m. ehsanifar

Analyzing financial ratios over consecutive years is beneficial for evaluating the financial performance of construction companies. However, such an analysis can be tedious due to the vast number of the ratios. Therefore, developing an expert system based on artificial intelligence algorithms to identify and predict factors influencing the construction companies' financial performance is essential. To this end, a hybrid model based on Genetic Algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) was introduced in this research to predict the financial performance of construction companies in Iran. This research is applied as descriptive and in terms of methodology well developed; also conducted cross-sectionally. The statistical population included all active construction companies in the construction sector in Tehran. Due to time and resource constraints, a random sampling technique was used. A questionnaire was utilized for data collection and data analysis, factor analysis methods and neuro-fuzzy system combined with GA were employed. The ANFIS combined with GA can evaluate the construction companies' financial performance with the minimum error. The findings ultimately resulted development of a model that forecasts the financial performance of Iranian construction companies, allowing them to concentrate on factors that improve financial performance.

Along with the emergence of new technologies in recent years, risks and uncertainties have become more complex. Although Building Information Modelling (BIM) is a promising method in the construction industry that can mitigate numerous risks, it also introduces new risks as an innovative technology. Therefore, improving risk management through BIM for the construction works is of great importance. The present study utilized a four-stage combined approach for this purpose. Initially, the most significant risks in BIM implementation in the construction industry were identified. Subsequently, the causal relationships among the risks were identified using the DEMATEL method, and the importance of each risk was determined using the Analytic Network Process (ANP). Finally, a mathematical model was developed to allocate responses to the risks, and the mathematical model was solved using the Invasive Weed Optimization (IWO) and Grey Wolf Optimizer (GWO) algorithms. The analysis revealed that the objective function value obtained through the Grey Wolf Optimizer method was consistently more favourable compared to the Invasive Weed Optimization method. Ultimately, the findings indicated that the proposed approach serves as a powerful tool for project managers to assess risks and predict appropriate responses for the risks of BIM implementation.

Successful civil projects are one of the key factors in the economic development. Civil projects are always criticized for time and budget wastage. Delays in the execution of these projects sometimes not only waste national resources and cause social damages, but also may ultimately render the project economically unjustifiable, to the extent that the direct and indirect damages from delays can sometimes exceed the actual value of the project. However, despite all government efforts, projects in Iran still suffer from delays. Therefore, managing the delays in construction projects in Iran for optimal utilization and proper management is essential. This research proposes a delay management model for construction projects based on evaluating the effective factors on the delay of construction projects in Iran using system dynamics and optimization methods. Finally, a model suitable for the managerial situation in top-ranked contracting companies in Tehran province was presented. The validation and credibility assessment of the proposed model in managing the delay of construction projects in Iran indicated that, by combining these two approaches, project managers can improve project performance under dynamic system conditions. This innovation can be utilized in different industries, especially in the construction industry. In fact, this study provides an optimal response to the management of delays in construction projects by changing the planning horizon using integrated tracking methods and identifying the best strategy and planning horizon for policymakers. Through this, an optimal planning horizon for managing delays in construction projects in the real world is achieved.

Nowadays supply chain sustainability efforts are continuing to grow globally.  While the focus of typical supply chain management mostly is on the speed, cost and reliability of operations, sustainable supply chain management adds the goals of upholding environmental, financial and social responsibilities. Although the worth of supply chain management in large-scale projects, in particular, the construction industry has been recognized currently, its execution remains subject to significant challenges. One of the main challenges is the complexity of the sustainable suppliers’ selection. They can enhance the profitability and success of the project-oriented construction organizations. In this respect, this article aims to apply fuzzy Utilities-Additives STAR (UTASTAR), the extended Utilities Additives Method for multiple criteria decision, to select sustainable suppliers and evaluate general utility function of the problem. Finally, since this method belongs to compensatory multi-criteria decision-making models the results obtained through the application of the fuzzy UTASTAR were compared with some selected models from the compensatory subgroup. According to comparative results, fuzzy UTASTAR can identify deviation from actual utility of alternatives using upper and lower estimations. As a result, this study shows that the rankings produced by the fuzzy UTASTAR method are more trustworthy for selecting suppliers in ensuring the sustainable development.

The Quadratic Assignment Problem (QAP) is one of the problems of combinatorial optimization belonging to the NP-hard problems’ class and has a wide application in the placement of facilities. Thus far, many efforts have been made to solve this problem and countless algorithms have been developed to achieve the optimal solutions; one of which is the Simulated Annealing (SA) algorithm. This paper aims at finding a suitable layout for the facilities of an industrial workshop by using a Developed Simulated Annealing (DSA) method.

Nowadays, with extending applications of bi-layer metallic sheets in different industrial sectors, accurate specification of each layer is very prominent to achieve desired properties. In order to predict behavior of sheets under different forming modes and determining rupture limit and necking, the concept of Forming Limit Diagram (FLD) is used. Optimization problem with objective functions and important parameters aims to find optimal thickness for each of Al3105-St14 bi-layer metallic sheet contributors. Optimized point is achieved where formability of the sheet approaches to maximum extent and its weight to minimum extent. In this paper, multi-objective Tabu search algorithm is employed to optimize the considered problem. Finally, derived Pareto front using Tabu search algorithm is presented and results are compared with the solutions obtained from genetic algorithm. Comparison revealed that Tabu search algorithm provides better results than genetic algorithm in terms of Mean Ideal Distance, Spacing, non-uniformity of Pareto front and CPU time.