshold values used in various Omega studies cover the risk-free rate and the average market return or simply a zero return, even though the inventors of this measure for risk warn that “using the values of the Omega function at particular points can be critically misleading” and that “only the entire Omega function contains information on distribution”. The obtained results demonstrate the importance of the selected values of the threshold return on portfolio performance – higher levels of the threshold lead to an increase in portfolio returns, albeit at the expense of a higher risk. In fact, within a certain threshold interval, Omega-optimized portfolios achieved the highest net return, compared with all other strategies for portfolio optimization using three different test datasets. However, beyond a certain limit, high threshold values will actually start hurting portfolio performance while meta-heuristic optimizers typically are able to produce a solution at any level of the threshold, and the obtained results would most likely be financially meaningless.

12 CFR 347.108 - Portfolio investments.

Science.gov (United States)

2010-01-01

... 12 Banks and Banking 4 2010-01-01 2010-01-01 false Portfolio investments. 347.108 Section 347.108... INTERNATIONAL BANKING § 347.108 Portfolio investments. (a) Portfolio investments. If a bank, directly or indirectly, acquires or holds an equity interest in a foreign organization as a portfolio investment and the...

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

Directory of Open Access Journals (Sweden)

Chun Wang

2017-01-01

Full Text Available A novel multiobjective memetic algorithm based on decomposition (MOMAD is proposed to solve multiobjective flexible job shop scheduling problem (MOFJSP, which simultaneously minimizes makespan, total workload, and critical workload. Firstly, a population is initialized by employing an integration of different machine assignment and operation sequencing strategies. Secondly, multiobjective memetic algorithm based on decomposition is presented by introducing a local search to MOEA/D. The Tchebycheff approach of MOEA/D converts the three-objective optimization problem to several single-objective optimization subproblems, and the weight vectors are grouped by K-means clustering. Some good individuals corresponding to different weight vectors are selected by the tournament mechanism of a local search. In the experiments, the influence of three different aggregation functions is first studied. Moreover, the effect of the proposed local search is investigated. Finally, MOMAD is compared with eight state-of-the-art algorithms on a series of well-known benchmark instances and the experimental results show that the proposed algorithm outperforms or at least has comparative performance to the other algorithms.

Fuzzy Multi-objective Linear Programming Approach

Directory of Open Access Journals (Sweden)

Amna Rehmat

2007-07-01

Full Text Available Traveling salesman problem (TSP is one of the challenging real-life problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with different approaches including genetic algorithms and linear programming. Conventional linear programming is designed to deal with crisp parameters, but information about real life systems is often available in the form of vague descriptions. Fuzzy methods are designed to handle vague terms, and are most suited to finding optimal solutions to problems with vague parameters. Fuzzy multi-objective linear programming, an amalgamation of fuzzy logic and multi-objective linear programming, deals with flexible aspiration levels or goals and fuzzy constraints with acceptable deviations. In this paper, a methodology, for solving a TSP with imprecise parameters, is deployed using fuzzy multi-objective linear programming. An example of TSP with multiple objectives and vague parameters is discussed.

Portfolios in Saudi medical colleges

Science.gov (United States)

Fida, Nadia M.; Shamim, Muhammad S.

2016-01-01

Over recent decades, the use of portfolios in medical education has evolved, and is being applied in undergraduate and postgraduate programs worldwide. Portfolios, as a learning process and method of documenting and assessing learning, is supported as a valuable tool by adult learning theories that stress the need for learners to be self-directed and to engage in experiential learning. Thoughtfully implemented, a portfolio provides learning experiences unequaled by any single learning tool. The credibility (validity) and dependability (reliability) of assessment through portfolios have been questioned owing to its subjective nature; however, methods to safeguard these features have been described in the literature. This paper discusses some of this literature, with particular attention to the role of portfolios in relation to self-reflective learning, provides an overview of current use of portfolios in undergraduate medical education in Saudi Arabia, and proposes research-based guidelines for its implementation and other similar contexts. PMID:26905344

A ubiquitous reflective e-portfolio architecture.

Science.gov (United States)

Forte, Marcos; de Souza, Wanderley L; da Silva, Roseli F; do Prado, Antonio F; Rodrigues, Jose F

2013-11-01

and in group activities, and for the paper-based version while in patient attendance. There is evidence that the environment where the professional practice takes place influences the usage of the e-portfolio. Mobile devices were able to support students in their professional practice; however, these devices present characteristics that must be judiciously selected, otherwise, they may limit the execution of important tasks. The main shortcoming identified during the evaluation tests was about the use of the module, and of the access device, during patient attendance. For this reason, we have envisioned a new version of the Professional Practice Module that shall follow a twofold requisite: by one side, it will include all the features of the module, to be used at the university or in the students' homes; from the other side, it will include only the features that are essential for the practice of patient attendance. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach

Science.gov (United States)

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

2014-09-01

Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.

Building a Smart E-Portfolio Platform for Optimal E-Learning Objects Acquisition

Directory of Open Access Journals (Sweden)

Chih-Kun Ke

2013-01-01

Full Text Available In modern education, an e-portfolio platform helps students in acquiring e-learning objects in a learning activity. Quality is an important consideration in evaluating the desirable e-learning object. Finding a means of determining a high quality e-learning object from a large number of candidate e-learning objects is an important requirement. To assist student learning in a modern e-portfolio platform, this work proposed an optimal selection approach determining a reasonable e-learning object from various candidate e-learning objects. An optimal selection approach which uses advanced information techniques is proposed. Each e-learning object undergoes a formalization process. An Information Retrieval (IR technique extracts and analyses key concepts from the student’s previous learning contexts. A context-based utility model computes the expected utility values of various e-learning objects based on the extracted key concepts. The expected utility values of e-learning objects are used in a multicriteria decision analysis to determine the optimal selection order of the candidate e-learning objects. The main contribution of this work is the demonstration of an effective e-learning object selection method which is easy to implement within an e-portfolio platform and which makes it smarter.

VAR Portfolio Optimal: Perbandingan Antara Metode Markowitz dan Mean Absolute Deviation

Directory of Open Access Journals (Sweden)

R. Agus Sartono

2009-05-01

Full Text Available Portfolio selection method which have been introduced by Harry Markowitz (1952 used variance or deviation standard as a measure of risk. Kanno and Yamazaki (1991 introduced another method and used mean absolute deviation as a measure of risk instead of variance. The Value-at Risk (VaR is a relatively new method to capitalized risk that been used by financial institutions. The aim of this research is compare between mean variance and mean absolute deviation of two portfolios. Next, we attempt to assess the VaR of two portfolios using delta normal method and historical simulation. We use the secondary data from the Jakarta Stock Exchange – LQ45 during 2003. We find that there is a weak-positive correlation between deviation standard and return in both portfolios. The VaR nolmal delta based on mean absolute deviation method eventually is higher than the VaR normal delta based on mean variance method. However, based on the historical simulation the VaR of two methods is statistically insignificant. Thus, the deviation standard is sufficient measures of portfolio risk.Keywords: optimalisasi portofolio, mean-variance, mean-absolute deviation, value-at-risk, metode delta normal, metode simulasi historis

Portfolio Analysis for Vector Calculus

Science.gov (United States)

Kaplan, Samuel R.

2015-01-01

Classic stock portfolio analysis provides an applied context for Lagrange multipliers that undergraduate students appreciate. Although modern methods of portfolio analysis are beyond the scope of vector calculus, classic methods reinforce the utility of this material. This paper discusses how to introduce classic stock portfolio analysis in a…

Using Electronic Portfolios

Science.gov (United States)

Page, Deb

2012-01-01

The digitized collections of artifacts known as electronic portfolios are creating solutions to a variety of performance improvement needs in ways that are cost-effective and improve both individual and group learning and performance. When social media functionality is embedded in e-portfolios, the tools support collaboration, social learning,…

Multi-objective congestion management by modified augmented ε-constraint method

International Nuclear Information System (INIS)

Esmaili, Masoud; Shayanfar, Heidar Ali; Amjady, Nima

2011-01-01

Congestion management is a vital part of power system operations in recent deregulated electricity markets. However, after relieving congestion, power systems may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion management framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion management framework is presented while simultaneously optimizing the competing objective functions of congestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented ε-constraint method, is based on the augmented ε-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multi-objective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method. (author)

Portfolios dominating indices: Optimization with second-order stochastic dominance constraints vs. minimum and mean variance portfolios

OpenAIRE

Keçeci, Neslihan Fidan; Kuzmenko, Viktor; Uryasev, Stan

2016-01-01

The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG) package, which has precoded modules for op...

Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios

OpenAIRE

Neslihan Fidan Keçeci; Viktor Kuzmenko; Stan Uryasev

2016-01-01

The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG) package, which has precoded modules for op...

