meta-heuristic algorithms
Sajad Janbaz; Seyed Mohammadreza Davoodi; Abdolmajid Abdolbaghi Ataabadi
Abstract
Purpose: The current research aims to present a multi-objective mathematical model with an integrated approach to scheduling and financial flow in production projects using Non-dominated Sorting Genetic Algorithm II (NSGA-II).Methodology: This research presents a multi-objective mathematical model integrating ...
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Purpose: The current research aims to present a multi-objective mathematical model with an integrated approach to scheduling and financial flow in production projects using Non-dominated Sorting Genetic Algorithm II (NSGA-II).Methodology: This research presents a multi-objective mathematical model integrating scheduling and financial flow optimization in civil engineering projects. This research addresses the scheduling and financial flow challenges in construction companies' production projects. The objective is to develop a multi-objective mathematical model that integrates scheduling and financial considerations to optimize resource allocation and minimize costs. The statistical population is in the form of a case study, and the required information and data were collected through interviews with managers of Kisson Construction Company.Findings: NSGA-II was used as an optimization algorithm to find efficient multi-objective solutions, and optimal results were presented to select civil and construction projects.Originality/Value: This research contributes to the field by proposing a novel multi-objective mathematical model that integrates scheduling and financial flow considerations in production projects. The use of the NSGA-II algorithm enhances the efficiency of finding optimal solutions. The findings can be valuable for decision-making when selecting construction and production projects.
Linear Optimization
Younes Nozarpour; Sayyed Mohammad Reza Davoodi; Mahdi Fadaee
Abstract
Purpose: The multi-period portfolio after closing, can be reviewed and modified at regular intervals. The philosophy behind using multi-period stock portfolio models is that investors often have a multi-period view of future asset changes that can be derived from technical, fundamental, or statistical ...
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Purpose: The multi-period portfolio after closing, can be reviewed and modified at regular intervals. The philosophy behind using multi-period stock portfolio models is that investors often have a multi-period view of future asset changes that can be derived from technical, fundamental, or statistical models. In conventional multi-period portfolio models, it is assumed that the forecast and correction horizons are the same for all assets. However, one asset may be predicted for the one-month horizon and another for the two-month horizon, and may be revised in the future in these periods. The purpose of this study is to present a multi-period stock portfolio model in which assets have different time horizons for correction or an asset can not be traded for the first few periods and then enter the correction cycle.Methodology: In this model, uncertainty variables defined on an uncertainty space are used to describe the returns. The objective function of the model is to maximize the ultimate wealth of the portfolio, and to limit portfolio risk, a constraint is used in which the uncertainty of the ultimate wealth below a threshold is controlled at a confidence level. To find the optimal solution, the model is converted into a form of linear programming by a change of variable method.Findings: After explaining how to model the research portfolio, using a numerical example the model is implemented on two portfolios with 6 and 10 stocks and 4 monthly time steps on the Tehran Stock Exchange.Originality/Value: The present study extends the uncertain multi-period portfolio to a multi-period portfolio with different time horizons and offers an optimal solution through linear programming. In the research stock portfolio, transaction costs are also considered to be more in line with the real conditions.