This page has only limited features, please log in for full access.
Electric power systems are moving toward smarter and more sustainable systems. These trends result in several positive advantages such as active participation of customers in electricity markets. However, resulting demand side flexibilities cause high demand fluctuations and increase the difficulty to maintain the power balance and reliability of smart grids. To address this challenge, this paper proposes a self-partitioning local neuro fuzzy model, which is capable of performing a fast and accurate short-term load forecasting. The proposed model, not only maintains the linearity as well as learning–from-data property via their fuzzy inference systems of local linear neuro fuzzy, but also benefits from partitioning the input space into linear and nonlinear vectors and assigning them separately into different local models. The proposed model is trained with the hierarchical binary-tree learning algorithm and rule premises are calculated through sigmoid partitioning functions. These appealing properties make the model appropriate for a fast and accurate analysis of the load time series featuring both linear and nonlinear characteristics. The effectiveness of the proposed model is compared with recently published forecasting models in terms of statistical performance.
Z. Tavassoli-Hojati; S.F. Ghaderi; H. Iranmanesh; P. Hilber; E. Shayesteh. A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids. Energy 2020, 199, 117514 .
AMA StyleZ. Tavassoli-Hojati, S.F. Ghaderi, H. Iranmanesh, P. Hilber, E. Shayesteh. A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids. Energy. 2020; 199 ():117514.
Chicago/Turabian StyleZ. Tavassoli-Hojati; S.F. Ghaderi; H. Iranmanesh; P. Hilber; E. Shayesteh. 2020. "A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids." Energy 199, no. : 117514.
Uncertainty and risks have been the inherent characteristics of large-scale projects. Although practitioners have applied different project risk management standards, numerous uncertainties, and risks in large-scale construction projects have led to significant failures in fulfilling a project’s goals. Therefore, in this study, a hybrid approach based on failure mode effects analysis (FMEA)/ISO 31000 has been proposed to identify, evaluate, and control the problem effectively. This hybrid approach is not a very accurate approach in providing an appropriate risk response; hence, a mixed-integer programming (MIP) model has been proposed to select the optimized risk response strategies for the project. In the present study, a model based on synergies among project risk responses was developed that is capable of considering the various criteria in the objective function and optimizing them based on the defined projects. Risk response selection for a large-scale project is a complex problem. Because of the nondeterministic polynomial time (NP)-hardness of the presented model, two metaheuristic algorithms, namely, the self-adaptive imperialist competitive algorithm and invasive weed optimization, were developed to solve the proposed MIP model. A large-scale high-rise residential building was evaluated as a case study to investigate the model proposed in this study empirically.
Yaser Rahimi; Reza Tavakkoli-Moghaddam; Seyed Hossein Iranmanesh; Maliheh Vaez-Alaei. Hybrid Approach to Construction Project Risk Management with Simultaneous FMEA/ISO 31000/Evolutionary Algorithms: Empirical Optimization Study. Journal of Construction Engineering and Management 2018, 144, 04018043 .
AMA StyleYaser Rahimi, Reza Tavakkoli-Moghaddam, Seyed Hossein Iranmanesh, Maliheh Vaez-Alaei. Hybrid Approach to Construction Project Risk Management with Simultaneous FMEA/ISO 31000/Evolutionary Algorithms: Empirical Optimization Study. Journal of Construction Engineering and Management. 2018; 144 (6):04018043.
Chicago/Turabian StyleYaser Rahimi; Reza Tavakkoli-Moghaddam; Seyed Hossein Iranmanesh; Maliheh Vaez-Alaei. 2018. "Hybrid Approach to Construction Project Risk Management with Simultaneous FMEA/ISO 31000/Evolutionary Algorithms: Empirical Optimization Study." Journal of Construction Engineering and Management 144, no. 6: 04018043.
Artificial fish swarm algorithm is a technique based on swarm behaviors that are inspired from schooling behaviors of fishes swarm in the nature. Group escaping is another interesting behavior of fish that is ignored. This behavior shows all fish change their moving directions rapidly while some fish sense a predator. In this paper, we proposed a new algorithm which is obtained by hybridizing artificial fish swarm algorithm and group escaping behavior of fish which can greatly speed up the convergence. It is presented proper pseudocode of improved algorithm and then experimental results on Traveling Salesman Problem is applied and demonstrated the advantages of the improved algorithm.
Seyed Hosein Iranmanesh; Fahimeh Tanhaie; Masoud Rabbani. Improving Artificial Fish Swarm Algorithm by Applying Group Escape Behavior of Fish. Advances in Intelligent Systems and Computing 2017, 576, 46 -54.
AMA StyleSeyed Hosein Iranmanesh, Fahimeh Tanhaie, Masoud Rabbani. Improving Artificial Fish Swarm Algorithm by Applying Group Escape Behavior of Fish. Advances in Intelligent Systems and Computing. 2017; 576 ():46-54.
Chicago/Turabian StyleSeyed Hosein Iranmanesh; Fahimeh Tanhaie; Masoud Rabbani. 2017. "Improving Artificial Fish Swarm Algorithm by Applying Group Escape Behavior of Fish." Advances in Intelligent Systems and Computing 576, no. : 46-54.