Portfolio i erhvervsuddannelserne

DEFF Research Database (Denmark)

2008-01-01

Materialet kombinerer korte film med introducerende tekster og belyser fra forskellige vinkler, hvordan portfolio kan bruges som evalueringsmetode i erhvervsuddannelserne. Udgiver: Undervisningsministeriet Udgivelsessted: Pub.uvm.dk......Materialet kombinerer korte film med introducerende tekster og belyser fra forskellige vinkler, hvordan portfolio kan bruges som evalueringsmetode i erhvervsuddannelserne. Udgiver: Undervisningsministeriet Udgivelsessted: Pub.uvm.dk...

Portfolio Management

Science.gov (United States)

Duncan, Sharon L.

2011-01-01

Enterprise Business Information Services Division (EBIS) supports the Laboratory and its functions through the implementation and support of business information systems on behalf of its business community. EBIS Five Strategic Focus Areas: (1) Improve project estimating, planning and delivery capability (2) Improve maintainability and sustainability of EBIS Application Portfolio (3) Leap forward in IT Leadership (4) Comprehensive Talent Management (5) Continuous IT Security Program. Portfolio Management is a strategy in which software applications are managed as assets

VAR Portfolio Optimal: Perbandingan Antara Metode Markowitz Dan Mean Absolute Deviation

OpenAIRE

Sartono, R. Agus; Setiawan, Arie Andika

2006-01-01

Portfolio selection method which have been introduced by Harry Markowitz (1952) used variance or deviation standard as a measure of risk. Kanno and Yamazaki (1991) introduced another method and used mean absolute deviation as a measure of risk instead of variance. The Value-at Risk (VaR) is a relatively new method to capitalized risk that been used by financial institutions. The aim of this research is compare between mean variance and mean absolute deviation of two portfolios. Next, we attem...

Omega-optimized portfolios: applying stochastic dominance criterion for the selection of the threshold return

Directory of Open Access Journals (Sweden)

Renaldas Vilkancas

2016-05-01

Full Text Available Purpose of the article: While using asymmetric risk-return measures an important role is played by selection of the investor‘s required or threshold rate of return. The scientific literature usually states that every investor should define this rate according to their degree of risk aversion. In this paper, it is attempted to look at the problem from a different perspective – empirical research is aimed at determining the influence of the threshold rate of return on the portfolio characteristics. Methodology/methods: In order to determine the threshold rate of return a stochastic dominance criterion was used. The results are verified using the commonly applied method of backtesting. Scientific aim: The aim of this paper is to propose a method allowing selecting the threshold rate of return reliably and objectively. Findings: Empirical research confirms that stochastic dominance criteria can be successfully applied to determine the rate of return preferred by the investor. Conclusions: A risk-free investment rate or simply a zero rate of return commonly used in practice is often justified neither by theoretical nor empirical studies. This work suggests determining the threshold rate of return by applying the stochastic dominance criterion

Convex hull ranking algorithm for multi-objective evolutionary algorithms

NARCIS (Netherlands)

Davoodi Monfrared, M.; Mohades, A.; Rezaei, J.

2012-01-01

Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity

Credibilistic multi-period portfolio optimization based on scenario tree

Science.gov (United States)

Mohebbi, Negin; Najafi, Amir Abbas

2018-02-01

In this paper, we consider a multi-period fuzzy portfolio optimization model with considering transaction costs and the possibility of risk-free investment. We formulate a bi-objective mean-VaR portfolio selection model based on the integration of fuzzy credibility theory and scenario tree in order to dealing with the markets uncertainty. The scenario tree is also a proper method for modeling multi-period portfolio problems since the length and continuity of their horizon. We take the return and risk as well cardinality, threshold, class, and liquidity constraints into consideration for further compliance of the model with reality. Then, an interactive dynamic programming method, which is based on a two-phase fuzzy interactive approach, is employed to solve the proposed model. In order to verify the proposed model, we present an empirical application in NYSE under different circumstances. The results show that the consideration of data uncertainty and other real-world assumptions lead to more practical and efficient solutions.

A procedure for multi-objective optimization of tire design parameters

Directory of Open Access Journals (Sweden)

Nikola Korunović

2015-04-01

Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

USEFULNESS OF BOOTSTRAPPING IN PORTFOLIO MANAGEMENT

Directory of Open Access Journals (Sweden)

Boris Radovanov

2012-12-01

Full Text Available This paper contains a comparison of in-sample and out-of-sample performances between the resampled efficiency technique, patented by Richard Michaud and Robert Michaud (1999, and traditional Mean-Variance portfolio selection, presented by Harry Markowitz (1952. Based on the Monte Carlo simulation, data (samples generation process determines the algorithms by using both, parametric and nonparametric bootstrap techniques. Resampled efficiency provides the solution to use uncertain information without the need for constrains in portfolio optimization. Parametric bootstrap process starts with a parametric model specification, where we apply Capital Asset Pricing Model. After the estimation of specified model, the series of residuals are used for resampling process. On the other hand, nonparametric bootstrap divides series of price returns into the new series of blocks containing previous determined number of consecutive price returns. This procedure enables smooth resampling process and preserves the original structure of data series.

Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas

Directory of Open Access Journals (Sweden)

Jin Xisong

2018-02-01

Full Text Available Previous research has focused on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, existing dynamic models are not easily applied to high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures such as Value at Risk (VaR and Expected Shortfall (ES for passive portfolios and dynamic optimal portfolios using Mean-Variance and ES criteria for a sample of US stocks over a period of 10 years. Our results suggest that (1 Modeling the marginal distribution is important for dynamic high-dimensional multivariate models. (2 Neglecting the dynamic dependence in the copula causes over-aggressive risk management. (3 The DCC/DECO Gaussian copula and t-copula work very well for both VaR and ES. (4 Grouped t-copulas and t-copulas with dynamic degrees of freedom further match the fat tail. (5 Correctly modeling the dependence structure makes an improvement in portfolio optimization with respect to tail risk. (6 Models driven by multivariate t innovations with exogenously given degrees of freedom provide a flexible and applicable alternative for optimal portfolio risk management.

The Development of E-Portfolio Evaluation Criteria and Application to the Blackboard LMS E-Portfolio

Science.gov (United States)

McKenna, Gary F.; Stansfield, Mark H.

2012-01-01

The purpose of this paper is to develop e-portfolio evaluation criteria which will be used to review the Blackboard LMS e-portfolio being used at one Higher Education (HE) institution in the UK as evaluation criteria for reviewing e-portfolio provision does not exist in the literature. The approach taken was to initiate a wide literature search…

13 CFR 107.760 - How a change in size or activity of a Portfolio Concern affects the Licensee and the Portfolio...

Science.gov (United States)

2010-01-01

... of a Portfolio Concern affects the Licensee and the Portfolio Concern. 107.760 Section 107.760... § 107.760 How a change in size or activity of a Portfolio Concern affects the Licensee and the Portfolio Concern. (a) Effect on Licensee of a change in size of a Portfolio Concern. If a Portfolio Concern no...

Robust Active Portfolio Management

National Research Council Canada - National Science Library

Erdogan, E; Goldfarb, D; Iyengar, G

2006-01-01

... on the portfolio beta, and limits on cash and industry exposure. We show that the optimal portfolios can be computed by solving second-order cone programs -- a class of optimization problems with a worst case complexity (i.e...

A multi-objective framework to predict flows of ungauged rivers within regions of sparse hydrometeorologic observation

Science.gov (United States)

Alipour, M.; Kibler, K. M.

2017-12-01

Despite advances in flow prediction, managers of ungauged rivers located within broad regions of sparse hydrometeorologic observation still lack prescriptive methods robust to the data challenges of such regions. We propose a multi-objective streamflow prediction framework for regions of minimum observation to select models that balance runoff efficiency with choice of accurate parameter values. We supplement sparse observed data with uncertain or low-resolution information incorporated as `soft' a priori parameter estimates. The performance of the proposed framework is tested against traditional single-objective and constrained single-objective calibrations in two catchments in a remote area of southwestern China. We find that the multi-objective approach performs well with respect to runoff efficiency in both catchments (NSE = 0.74 and 0.72), within the range of efficiencies returned by other models (NSE = 0.67 - 0.78). However, soil moisture capacity estimated by the multi-objective model resonates with a priori estimates (parameter residuals of 61 cm versus 289 and 518 cm for maximum soil moisture capacity in one catchment, and 20 cm versus 246 and 475 cm in the other; parameter residuals of 0.48 versus 0.65 and 0.7 for soil moisture distribution shape factor in one catchment, and 0.91 versus 0.79 and 1.24 in the other). Thus, optimization to a multi-criteria objective function led to very different representations of soil moisture capacity as compared to models selected by single-objective calibration, without compromising runoff efficiency. These different soil moisture representations may translate into considerably different hydrological behaviors. The proposed approach thus offers a preliminary step towards greater process understanding in regions of severe data limitations. For instance, the multi-objective framework may be an adept tool to discern between models of similar efficiency to select models that provide the "right answers for the right reasons

A Numerical Study for Robust Active Portfolio Management with Worst-Case Downside Risk Measure

Directory of Open Access Journals (Sweden)

Aifan Ling

2014-01-01

Full Text Available Recently, active portfolio management problems are paid close attention by many researchers due to the explosion of fund industries. We consider a numerical study of a robust active portfolio selection model with downside risk and multiple weights constraints in this paper. We compare the numerical performance of solutions with the classical mean-variance tracking error model and the naive 1/N portfolio strategy by real market data from China market and other markets. We find from the numerical results that the tested active models are more attractive and robust than the compared models.

