Meta-heuristic based decision support for portfolio optimization with a case study on tracking error minimization in passive portfolio management

OR Spectrum ◽  
2003 ◽  
Vol 25 (3) ◽  
pp. 345-378 ◽  
Author(s):  
Ulrich Derigs ◽  
Nils-H. Nickel
Keyword(s):  
Decision Support ◽  
Portfolio Optimization ◽  
Portfolio Management ◽  
Tracking Error ◽  
Error Minimization

scholarly journals A Python-based multicriteria portfolio selection DSS

2020 ◽  
Author(s):  
Panos Xidonas ◽  
Haris Doukas ◽  
Elissaios Sarmas
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Portfolio Optimization ◽  
Support System ◽  
Portfolio Management ◽  
Programming Model ◽  
Mixed Integer ◽  
Management Decision ◽  
Integer Quadratic Programming ◽  
Goal Programming Model

Our purpose in this article is to develop an integrated portfolio management decision support system, which takes into account the inherent multidimensional nature of the problem, while allowing the decision maker, i.e. investor, to incorporate his/her preferences in the decision process. The proposed decision support system has been developed in Python programming language and consists of two components: The first component is associated with the security selection phase, while the second component is associated with the portfolio optimization phase. In the first phase, four discrete multicriteria methods are employed; the PROMETHEE II, the ELECTRE III, the MAUT and the TOPSIS. After the cumulative integration of the results, a series of mathematical programming models are applied in the sec- ond phase, that of multicriteria portfolio optimization; a mixed-integer quadratic programming model, a goal programming model, a genetic algorithm model, and a multiobjective PROMETHEE flow model. Finally, the proposed approach is tested through a large-scale illustrative application in several stock markets and various sectors, analyzing simultaneously a very large number of securities.

scholarly journals Decision Support for the (Inter-)Basin Management of Water Resources Using Integrated Hydro-Economic Modeling

Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Gerald Norbert Souza da Silva ◽  
Márcia Maria Guedes Alcoforado de Moraes
Keyword(s):  
Decision Support ◽  
Water Resources ◽  
Water Allocation ◽  
Hydrological Modeling ◽  
Well Being ◽  
Water Transfer ◽  
Economic Modeling ◽  
Trade Offs ◽  
Study Results

The development of adequate modeling at the basin level to establish public policies has an important role in managing water resources. Hydro-economic models can measure the economic effects of structural and non-structural measures, land and water management, ecosystem services and development needs. Motivated by the need of improving water allocation using economic criteria, in this study, a Spatial Decision Support System (SDSS) with a hydro-economic optimization model (HEAL system) was developed and used for the identification and analysis of an optimal economic allocation of water resources in a case study: the sub-middle basin of the São Francisco River in Brazil. The developed SDSS (HEAL system) made the economically optimum allocation available to analyze water allocation conflicts and trade-offs. With the aim of providing a tool for integrated economic-hydrological modeling, not only for researchers but also for decision-makers and stakeholders, the HEAL system can support decision-making on the design of regulatory and economic management instruments in practice. The case study results showed, for example, that the marginal benefit function obtained for inter-basin water transfer, can contribute for supporting the design of water pricing and water transfer decisions, during periods of water scarcity, for the well-being in both basins.

scholarly journals Sustainable Production in Cement via Artificial Intelligence based Decision Support System: Case Study

2021 ◽  
pp. e00628
Author(s):  
Kübra Tümay Ateş ◽  
Cenk Şahin ◽  
Yusuf Kuvvetli ◽  
Bülent A. Küren ◽  
Aykut Uysal
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Sustainable Production

scholarly journals Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3747
Author(s):  
Ricardo Faia ◽  
Tiago Pinto ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Portfolio Optimization ◽  
Electricity Markets ◽  
Electricity Market ◽  
Retail Market ◽  
Wholesale Market ◽  
Generation Capacity ◽  
Wholesale Electricity Market ◽  
Portfolio Optimization Model ◽  
Photovoltaic Generators

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

scholarly journals Portfolio optimization of power plants by using renewable energy in Iran

2020 ◽  
Author(s):  
Seyedeh Asra Ahmadi ◽  
Seyed Mojtaba Mirlohi ◽  
Mohammad Hossein Ahmadi ◽  
Majid Ameri
Keyword(s):  
Renewable Energy ◽  
Portfolio Optimization ◽  
Energy Supply ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Modern Society ◽  
Sustainable Growth ◽  
Electricity Sector ◽  
Capital Costs

Abstract Lack of investment in the electricity sector has created a huge bottleneck in the continuous flow of energy in the market, and this will create many problems for the sustainable growth and development of modern society. The main reason for this lack of investment is the investment risk in the electricity sector. One way to reduce portfolio risk is to diversify it. This study applies the concept of portfolio optimization to demonstrate the potential for greater use of renewable energy, which reduces the risk of investing in the electricity sector. Besides, it shows that investing in renewable energies can offset the risk associated with the total input costs. These costs stem from the volatility of associated prices, including fossil fuel, capital costs, maintenance, operation and environmental costs. This case study shows that Iran can theoretically supply ~33% of its electricity demand from renewable energy sources compared to its current 15% share. This case study confirms this finding and predicts that Iran, while reducing the risk of investing in electricity supply, can achieve a renewable energy supply of ~9% with an average increase in supply costs. Sensitivity analysis further shows that with a 10% change in input cost factors, the percentage of renewable energy supply is only partially affected, but basket costs change according to the scenario of 5–32%. Finally, suggestions are made that minimize risk rather than cost, which will bring about an increase in renewable energy supply.

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