Dust storm phenomena have vital effects on human life and are significant threat on ecosystem, climate and environmental conditions. Therefore, it may be of vital importance to develop an effective prediction system and mechanism to prevent it and/or reduce its devastating effects. This paper focuses on predicting meteorological conditions associated with dust-storms in the city of Ahvaz, south-western of Iran utilizing local linear neuro fuzzy model with LOLIMOT learning algorithm. For this purpose two different cases are considered. The first case aims to predict the next storm day occurrence and the second case focuses to calculate the number of storm days in next 15 days. The results show that findings under both cases are very close to reality and efficient for predicting dust storm occurrences in Ahvaz city and thus, the methodology could be useful for predicting this event for similar cities as well.
Hossein Iranmanesh; Mehdi Keshavarz; Majid Abdollahzade. Predicting Dust Storm Occurrences with Local Linear Neuro Fuzzy Model: A Case Study in Ahvaz City, Iran. Advances in Intelligent Systems and Computing 2017, 576, 158 -167.
AMA StyleHossein Iranmanesh, Mehdi Keshavarz, Majid Abdollahzade. Predicting Dust Storm Occurrences with Local Linear Neuro Fuzzy Model: A Case Study in Ahvaz City, Iran. Advances in Intelligent Systems and Computing. 2017; 576 ():158-167.
Chicago/Turabian StyleHossein Iranmanesh; Mehdi Keshavarz; Majid Abdollahzade. 2017. "Predicting Dust Storm Occurrences with Local Linear Neuro Fuzzy Model: A Case Study in Ahvaz City, Iran." Advances in Intelligent Systems and Computing 576, no. : 158-167.
During a construction project life cycle, project costs and time estimations contribute greatly to baseline scheduling. Besides, schedule risk analysis and project control are also influenced by the above factors. Although many papers have offered estimation techniques, little attempt has been made to generate project time series data as daily progressive estimations in different project environments that could help researchers in generating general and customized formulae in further studies. This paper, however, is an attempt to introduce a new simulation approach to reflect the data regarding time series progress of the project, considering the specifications and the complexity of the project and the environment where the project is performed. Moreover, this simulator can equip project managers with estimated information, which reassures them of the execution stages of the project although they lack historical data. A case study is presented to show the usefulness of the model and its applicability in practice. In this study, singular spectrum analysis has been employed to analyze the simulated outputs, and the results are separated based on their signal and noise trends. The signal trend is used as a point-of-reference to compare the outputs of a simulation employing S-curve technique results and the formulae corresponding to earned value management, as well as the life of a given project.
Zahra Hojjati Tavassoli; Seyed Hossein Iranmanesh; Ahmad Tavassoli Hojjati. Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis. Algorithms 2016, 9, 45 .
AMA StyleZahra Hojjati Tavassoli, Seyed Hossein Iranmanesh, Ahmad Tavassoli Hojjati. Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis. Algorithms. 2016; 9 (3):45.
Chicago/Turabian StyleZahra Hojjati Tavassoli; Seyed Hossein Iranmanesh; Ahmad Tavassoli Hojjati. 2016. "Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis." Algorithms 9, no. 3: 45.
As a modern kind of engineering management tools, intelligent systems can facilitate better business or organizational decision-making. This chapter discusses several applications of intelligent systems in project management practice. First, the relevant literature is reviewed and different applications of intelligent tools are categorized into seven problem types, i.e. recognizing the relations between activities, estimating duration of activities and project completion time, project scheduling, resource leveling, forecasting project total cost, cash flow/S-curve estimation, and estimating project quality level. This categorization provides the basis for analyzing the underlying problem types and prepares the ground for future research via a faster access to the relevant literature. Then, a real case study and the corresponding results are discussed in order to show the potential usefulness and applicability of such intelligent tools in practice.
Majid Shakhsi-Niaei; Seyed Hossein Iranmanesh. Intelligent Systems in Project Planning. Springer Texts in Business and Economics 2015, 531 -557.
AMA StyleMajid Shakhsi-Niaei, Seyed Hossein Iranmanesh. Intelligent Systems in Project Planning. Springer Texts in Business and Economics. 2015; ():531-557.
Chicago/Turabian StyleMajid Shakhsi-Niaei; Seyed Hossein Iranmanesh. 2015. "Intelligent Systems in Project Planning." Springer Texts in Business and Economics , no. : 531-557.
Over the life cycle of a project, project costs and time estimations play important roles in baseline scheduling, schedule risk analysis and project control. Performance measurement is the ongoing, regular collection of information that can provide this controlling system. In this study, firstly, a new simulation approach is proposed to develop project progress time-series data, based on the complexity and specifications of the project as well as on the environment in which the project is executed. This simulator is capable of simulating fictitious projects, as well as real projects based on empirical data and helps project managers to monitor the project’s execution, despite the lack of historical data. Besides, this chapter compares the effects of different inputs on generated time series, as estimated results obtained on a fictitious dataset. Secondly, the validated outputs can provide researchers with an opportunity to generate general and customized formulae such as project completion time estimation. This study also implies four soft computing methods, Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Interface System (ANFIS), Emotional Learning based Fuzzy Interface System (ELFIS) and Conventional Regression to forecast the completion time of project. Core variables in proposed model are known parameters in Earned Value Management (EVM). Finally, the result of using intelligent models and their performances in modeling the expert emotions are compared.