Optimal portfolio strategies under a shortfall constraint | Akume ...

African Journals Online (AJOL)

We impose dynamically, a shortfall constraint in terms of Tail Conditional Expectation on the portfolio selection problem in continuous time, in order to obtain optimal strategies. The nancial market is assumed to comprise n risky assets driven by geometric Brownian motion and one risk-free asset. The method of Lagrange ...

Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

Directory of Open Access Journals (Sweden)

L. DJEROU,

2012-01-01

Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

Recent advances in evolutionary multi-objective optimization

CERN Document Server

Datta, Rituparna; Gupta, Abhishek

2017-01-01

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high ...

Geometrical framework for robust portfolio optimization

OpenAIRE

Bazovkin, Pavel

2014-01-01

We consider a vector-valued multivariate risk measure that depends on the user's profile given by the user's utility. It is constructed on the basis of weighted-mean trimmed regions and represents the solution of an optimization problem. The key feature of this measure is convexity. We apply the measure to the portfolio selection problem, employing different measures of performance as objective functions in a common geometrical framework.

A Mean variance analysis of arbitrage portfolios

Science.gov (United States)

Fang, Shuhong

2007-03-01

Based on the careful analysis of the definition of arbitrage portfolio and its return, the author presents a mean-variance analysis of the return of arbitrage portfolios, which implies that Korkie and Turtle's results ( B. Korkie, H.J. Turtle, A mean-variance analysis of self-financing portfolios, Manage. Sci. 48 (2002) 427-443) are misleading. A practical example is given to show the difference between the arbitrage portfolio frontier and the usual portfolio frontier.

Patent portfolio management: literature review and a proposed model.

Science.gov (United States)

Conegundes De Jesus, Camila Kiyomi; Salerno, Mario Sergio

2018-05-09

Patents and patent portfolios are gaining attention in the last decades, from the called 'pro-patent era' to the recent billionaire transactions involving patent portfolios. The field is growing in importance, both theoretically and practically and despite having substantial literature on new product development portfolio management, we have not found an article relating this theory to patent portfolios. Areas covered: The paper develops a systematic literature review on patent portfolio management to organize the evolution and tendencies of patent portfolio management, highlighting distinctive features of patent portfolio management. Interview with IP manager of three life sciences companies, including a leading multinational group provided relevant information about patent portfolio management. Expert opinion: Based on the systematic literature review on portfolio management, more specifically, on new product development portfolio theory, and interview the paper proposes the paper proposes a reference model to manage patent portfolios. The model comprises four stages aligned with the three goals of the NPD portfolio management: 1 - Linking strategy of the Company's NPD Portfolio to Patent Portfolio; 2 - Balancing the portfolio in buckets; 3 - Patent Valuation (maximizing valuation); 4 - Regularly reviewing the patent portfolio.

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

Energy Technology Data Exchange (ETDEWEB)

Zhou, Z; Folkert, M; Iyengar, P; Zhang, Y; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

Science.gov (United States)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Multi-objective and multi-criteria optimization for power generation expansion planning with CO2 mitigation in Thailand

Directory of Open Access Journals (Sweden)

Kamphol Promjiraprawat

2013-06-01

Full Text Available In power generation expansion planning, electric utilities have encountered the major challenge of environmental awareness whilst being concerned with budgetary burdens. The approach for selecting generating technologies should depend on economic and environmental constraint as well as externalities. Thus, the multi-objective optimization becomes a more attractive approach. This paper presents a hybrid framework of multi-objective optimization and multi-criteria decision making to solve power generation expansion planning problems in Thailand. In this paper, CO2 emissions and external cost are modeled as a multi-objective optimization problem. Then the analytic hierarchy process is utilized to determine thecompromised solution. For carbon capture and storage technology, CO2 emissions can be mitigated by 74.7% from the least cost plan and leads to the reduction of the external cost of around 500 billion US dollars over the planning horizon. Results indicate that the proposed approach provides optimum cost-related CO2 mitigation plan as well as external cost.

Original article Portfolio working – a psychological analysis of the phenomenon

Directory of Open Access Journals (Sweden)

Agnieszka Lipińska-Grobelny

2014-10-01

Full Text Available Background It turns out from the latest Eurostat data that Poland holds the second place in Europe in terms of the number of so-called portfolio workers, i.e. persons who work for more than one employer. From the psychological point of view, there arises the question regarding the possible determinants of the mentioned phenomenon. Therefore the general purpose of this study is to present the personal and situational indicators of portfolio working. Participants and procedure Two hundred and eighteen portfolio workers and 218 workers employed in one workplace (i.e. monoworkers participated in research using the following set of ‘paper-pencil’ techniques: a self-made survey, the Value Scale by Rokeach, the Formal Characteristics of Behaviour – Temperament Questionnaire by Zawadzki and Strelau, the Masculinity and Femininity Scale by Lipińska-Grobelny and Gorczycka, and the Organizational Climate Questionnaire by Kolb. Results Portfolio working is mainly determined by a number of personal variables (temperamental characteristics, values and spheres of motivation, intensity of masculinity and femininity. A specific role is played by values represented by portfolio workers. The discriminant analysis conducted in groups selected on the basis of working hours indicates that the prediction of participation of the examined persons in the group of portfolio workers with the greatest accuracy appeared in the case of a workload of 48 or more hours, next in the case of a smaller workload up to 47 hours, and finally for the whole group. Conclusions The examination of the phenomenon of portfolio working from the psychological perspective presents an important contribution to the discussion on work and directions of its transformations.

Multi-objective optimization design method of radiation shielding

International Nuclear Information System (INIS)

Yang Shouhai; Wang Weijin; Lu Daogang; Chen Yixue

2012-01-01

Due to the shielding design goals of diversification and uncertain process of many factors, it is necessary to develop an optimization design method of intelligent shielding by which the shielding scheme selection will be achieved automatically and the uncertainties of human impact will be reduced. For economical feasibility to achieve a radiation shielding design for automation, the multi-objective genetic algorithm optimization of screening code which combines the genetic algorithm and discrete-ordinate method was developed to minimize the costs, size, weight, and so on. This work has some practical significance for gaining the optimization design of shielding. (authors)

Purchasing portfolio usage and purchasing sophistication

NARCIS (Netherlands)

Gelderman, C.J.; Weele, van A.J.

2005-01-01

Purchasing portfolio models have caused considerable controversy in literature. Many advantages and disadvantages have been put forward, revealing a strong disagreement on the merits of portfolio models. This study addresses the question whether or not the use of purchasing portfolio models should

Household portfolios and implicit risk aversion

NARCIS (Netherlands)

Bucciol, A.; Miniaci, R.

2008-01-01

We derive from a sample of US households the distribution of the risk aversion implicit in their portfolio choice. Our estimate minimizes the distance between the certainty equivalent return generated with observed portfolios and portfolios that are optimal in a mean-variance framework. Taking into

Hierarchical Portfolio Management: Theory and Applications

NARCIS (Netherlands)

H. Ning (Haikun)

2007-01-01

textabstractUnder his own preference, how should an investor coordinate the asset managers such that his aggregated portfolio is optimized? The efficiency of each managed sub portfolio and the aggregation of all the sub portfolios are the 2 main underlying problems considered in this dissertation.

Quantitative investment strategies and portfolio management

NARCIS (Netherlands)

Guo, J.

2012-01-01

This book contains three essays on alternative investments and portfolio management. Taking from a portfolio investor’s perspective, the first essay analyzes the portfolio implication of investing in hedge funds when there is a hedge fund lockup period. The second essay studies the investment

Exploiting stock data: a survey of state of the art computational techniques aimed at producing beliefs regarding investment portfolios

Directory of Open Access Journals (Sweden)

Mario Linares Vásquez

2008-01-01

Full Text Available Selecting an investment portfolio has inspired several models aimed at optimising the set of securities which an in-vesttor may select according to a number of specific decision criteria such as risk, expected return and planning hori-zon. The classical approach has been developed for supporting the two stages of portfolio selection and is supported by disciplines such as econometrics, technical analysis and corporative finance. However, with the emerging field of computational finance, new and interesting techniques have arisen in line with the need for the automatic processing of vast volumes of information. This paper surveys such new techniques which belong to the body of knowledge con-cerning computing and systems engineering, focusing on techniques particularly aimed at producing beliefs regar-ding investment portfolios.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

Science.gov (United States)

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms

Science.gov (United States)

Aguirre, Hernán; Sato, Masahiko; Tanaka, Kiyoshi

In this paper, we propose δ-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.

A multi-objective programming model for assessment the GHG emissions in MSW management

Energy Technology Data Exchange (ETDEWEB)

Mavrotas, George, E-mail: mavrotas@chemeng.ntua.gr [National Technical University of Athens, Iroon Polytechniou 9, Zografou, Athens, 15780 (Greece); Skoulaxinou, Sotiria [EPEM SA, 141 B Acharnon Str., Athens, 10446 (Greece); Gakis, Nikos [FACETS SA, Agiou Isidorou Str., Athens, 11471 (Greece); Katsouros, Vassilis [Athena Research and Innovation Center, Artemidos 6 and Epidavrou Str., Maroussi, 15125 (Greece); Georgopoulou, Elena [National Observatory of Athens, Thisio, Athens, 11810 (Greece)

2013-09-15

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the

A multi-objective programming model for assessment the GHG emissions in MSW management

International Nuclear Information System (INIS)

Mavrotas, George; Skoulaxinou, Sotiria; Gakis, Nikos; Katsouros, Vassilis; Georgopoulou, Elena

2013-01-01

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH 4 generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application

Project Portfolio Management Applications Testing

OpenAIRE

Paul POCATILU

2006-01-01

Many IT companies are running project simultaneously. In order to achieve the best results, they have to group to the project in portfolios, and to use specific software that helps to manage them. Project portfolio management applications have a high degree of complexity and they are very important for the companies that are using it. This paper focuses on some characteristics of the testing process for project portfolio management applications

Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions

Directory of Open Access Journals (Sweden)

Yunsong Han

2017-12-01

Full Text Available In the current context of increasing energy demand, timber-glass buildings will become a necessary trend in sustainable architecture in the future. Especially in severe cold zones of China, energy consumption and the visual comfort of residential buildings have attracted wide attention, and there are always trade-offs between multiple objectives. This paper aims to propose a simulation-based multiobjective optimization method to improve the daylighting, energy efficiency, and economic performance of timber-glass buildings in severe cold regions. Timber-glass building form variables have been selected as the decision variables, including building width, roof height, south and north window-to-wall ratio (WWR, window height, and orientation. A simulation-based multiobjective optimization model has been developed to optimize these performance objectives simultaneously. The results show that Daylighting Autonomy (DA presents negative correlations with Energy Use Intensity (EUI and total cost. Additionally, with an increase in DA, Useful Daylighting Illuminance (UDI demonstrates a tendency of primary increase and then decrease. Using this optimization model, four building performances have been improved from the initial generation to the final generation, which proves that simulation-based multiobjective optimization is a promising approach to improve the daylighting, energy efficiency, and economic performances of timber-glass buildings in severe cold regions.

A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

International Nuclear Information System (INIS)

Khoroshiltseva, Marina; Slanzi, Debora; Poli, Irene

2016-01-01

Highlights: • We present a multi-objective optimization algorithm for shading design. • We combine Harmony search and Pareto-based procedures. • Thermal and daylighting performances of external shading were considered. • We applied the optimization process to a residential social housing in Madrid. - Abstract: In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions.

Multi-objective optimization of a vertical ground source heat pump using evolutionary algorithm

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Amlashi, Emad Hadaddi; Amidpour, Majid

2009-01-01

Thermodynamic and thermoeconomic optimization of a vertical ground source heat pump system has been studied. A model based on the energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. The proposed vertical ground source heat pump system including eight decision variables is considered for optimization. An artificial intelligence technique known as evolutionary algorithm (EA) has been utilized as an optimization method. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective, thermoeconomic single objective and multi-objective optimizations are performed. In Multi-objective optimization, both thermodynamic and thermoeconomic objectives are considered, simultaneously. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from available optimal points on Pareto frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, to the annual number of operating hours and to the electricity cost are studied in detail.

A multi-objective approach for developing national energy efficiency plans

International Nuclear Information System (INIS)

Haydt, Gustavo; Leal, Vítor; Dias, Luís

2014-01-01

This paper proposes a new approach to deal with the problem of building national energy efficiency (EE) plans, considering multiple objectives instead of only energy savings. The objectives considered are minimizing the influence of energy use on climate change, minimizing the financial risk from the investment, maximizing the security of energy supply, minimizing investment costs, minimizing the impacts of building new power plants and transmission infrastructures, and maximizing the local air quality. These were identified through literature review and interaction with real decision makers. A database of measures is established, from which millions of potential EE plans can be built by combining measures and their respective degree of implementation. Finally, a hybrid multi-objective and multi-criteria decision analysis (MCDA) model is proposed to search and select the EE plans that best match the decision makers’ preferences. An illustration of the working mode and the type of results obtained from this novel hybrid model is provided through an application to Portugal. For each of five decision perspectives a wide range of potential best plans were identified. These wide ranges show the relevance of introducing multi-objective analysis in a comprehensive search space as a tool to inform decisions about national EE plans. - Highlights: • A multiple objective approach to aid the choice of national energy efficiency plans. • A hybrid multi-objective MCDA model is proposed to search among the possible plans. • The model identified relevant plans according to five different idealized DMs. • The approach is tested with Portugal

Optimization Stock Portfolio With Mean-Variance and Linear Programming: Case In Indonesia Stock Market

Directory of Open Access Journals (Sweden)

Yen Sun

2010-05-01

Full Text Available It is observed that the number of Indonesia’s domestic investor who involved in the stock exchange is very less compare to its total number of population (only about 0.1%. As a result, Indonesia Stock Exchange (IDX is highly affected by foreign investor that can threat the economy. Domestic investor tends to invest in risk-free asset such as deposit in the bank since they are not familiar yet with the stock market and anxious about the risk (risk-averse type of investor. Therefore, it is important to educate domestic investor to involve in the stock exchange. Investing in portfolio of stock is one of the best choices for risk-averse investor (such as Indonesia domestic investor since it offers lower risk for a given level of return. This paper studies the optimization of Indonesian stock portfolio. The data is the historical return of 10 stocks of LQ 45 for 5 time series (January 2004 – December 2008. It will be focus on selecting stocks into a portfolio, setting 10 of stock portfolios using mean variance method combining with the linear programming (solver. Furthermore, based on Efficient Frontier concept and Sharpe measurement, there will be one stock portfolio picked as an optimum Portfolio (Namely Portfolio G. Then, Performance of portfolio G will be evaluated by using Sharpe, Treynor and Jensen Measurement to show whether the return of Portfolio G exceeds the market return. This paper also illustrates how the stock composition of the Optimum Portfolio (G succeeds to predict the portfolio return in the future (5th January – 3rd April 2009. The result of the study observed that optimization portfolio using Mean-Variance (consistent with Markowitz theory combine with linear programming can be applied into Indonesia stock’s portfolio. All the measurements (Sharpe, Jensen, and Treynor show that the portfolio G is a superior portfolio. It is also been found that the composition (weights stocks of optimum portfolio (G can be used to

A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy

Science.gov (United States)

Jahangoshai Rezaee, Mustafa; Yousefi, Samuel; Hayati, Jamileh

2017-06-01

Supplier selection and allocation of optimal order quantity are two of the most important processes in closed-loop supply chain (CLSC) and reverse logistic (RL). So that providing high quality raw material is considered as a basic requirement for a manufacturer to produce popular products, as well as achieve more market shares. On the other hand, considering the existence of competitive environment, suppliers have to offer customers incentives like discounts and enhance the quality of their products in a competition with other manufacturers. Therefore, in this study, a model is presented for CLSC optimization, efficient supplier selection, as well as orders allocation considering quantity discount policy. It is modeled using multi-objective programming based on the integrated simultaneous data envelopment analysis-Nash bargaining game. In this study, maximizing profit and efficiency and minimizing defective and functions of delivery delay rate are taken into accounts. Beside supplier selection, the suggested model selects refurbishing sites, as well as determining the number of products and parts in each network's sector. The suggested model's solution is carried out using global criteria method. Furthermore, based on related studies, a numerical example is examined to validate it.

Implementing Portfolio Assessment in Lower-Secondary School

Directory of Open Access Journals (Sweden)

Anna Czura

2013-05-01

Full Text Available Since alternative assessment embraces highly authentic tasks consistent with classroom goals and instruction, its implementation in the language classroom is believed to promote collaboration with peers, transfer responsibility to the learners and, consequently, foster learner autonomy. This paper presents the results of a research study aiming to determine whether portfolio assessment contributes to the development of autonomy in adolescent learners. In order to collect the data, qualitative and quantative methods of research were applied. The research results reveal that the implementation of portfolio assessment failed to affect the overall level of learner autonomy. Introducing one selected pedagogical procedure does not suffice to foster learner autonomy. Teachers need to be ready to pass a portion of their authority to the learners, who, in turn, need to know how to use the new privileges judiciously.