Seyed Hossein Iranmanesh; Zahra Tavassoli Hojati. Intelligent Systems in Project Performance Measurement and Evaluation. Springer Texts in Business and Economics 2015, 581 -619.
AMA StyleSeyed Hossein Iranmanesh, Zahra Tavassoli Hojati. Intelligent Systems in Project Performance Measurement and Evaluation. Springer Texts in Business and Economics. 2015; ():581-619.
Chicago/Turabian StyleSeyed Hossein Iranmanesh; Zahra Tavassoli Hojati. 2015. "Intelligent Systems in Project Performance Measurement and Evaluation." Springer Texts in Business and Economics , no. : 581-619.
M. Shakhsi-Niaei; S.H. Iranmanesh; S. Ali Torabi. Optimal planning of oil and gas development projects considering long-term production and transmission. Computers & Chemical Engineering 2014, 65, 67 -80.
AMA StyleM. Shakhsi-Niaei, S.H. Iranmanesh, S. Ali Torabi. Optimal planning of oil and gas development projects considering long-term production and transmission. Computers & Chemical Engineering. 2014; 65 ():67-80.
Chicago/Turabian StyleM. Shakhsi-Niaei; S.H. Iranmanesh; S. Ali Torabi. 2014. "Optimal planning of oil and gas development projects considering long-term production and transmission." Computers & Chemical Engineering 65, no. : 67-80.
In factories during production, preventive maintenance (PM) scheduling is an important problem in preventing and predicting the failure of machines, and most other critical tasks. In this paper, we present a new method of PM scheduling in two modes for more precise and better machine maintenance, as pieces must be replaced or be repaired. Because of the importance of this problem, we define multi-objective functions including makespan, PM cost, variance tardiness, and variance cost; we also consider multi-parallel series machines that perform multiple jobs on each machine and an aid, the analytic network process, to weight these objectives and their alternatives. PM scheduling is an NP-hard problem, so we use a dynamic genetic algorithm (GA) (the probability of mutation and crossover is changed through the main GA) to solve our algorithm and present another heuristic model (particle swarm optimization) algorithm against which to compare the GA’s answer. At the end, a numerical example shows that the presented method is very useful in implementing and maintaining machines and devices.
S. Nima Mirabedini; Hossein Iranmanesh. A scheduling model for serial jobs on parallel machines with different preventive maintenance (PM). The International Journal of Advanced Manufacturing Technology 2013, 70, 1579 -1589.
AMA StyleS. Nima Mirabedini, Hossein Iranmanesh. A scheduling model for serial jobs on parallel machines with different preventive maintenance (PM). The International Journal of Advanced Manufacturing Technology. 2013; 70 (9):1579-1589.
Chicago/Turabian StyleS. Nima Mirabedini; Hossein Iranmanesh. 2013. "A scheduling model for serial jobs on parallel machines with different preventive maintenance (PM)." The International Journal of Advanced Manufacturing Technology 70, no. 9: 1579-1589.
This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick–Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT) learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications.
Hossein Iranmanesh; Majid Abdollahzade; Arash Miranian. Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models. Energies 2011, 5, 1 -21.
AMA StyleHossein Iranmanesh, Majid Abdollahzade, Arash Miranian. Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models. Energies. 2011; 5 (1):1-21.
Chicago/Turabian StyleHossein Iranmanesh; Majid Abdollahzade; Arash Miranian. 2011. "Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models." Energies 5, no. 1: 1-21.
This paper proposes a comprehensive framework for project selection problem under uncertainty and subject to real-world constraints, like segmentation, logical, and budget constraints. The framework consists of two main phases. In the first phase, the candidate projects are ranked considering the uncertainty, through a Monte Carlo simulation linked to a multi-criteria approach. In the second phase, the overall complete preorder of the projects in different iterations is first determined and then used in another Monte Carlo simulation linked to an integer programming module in order to effectively drive the final portfolio selection while satisfying the budget, segmentation and other logical constraints. The proposed framework is implemented in a case study to show its usefulness and applicability in practice. Finally, a comparison is carried out between the proposed approach and its deterministic counterpart and the corresponding results are discussed.
M. Shakhsi-Niaei; S.A. Torabi; S.H. Iranmanesh. A comprehensive framework for project selection problem under uncertainty and real-world constraints. Computers & Industrial Engineering 2011, 61, 226 -237.
AMA StyleM. Shakhsi-Niaei, S.A. Torabi, S.H. Iranmanesh. A comprehensive framework for project selection problem under uncertainty and real-world constraints. Computers & Industrial Engineering. 2011; 61 (1):226-237.
Chicago/Turabian StyleM. Shakhsi-Niaei; S.A. Torabi; S.H. Iranmanesh. 2011. "A comprehensive framework for project selection problem under uncertainty and real-world constraints." Computers & Industrial Engineering 61, no. 1: 226-237.