Analysis of the Capability Portfolio Review (CPR)

Science.gov (United States)

2014-06-01

10  2.4.1.1.  The Basics of Modern Portfolio Theory ...of Modern Portfolio Theory Much of modern portfolio management has been motivated by the influential work of Harry Markowitz (Markowitz, 1952) and...unsystematic risk associated with individual stocks, leaving only the generally market risk (Walls, 2004). The basic assumption of modern portfolio theory is

Project Portfolio Management Applications Testing

Directory of Open Access Journals (Sweden)

Paul POCATILU

2006-01-01

Full Text Available Many IT companies are running project simultaneously. In order to achieve the best results, they have to group to the project in portfolios, and to use specific software that helps to manage them. Project portfolio management applications have a high degree of complexity and they are very important for the companies that are using it. This paper focuses on some characteristics of the testing process for project portfolio management applications

Portfolio insurance using traded options

OpenAIRE

Machado-Santos, Carlos

2001-01-01

Literature concerning the institutional use of options indicates that the main purpose of option trading is to provide investors with the opportunity to create return distributions previously unavailable, considering that options provide the means to manipulate portfolio returns. In such a context, this study intends to analyse the returns of insured portfolios generated by hedging strategies on underlying stock portfolios. Because dynamic hedging is too expensive, we have hedged the stock po...

Existence of pareto equilibria for multiobjective games without compactness

OpenAIRE

Shiraishi, Yuya; Kuroiwa, Daishi

2013-01-01

In this paper, we investigate the existence of Pareto and weak Pareto equilibria for multiobjective games without compactness. By employing an existence theorem of Pareto equilibria due to Yu and Yuan([10]), several existence theorems of Pareto and weak Pareto equilibria for the multiobjective games are established in a similar way to Flores-B´azan.

Minimum Variance Portfolios in the Brazilian Equity Market

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Alexandre Rubesam

2013-03-01

Full Text Available We investigate minimum variance portfolios in the Brazilian equity market using different methods to estimate the covariance matrix, from the simple model of using the sample covariance to multivariate GARCH models. We compare the performance of the minimum variance portfolios to those of the following benchmarks: (i the IBOVESPA equity index, (ii an equally-weighted portfolio, (iii the maximum Sharpe ratio portfolio and (iv the maximum growth portfolio. Our results show that the minimum variance portfolio has higher returns with lower risk compared to the benchmarks. We also consider long-short 130/30 minimum variance portfolios and obtain similar results. The minimum variance portfolio invests in relatively few stocks with low βs measured with respect to the IBOVESPA index, being easily replicable by individual and institutional investors alike.

PSN: Portfolio Social Network

DEFF Research Database (Denmark)

Cortes, Jordi Magrina; Nizamani, Sarwat; Memon, Nasrullah

2014-01-01

In this paper we present a web-based information system which is a portfolio social network (PSN) that provides solutions to the recruiters and job seekers. The proposed system enables users to create portfolio so that he/she can add his specializations with piece of code if any specifically...

Multi-Period Portfolio Optimization of Power Generation Assets

Directory of Open Access Journals (Sweden)

Barbara Glensk

2013-01-01

Full Text Available The liberalization and deregulation of the energy industry in the past decades have been significantly affected by changes in the strategies of energy firms. The traditionally used approach of cost minimization was no longer sufficient, risk and market behavior could no longer be ignored and the need for more appropriate optimization methods for uncertain environments was increased. Meanvariance portfolio (MVP theory is one of the more advanced financial methods that has been successfully applied to the energy sector. Unfortunately, this static approach is inadequate for studying multi-stage investment decision problems. The methodology proposed in this paper considering power generation assets is based on the model introduced by Mulvey, who suggests a reallocation approach using the analysis of various scenarios. The adoption of this methodology to power generation assets allows us to capture the impact of variations in the economic and technical parameters considered. The results of our study show that the application of a model for selection of multi-period portfolio can indeed improve the decision making process. Especially for the case of adding new investments to the portfolio mix, this rebalancing model captures new entries very well. (original abstract

Sygeplejestuderendes brug af portfolio i klinisk undervisning

DEFF Research Database (Denmark)

Hjorth, Anne Charlotte Overgaard; Bruhn, Helle

2014-01-01

Brugen af portfolio var mangelfuld, men et udviklingsprojekt i samarbejde med den kommunale sygepleje motiverede både sygeplejestuderende og kliniske vejledere til at anvende læringsdelen af portfolio aktivt.......Brugen af portfolio var mangelfuld, men et udviklingsprojekt i samarbejde med den kommunale sygepleje motiverede både sygeplejestuderende og kliniske vejledere til at anvende læringsdelen af portfolio aktivt....

Analysis of Various Multi-Objective Optimization Evolutionary Algorithms for Monte Carlo Treatment Planning System

CERN Document Server

Tydrichova, Magdalena

2017-01-01

In this project, various available multi-objective optimization evolutionary algorithms were compared considering their performance and distribution of solutions. The main goal was to select the most suitable algorithms for applications in cancer hadron therapy planning. For our purposes, a complex testing and analysis software was developed. Also, many conclusions and hypothesis have been done for the further research.

Multi-objective optimization of an organic Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm

International Nuclear Information System (INIS)

Wang, Jiangfeng; Yan, Zhequan; Wang, Man; Li, Maoqing; Dai, Yiping

2013-01-01

Highlights: • Multi-objective optimization of an ORC is conducted to obtain optimum performance. • NSGA-II is employed to solve this multi-objective optimization problem. • The effects of parameters on the exergy efficiency and capital cost are examined. - Abstract: Organic Rankine cycle (ORC) can effectively recover low grade waste heat due to its excellent thermodynamic performance. Based on the examinations of the effects of key thermodynamic parameters on the exergy efficiency and overall capital cost, multi-objective optimization of the ORC with R134a as working fluid is conducted to achieve the system optimization design from both thermodynamic and economic aspects using Non-dominated sorting genetic algorithm-II (NSGA-II). The exergy efficiency and overall capital cost are selected as two objective functions to maximize the exergy efficiency and minimize the overall capital cost under the given waste heat conditions. Turbine inlet pressure, turbine inlet temperature, pinch temperature difference, approach temperature difference and condenser temperature difference are selected as the decision variables owing to their significant effects on the exergy efficiency and overall capital cost. A Pareto frontier obtained shows that an increase in the exergy efficiency can increase the overall capital cost of the ORC system. The optimum design solution with their corresponding decision variables is selected from the Pareto frontier. The optimum exergy efficiency and overall capital cost are 13.98% and 129.28 × 10 4 USD, respectively, under the given waste heat conditions

Analysis of optoelectronic strategic planning in Taiwan by artificial intelligence portfolio tool

Science.gov (United States)

Chang, Rang-Seng

1992-05-01

Taiwan ROC has achieved significant advances in the optoelectronic industry with some Taiwan products ranked high in the world market and technology. Six segmentations of optoelectronic were planned. Each one was divided into several strategic items, design artificial intelligent portfolio tool (AIPT) to analyze the optoelectronic strategic planning in Taiwan. The portfolio is designed to provoke strategic thinking intelligently. This computer- generated strategy should be selected and modified by the individual. Some strategies for the development of the Taiwan optoelectronic industry also are discussed in this paper.

A New Method for Solving Multiobjective Bilevel Programs

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Ying Ji

2017-01-01

Full Text Available We study a class of multiobjective bilevel programs with the weights of objectives being uncertain and assumed to belong to convex and compact set. To the best of our knowledge, there is no study about this class of problems. We use a worst-case weighted approach to solve this class of problems. Our “worst-case weighted multiobjective bilevel programs” model supposes that each player (leader or follower has a set of weights to their objectives and wishes to minimize their maximum weighted sum objective where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto optimum concept, which we call “robust-weighted Pareto optimum”; for the worst-case weighted multiobjective optimization with the weight set of each player given as a polytope, we show that a robust-weighted Pareto optimum can be obtained by solving mathematical programing with equilibrium constraints (MPEC. For an application, we illustrate the usefulness of the worst-case weighted multiobjective optimization to a supply chain risk management under demand uncertainty. By the comparison with the existing weighted approach, we show that our method is more robust and can be more efficiently applied to real-world problems.

Cluster analysis for portfolio optimization

OpenAIRE

Vincenzo Tola; Fabrizio Lillo; Mauro Gallegati; Rosario N. Mantegna

2005-01-01

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio compositi...

Portfolio theory in the selection of oil investment projects Teoria de carteiras na seleção de projetos de investimento em petróleo

Directory of Open Access Journals (Sweden)

Jalimar Guimarães Simplício

2012-01-01

Full Text Available The objective of this article is to compare investment project selection using the efficient frontier in the mean-variance space based on optimization models introduced by Markowitz (1952 with the project ranking method according to the profitability index (PI. The selection of real assets by companies did not incorporate the mean-variance optimization procedure in the same way the selection of financial assets in investment portfolios did. The process of selection and formation of portfolios of investment projects for the oil area of a company in the energy industry was analyzed. Project portfolios formed according to the usual company practice of ranking by their PI were compared with those that result from applying mean-variance optimization through Monte Carlo simulation, which allows the computation of mean returns, variances, and covariances for the set of projects considered. The inefficiency of project portfolios obtained by ranking according to the PI compared to those obtained by the method of Markowitz suggests that there are opportunities to improve the process of selecting the set of projects to be implemented by companies.O objetivo deste artigo é comparar a seleção de projetos de investimento segundo a fronteira eficiente no espaço média-variância com base em modelos de otimização introduzidos por Markowitz (1952 com o método do ordenamento de projetos segundo o índice de lucratividade (IL. A seleção de ativos reais pelas empresas não incorporou o procedimento de otimização de média-variância da mesma forma que na seleção de ativos financeiros para carteiras de investimento. O processo de seleção e formação de carteiras de projetos de investimento pela área de petróleo de uma empresa do setor de energia foi analisado. Carteiras de projetos constituídas de acordo com a prática usual da empresa de ordenamento pelo IL foram comparadas com as que resultariam da aplicação da otimização de m

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

Directory of Open Access Journals (Sweden)

Jinjiao Hou

2018-05-01

Full Text Available This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC, and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1 compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2 multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

Constant Proportion Portfolio Insurance

DEFF Research Database (Denmark)

Jessen, Cathrine

2014-01-01

on the theme, originally proposed by Fischer Black. In CPPI, a financial institution guarantees a floor value for the “insured” portfolio and adjusts the stock/bond mix to produce a leveraged exposure to the risky assets, which depends on how far the portfolio value is above the floor. Plain-vanilla portfolio...... insurance largely died with the crash of 1987, but CPPI is still going strong. In the frictionless markets of finance theory, the issuer’s strategy to hedge its liability under the contract is clear, but in the real world with transactions costs and stochastic jump risk, the optimal strategy is less obvious...

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

Science.gov (United States)

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

Science.gov (United States)

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

Directory of Open Access Journals (Sweden)

Houli Duan

2010-01-01

Full Text Available We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

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Zhi Yang

2016-01-01

Full Text Available Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II.

Fuzzy multiobjective models for optimal operation of a hydropower system

Science.gov (United States)

Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

2013-06-01

Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

Validity of the Learning Portfolio: Analysis of a Portfolio Proposal for the University

Science.gov (United States)

Gregori-Giralt, Eva; Menéndez-Varela, José Luis

2015-01-01

Validity is a central issue in portfolio-based assessment. This empirical study used a quantitative approach to analyse the validity of the inferences drawn from a disciplinary course work portfolio assessment comprising profession-specific and learning competencies. The study also examined the problems involved in the development of the…

All-hexahedral meshing and remeshing for multi-object manufacturing applications

DEFF Research Database (Denmark)

Nielsen, Chris Valentin; Fernandes, J.L.M.; Martins, P.A.F.

2013-01-01

new developments related to the construction of adaptive core meshes and processing of multi-objects that are typical of manufacturing applications.Along with the aforementioned improvements there are other developments that will also be presented due to their effectiveness in increasing.......The presentation is enriched with examples taken from pure geometry and metal forming applications, and a resistance projection welding industrial test case consisting of four different objects is included to show the capabilities of selective remeshing of objects while maintaining contact conditions and local...

Performance of the reverse Helmbold universal portfolio

Science.gov (United States)

Tan, Choon Peng; Kuang, Kee Seng; Lee, Yap Jia

2017-04-01

The universal portfolio is an important investment strategy in a stock market where no stochastic model is assumed for the stock prices. The zero-gradient set of the objective function estimating the next-day portfolio which contains the reverse Kullback-Leibler order-alpha divergence is considered. From the zero-gradient set, the explicit, reverse Helmbold universal portfolio is obtained. The performance of the explicit, reverse Helmbold universal portfolio is studied by running them on some stock-price data sets from the local stock exchange. It is possible to increase the wealth of the investor by using these portfolios in investment.

Multiobjective memetic estimation of distribution algorithm based on an incremental tournament local searcher.

Science.gov (United States)

Yang, Kaifeng; Mu, Li; Yang, Dongdong; Zou, Feng; Wang, Lei; Jiang, Qiaoyong

2014-01-01

A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.

Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher

Directory of Open Access Journals (Sweden)

Kaifeng Yang

2014-01-01

Full Text Available A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.

Æstetik og portfolio

DEFF Research Database (Denmark)

Hyldahl, Kirsten Kofod; Sams, Pernille; Egelund, Karen Stine

2017-01-01

Nærværende artikel præsenterer resultaterne af udviklingsprojektet ”Portfolio og æstetik” på pædagoguddannelsen i Hjørring. Projektet har til formål, gennem æstetisk formsprog, at stilladsere og fastholde de studerendes læreprocesser samt udvikle og implementere portfolio i studieaktiviteter på...

Improvement of Iranian nurses' competence through professional portfolio: a quasi-experimental study.

Science.gov (United States)

Bahreini, Masoud; Moattari, Marzieh; Shahamat, Shohreh; Dobaradaran, Sina; Ravanipour, Mariam

2013-03-01

The aim of the study was to determine the effect of a portfolio-based professional development program on nurses' competence in a university hospital in Iran. A pre-test/post-test, controlled, quasi-experimental design was used. From the university hospital's 18 general wards, four wards were randomly selected. Two wards were randomly allocated as the experimental group (35 subjects) and two wards as the control group (38 subjects). Nurses in the experimental group participated in a 12-month portfolio-based professional development program and nurses in the control group participated in the routine professional development programs of their wards. The data were collected by the Nurse Competence Scale and were analyzed using descriptive statistics and independent and paired t-tests. After intervention, the average nurses' competence in the experimental group increased significantly (P professional portfolio is an effective tool for improving nurses' competence. The professional portfolios help nurses update their knowledge, skills, and competence towards their full role as nurses. © 2012 Wiley Publishing Asia Pty Ltd.

Specific patterns in portfolio analysis

Directory of Open Access Journals (Sweden)

Gabriela Victoria ANGHELACHE

2013-11-01

Full Text Available In the mid-twentieth century, under an unprecedented growth of the business of trading in securities, the need to provide a modern framework for assessing the performance of portfolios of financial instruments was felt. To that effect, it is noted that over this period, more and more economists have attempted to develop statistical mathematical models that ensure the evaluation of profitability and portfolio risk securities. These models are considered to be part of "the modern portfolio theory".

Modern Portfolio Theory: Some Main Results

OpenAIRE

Müller, Heinz H.

2017-01-01

This article summarizes some main results in modern portfolio theory. First, the Markowitz approach is presented. Then the capital asset pricing model is derived and its empirical testability is discussed. Afterwards Neumann-Morgenstern utility theory is applied to the portfolio problem. Finally, it is shown how optimal risk allocation in an economy may lead to portfolio insurance

Solving multiobjective optimal reactive power dispatch using modified NSGA-II

Energy Technology Data Exchange (ETDEWEB)

Jeyadevi, S.; Baskar, S.; Babulal, C.K.; Willjuice Iruthayarajan, M. [Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu 625 015 (India)

2011-02-15

This paper addresses an application of modified NSGA-II (MNSGA-II) by incorporating controlled elitism and dynamic crowding distance (DCD) strategies in NSGA-II to multiobjective optimal reactive power dispatch (ORPD) problem by minimizing real power loss and maximizing the system voltage stability. To validate the Pareto-front obtained using MNSGA-II, reference Pareto-front is generated using multiple runs of single objective optimization with weighted sum of objectives. For simulation purposes, IEEE 30 and IEEE 118 bus test systems are considered. The performance of MNSGA-II, NSGA-II and multiobjective particle swarm optimization (MOPSO) approaches are compared with respect to multiobjective performance measures. TOPSIS technique is applied on obtained non-dominated solutions to determine best compromise solution (BCS). Karush-Kuhn-Tucker (KKT) conditions are also applied on the obtained non-dominated solutions to substantiate a claim on optimality. Simulation results are quite promising and the MNSGA-II performs better than NSGA-II in maintaining diversity and authenticates its potential to solve multiobjective ORPD effectively. (author)

The Role of Agribusiness Assets in Investment Portfolios

OpenAIRE

Johnson, Michael; Malcolm, Bill; O'Connor, Ian

2006-01-01

Investment in agribusiness assets has grown significantly in recent years. The question of interest is whether including agribusiness assets in investment portfolios provide benefits. The effects of diversification by including agribusiness assets in two investment portfolios, a mixed asset portfolio and a diversified share portfolio was investigated using Markowitz’s (1952) Modern Portfolio Theory (MPT) of mean-variance optimization. To measure the performance of agribusiness assets, an in...

Performance of salmon fishery portfolios across western North America

Science.gov (United States)

Griffiths, Jennifer R; Schindler, Daniel E; Armstrong, Jonathan B; Scheuerell, Mark D; Whited, Diane C; Clark, Robert A; Hilborn, Ray; Holt, Carrie A; Lindley, Steven T; Stanford, Jack A; Volk, Eric C

2014-01-01

Quantifying the variability in the delivery of ecosystem services across the landscape can be used to set appropriate management targets, evaluate resilience and target conservation efforts. Ecosystem functions and services may exhibit portfolio-type dynamics, whereby diversity within lower levels promotes stability at more aggregated levels. Portfolio theory provides a framework to characterize the relative performance among ecosystems and the processes that drive differences in performance. We assessed Pacific salmon Oncorhynchus spp. portfolio performance across their native latitudinal range focusing on the reliability of salmon returns as a metric with which to assess the function of salmon ecosystems and their services to humans. We used the Sharpe ratio (e.g. the size of the total salmon return to the portfolio relative to its variability (risk)) to evaluate the performance of Chinook and sockeye salmon portfolios across the west coast of North America. We evaluated the effects on portfolio performance from the variance of and covariance among salmon returns within each portfolio, and the association between portfolio performance and watershed attributes. We found a positive latitudinal trend in the risk-adjusted performance of Chinook and sockeye salmon portfolios that also correlated negatively with anthropogenic impact on watersheds (e.g. dams and land-use change). High-latitude Chinook salmon portfolios were on average 2·5 times more reliable, and their portfolio risk was mainly due to low variance in the individual assets. Sockeye salmon portfolios were also more reliable at higher latitudes, but sources of risk varied among the highest performing portfolios. Synthesis and applications. Portfolio theory provides a straightforward method for characterizing the resilience of salmon ecosystems and their services. Natural variability in portfolio performance among undeveloped watersheds provides a benchmark for restoration efforts. Locally and regionally

Performance of salmon fishery portfolios across western North America.

Science.gov (United States)

Griffiths, Jennifer R; Schindler, Daniel E; Armstrong, Jonathan B; Scheuerell, Mark D; Whited, Diane C; Clark, Robert A; Hilborn, Ray; Holt, Carrie A; Lindley, Steven T; Stanford, Jack A; Volk, Eric C

2014-12-01

Quantifying the variability in the delivery of ecosystem services across the landscape can be used to set appropriate management targets, evaluate resilience and target conservation efforts. Ecosystem functions and services may exhibit portfolio-type dynamics, whereby diversity within lower levels promotes stability at more aggregated levels. Portfolio theory provides a framework to characterize the relative performance among ecosystems and the processes that drive differences in performance. We assessed Pacific salmon Oncorhynchus spp. portfolio performance across their native latitudinal range focusing on the reliability of salmon returns as a metric with which to assess the function of salmon ecosystems and their services to humans. We used the Sharpe ratio (e.g. the size of the total salmon return to the portfolio relative to its variability (risk)) to evaluate the performance of Chinook and sockeye salmon portfolios across the west coast of North America. We evaluated the effects on portfolio performance from the variance of and covariance among salmon returns within each portfolio, and the association between portfolio performance and watershed attributes. We found a positive latitudinal trend in the risk-adjusted performance of Chinook and sockeye salmon portfolios that also correlated negatively with anthropogenic impact on watersheds (e.g. dams and land-use change). High-latitude Chinook salmon portfolios were on average 2·5 times more reliable, and their portfolio risk was mainly due to low variance in the individual assets. Sockeye salmon portfolios were also more reliable at higher latitudes, but sources of risk varied among the highest performing portfolios. Synthesis and applications . Portfolio theory provides a straightforward method for characterizing the resilience of salmon ecosystems and their services. Natural variability in portfolio performance among undeveloped watersheds provides a benchmark for restoration efforts. Locally and regionally

Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: Evidence from the Chinese stock market

Science.gov (United States)

Sun, Xuelian; Liu, Zixian

2016-02-01

In this paper, a new estimator of correlation matrix is proposed, which is composed of the detrended cross-correlation coefficients (DCCA coefficients), to improve portfolio optimization. In contrast to Pearson's correlation coefficients (PCC), DCCA coefficients acquired by the detrended cross-correlation analysis (DCCA) method can describe the nonlinear correlation between assets, and can be decomposed in different time scales. These properties of DCCA make it possible to improve the investment effect and more valuable to investigate the scale behaviors of portfolios. The minimum variance portfolio (MVP) model and the Mean-Variance (MV) model are used to evaluate the effectiveness of this improvement. Stability analysis shows the effect of two kinds of correlation matrices on the estimation error of portfolio weights. The observed scale behaviors are significant to risk management and could be used to optimize the portfolio selection.

Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses

Science.gov (United States)

Dokeroglu, Tansel; Sert, Seyyit Alper; Cinar, Muhammet Serkan

2014-01-01

With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose. PMID:24892048

Ant colony algorithm for clustering in portfolio optimization

Science.gov (United States)

Subekti, R.; Sari, E. R.; Kusumawati, R.

2018-03-01

This research aims to describe portfolio optimization using clustering methods with ant colony approach. Two stock portfolios of LQ45 Indonesia is proposed based on the cluster results obtained from ant colony optimization (ACO). The first portfolio consists of assets with ant colony displacement opportunities beyond the defined probability limits of the researcher, where the weight of each asset is determined by mean-variance method. The second portfolio consists of two assets with the assumption that each asset is a cluster formed from ACO. The first portfolio has a better performance compared to the second portfolio seen from the Sharpe index.

Financial Advice and Individual Investor Portfolio Performance

NARCIS (Netherlands)

Kramer, M.M.

2012-01-01

This paper investigates whether financial advisers add value to individual investors portfolio decisions by comparing portfolios of advised and self-directed (execution-only) Dutch individual investors. The results indicate significant differences in characteristics and portfolios between these

Decentralized Portfolio Management

Directory of Open Access Journals (Sweden)

Benjamin Miranda Tabak

2003-12-01

Full Text Available We use a mean-variance model to analyze the problem of decentralized portfolio management. We find the solution for the optimal portfolio allocation for a head trader operating in n different markets, which is called the optimal centralized portfolio. However, as there are many traders specialized in different markets, the solution to the problem of optimal decentralized allocation should be different from the centralized case. In this paper we derive conditions for the solutions to be equivalent. We use multivariate normal returns and a negative exponential function to solve the problem analytically. We generate the equivalence of solutions by assuming that different traders face different interest rates for borrowing and lending. This interest rate is dependent on the ratio of the degrees of risk aversion of the trader and the head trader, on the excess return, and on the correlation between asset returns.

Multi-objective optimization of an underwater compressed air energy storage system using genetic algorithm

International Nuclear Information System (INIS)

Cheung, Brian C.; Carriveau, Rupp; Ting, David S.K.

2014-01-01

This paper presents the findings from a multi-objective genetic algorithm optimization study on the design parameters of an underwater compressed air energy storage system (UWCAES). A 4 MWh UWCAES system was numerically simulated and its energy, exergy, and exergoeconomics were analysed. Optimal system configurations were determined that maximized the UWCAES system round-trip efficiency and operating profit, and minimized the cost rate of exergy destruction and capital expenditures. The optimal solutions obtained from the multi-objective optimization model formed a Pareto-optimal front, and a single preferred solution was selected using the pseudo-weight vector multi-criteria decision making approach. A sensitivity analysis was performed on interest rates to gauge its impact on preferred system designs. Results showed similar preferred system designs for all interest rates in the studied range. The round-trip efficiency and operating profit of the preferred system designs were approximately 68.5% and $53.5/cycle, respectively. The cost rate of the system increased with interest rates. - Highlights: • UWCAES system configurations were developed using multi-objective optimization. • System was optimized for energy efficiency, exergy, and exergoeconomics • Pareto-optimal solution surfaces were developed at different interest rates. • Similar preferred system configurations were found at all interest rates studied

Optimal Portfolio Rebalancing Strategy : Evidence from Finnish Stocks

OpenAIRE

Savage, Akinwunmi

2010-01-01

Portfolio rebalancing is an established concept in portfolio management and investing generally. Assets within a portfolio have different return and risk prospects, and this inevitably leads them to drift away from their initial allocation weights overtime. Portfolio rebalancing is arguably the only method by which such assets can be reset to their initial weights, thus ensuring the portfolio reflects the risk appetite of the investor. Like many other concepts and practices in finance, portfo...

Multiobjective Genetic Algorithm applied to dengue control.

Science.gov (United States)

Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F

2014-12-01

Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. Copyright © 2014 Elsevier Inc. All rights reserved.

An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods.

Science.gov (United States)

Han, Honggui; Lu, Wei; Qiao, Junfei

2017-09-01

Multiobjective particle swarm optimization (MOPSO) algorithms have attracted much attention for their promising performance in solving multiobjective optimization problems (MOPs). In this paper, an adaptive MOPSO (AMOPSO) algorithm, based on a hybrid framework of the solution distribution entropy and population spacing (SP) information, is developed to improve the search performance in terms of convergent speed and precision. First, an adaptive global best (gBest) selection mechanism, based on the solution distribution entropy, is introduced to analyze the evolutionary tendency and balance the diversity and convergence of nondominated solutions in the archive. Second, an adaptive flight parameter adjustment mechanism, using the population SP information, is proposed to obtain the distribution of particles with suitable diversity and convergence, which can balance the global exploration and local exploitation abilities of the particles. Third, based on the gBest selection mechanism and the adaptive flight parameter mechanism, this proposed AMOPSO algorithm not only has high accuracy, but also attain a set of optimal solutions with better diversity. Finally, the performance of the proposed AMOPSO algorithm is validated and compared with other five state-of-the-art algorithms on a number of benchmark problems and water distribution system. The experimental results validate the effectiveness of the proposed AMOPSO algorithm, as well as demonstrate that AMOPSO outperforms other MOPSO algorithms in solving MOPs.

A Relationship Strategy Perspective on Relationship Portfolios

DEFF Research Database (Denmark)

Ritter, Thomas; Andersen, Henrik

2014-01-01

The paper develops a three-dimensional portfolio model for business relationships which distinguishes among six different categories. Based on assessments of customer profitability, customer commitment, and growth potential, the positioning of a given customer relationship in the portfolio allows...... managers to determine appropriate customer relationship strategies and appropriate performance indicators. Results from applying the portfolio model are reported and managerial implications and future research are discussed.......The paper develops a three-dimensional portfolio model for business relationships which distinguishes among six different categories. Based on assessments of customer profitability, customer commitment, and growth potential, the positioning of a given customer relationship in the portfolio allows...

Multi-objective optimization in computer networks using metaheuristics

CERN Document Server

Donoso, Yezid

2007-01-01

Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...

Fostering EFL learners’ autonomy in light of portfolio assessment: Exploring the potential impact of gender

Directory of Open Access Journals (Sweden)

Mahmood Hashemian

2013-07-01

Full Text Available The purpose of this study was to investigate the impact of portfolio assessment as a process-oriented mechanism on the autonomy of Iranian advanced EFL learners. A particular concern was to examine the potential effect of gender on portfolio assessment by taking the learners’ writing ability into account. The participants were 80 male and female advanced EFL learners to whom the Learner Autonomy Questionnaire (Kashefian, 2002 was administered to check their homogeneity prior to the study in terms of autonomy; a truncated form of a TOEFL test was also given to the participants to assess their language proficiency. The participants were then randomly divided into 4 groups: 2 experimental groups (20 females in class A and 20 males in class B and 2 control groups (20 females in class C and 20 males in class D. The portfolio assessment was integrated into the experimental groups to explore whether and to what extent their autonomy might enhance and also to investigate the possible effect of gender on portfolio assessment in writing ability. The portfolio assessment was based on the classroom portfolio model adopted from Hamp-Lyons and Condon (2000, consisting of 3 procedures: collection, selection, and reflection. In contrast, the control groups received the traditional assessment of writing. The data were analyzed using 2 independent samples t tests, mean, and the effect size. The results showed that the portfolio procedures considerably improved the autonomy of the participants. Also, gender had no impact on portfolio assessment.

Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection.

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Mark N Read

2016-09-01

Full Text Available The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto

12 CFR 1252.1 - Enterprise portfolio holding criteria.

Science.gov (United States)

2010-01-01

... 12 Banks and Banking 7 2010-01-01 2010-01-01 false Enterprise portfolio holding criteria. 1252.1 Section 1252.1 Banks and Banking FEDERAL HOUSING FINANCE AGENCY ENTERPRISES PORTFOLIO HOLDINGS § 1252.1 Enterprise portfolio holding criteria. The Enterprises are required to comply with the portfolio holdings...

Multiobjective Multifactorial Optimization in Evolutionary Multitasking.

Science.gov (United States)

Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang; Tan, Kay Chen

2016-05-03

In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

PRODUCT PORTFOLIO ANALYSIS - ARTHUR D. LITTLE MATRIX

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Curmei Catalin Valeriu

2011-07-01

Full Text Available In recent decades we have witnessed an unseen dynamism among companies, which is explained by their desire to engage in more activities that provide a high level of development and diversification. Thus, as companies are diversifying more and more, their managers confront a number of challenges arising from the management of resources for the product portfolio and the low level of resources with which companies can identify, at a time. Responding to these challenges, over time were developed a series of analytical product portfolio methods through which managers can balance the sources of cash flows from the multiple products and also can identify the place and role of products, in strategic terms, within the product portfolio. In order to identify these methods the authors of the present paper have conducted a desk research in order to analyze the strategic marketing and management literature of the last 2 decades. Widely were studied a series of methods that are presented in the marketing and management literature as the main instruments used within the product portfolio strategic planning process. Among these methods we focused on the Arthur D. Little matrix. Thus the present paper has the purpose to outline the characteristics and strategic implications of the ADL matrix within a company’s product portfolio. After conducting this analysis we have found that restricting the product portfolio analysis to the A.D.L. matrix is not a very wise decision. The A.D.L. matrix among with other marketing tools of product portfolio analysis have some advantages and disadvantages and is trying to provide, at a time, a specific diagnosis of a company’s product portfolio. Therefore, the recommendation for the Romanian managers consists in a combined use of a wide range of tools and techniques for product portfolio analysis. This leads to a better understanding of the whole mix of product markets, included in portfolio analysis, the strategic position

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

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Lvjiang Yin

2016-12-01

Full Text Available Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T, cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II, Strength Pareto Evolutionary Algorithm 2 (SPEA2, Multiobjective Particle Swarm Optimization (OMOPSO, and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D. Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

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Jing Qiu

2017-07-01

Full Text Available This paper presents a multi-objective transmission expansion planning (TEP framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component availability, and incentive-based demand response (IBDR costs. Specifically, the formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to conduct risk analysis. Thus, a final expansion plan can be selected according to individual risk preferences. Moreover, the economic value of IBDR is modeled and integrated into the cost objective. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and risk are provided. The proposed approach is numerically verified on the Garver’s six-bus, IEEE 24-bus RTS and Polish 2383-bus systems. Case study results demonstrate that the proposed approach can effectively reduce cost and hedge risk in relation to increasing wind power integration.

Development of a pump-turbine runner based on multiobjective optimization

International Nuclear Information System (INIS)

Xuhe, W; Baoshan, Z; Lei, T; Jie, Z; Shuliang, C

2014-01-01

As a key component of reversible pump-turbine unit, pump-turbine runner rotates at pump or turbine direction according to the demand of power grid, so higher efficiencies under both operating modes have great importance for energy saving. In the present paper, a multiobjective optimization design strategy, which includes 3D inverse design method, CFD calculations, response surface method (RSM) and multiobjective genetic algorithm (MOGA), is introduced to develop a model pump-turbine runner for middle-high head pumped storage plant. Parameters that controlling blade shape, such as blade loading and blade lean angle at high pressure side are chosen as input parameters, while runner efficiencies under both pump and turbine modes are selected as objective functions. In order to validate the availability of the optimization design system, one runner configuration from Pareto front is manufactured for experimental research. Test results show that the highest unit efficiency is 91.0% under turbine mode and 90.8% under pump mode for the designed runner, of which prototype efficiencies are 93.88% and 93.27% respectively. Viscous CFD calculations for full passage model are also conducted, which aim at finding out the hydraulic improvement from internal flow analyses

Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm

International Nuclear Information System (INIS)

Duan, Chen; Wang, Xinggang; Shu, Shuiming; Jing, Changwei; Chang, Huawei

2014-01-01

Highlights: • An improved thermodynamic model taking into account irreversibility parameter was developed. • A multi-objective optimization method for designing Stirling engine was investigated. • Multi-objective particle swarm optimization algorithm was adopted in the area of Stirling engine for the first time. - Abstract: In the recent years, the interest in Stirling engine has remarkably increased due to its ability to use any heat source from outside including solar energy, fossil fuels and biomass. A large number of studies have been done on Stirling cycle analysis. In the present study, a mathematical model based on thermodynamic analysis of Stirling engine considering regenerative losses and internal irreversibilities has been developed. Power output, thermal efficiency and the cycle irreversibility parameter of Stirling engine are optimized simultaneously using Particle Swarm Optimization (PSO) algorithm, which is more effective than traditional genetic algorithms. In this optimization problem, some important parameters of Stirling engine are considered as decision variables, such as temperatures of the working fluid both in the high temperature isothermal process and in the low temperature isothermal process, dead volume ratios of each heat exchanger, volumes of each working spaces, effectiveness of the regenerator, and the system charge pressure. The Pareto optimal frontier is obtained and the final design solution has been selected by Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP). Results show that the proposed multi-objective optimization approach can significantly outperform traditional single objective approaches

Mean-Reverting Portfolio With Budget Constraint

Science.gov (United States)

Zhao, Ziping; Palomar, Daniel P.

2018-05-01

This paper considers the mean-reverting portfolio design problem arising from statistical arbitrage in the financial markets. We first propose a general problem formulation aimed at finding a portfolio of underlying component assets by optimizing a mean-reversion criterion characterizing the mean-reversion strength, taking into consideration the variance of the portfolio and an investment budget constraint. Then several specific problems are considered based on the general formulation, and efficient algorithms are proposed. Numerical results on both synthetic and market data show that our proposed mean-reverting portfolio design methods can generate consistent profits and outperform the traditional design methods and the benchmark methods in the literature.

A portfolio analysis model of the demand for nuclear power plants