Publication's Abstract
A. Journal
A.1. Published Articles
Flexible job-shop scheduling problem considering machine and order acceptance, transportation costs, and setup times.
This paper, for the first time, studied a new extension of the flexible job shop scheduling problem by assuming the acceptance and rejection of machines and orders. The flexible job shop problem was extended to implement production without a factory in natural environments. Therefore, the mixed-integer linear programming (MILP) model was developed for this problem aiming to minimize total costs, including the fixed cost of machine selection, variable operational cost, transportation cost, and order rejection cost. Due to the high complexity of this problem, a heuristic algorithm was employed to find an acceptable solution. For algorithm performance evaluation, 40 samples were randomly generated and solved using the mathematical model and the proposed algorithm. The results of analyzing random samples showed a negligible error rate indicating algorithm efficiency.
Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize.
This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The objective functions include minimising costs, CO2 emissions, energy consumption, and maximising employment by applying Conditional Value at Risk (CVaR) to achieve reliability through risk reduction. The Entropic Value at Risk (EVaR) and Minimax method are used to compare with the proposed model. We utilise the Lp-Metric method to solve the multiobjective problem. Since this model is complex, the Lagrange relaxation and Fix-and-Optimise algorithm are applied to find lower and upper bounds in large-scale, respectively. The results confirm the superior power of the model offered in estimating costs, energy consumption, environmental pollution, and employment level. This model and algorithms are applicable for other CLSC problems.
Providing ZBWM group model based on DEA and ZLCWAA methods approach, (In Persian).
Objective: Considering that in most specialized fields, decisions are made in groups, so in this study, a method to select the desired option in conditions of uncertainty and to increase the effectiveness of group decisions is presented.
Method: In this study, ZlCWAA and data envelopment analysis methods were used to create the best-worst Z numbers method, which is responsible for the averaging of Z numbers and assigning weight to specialists, respectively. The best-worst Z numbers method is innovative in the field of decision making, and in this study, the best-worst Z numbers group method has been used.
Conclusion: To prove the effectiveness of the DEA-GZBWM method, a case study has been conducted to show how to use this method in selecting the optimal stock portfolio; In which the investor, with the help of financial experts (experts), invests and selects the optimal stock portfolio from among the companies in the stock exchange and securities organization. Then, the results of the proposed method and FBWM and ZBWM methods were compared based on weight, rank, and rate of incompatibility. This comparison showed that this proposed method has a better incompatibility rate (0.108) than other methods.
A.2. Under Review Articles
Formation of Optimal Multi-Period Mean-Semivariance Portfolio with Fuzzy Interest Rates: Case Study on the New York Stock Exchange.
This study aimed to find a mechanism for allocating assets between a desired collection of stocks with maximum return and minimum risk at the same time. It was also tried to use the uncertain interest rate for each stock to optimize the portfolio. To this end, a bi-objective multi-period mean-semivariance was first used to model the major problem. Then, the model was investigated under the primary state by considering the existing constraints. This study investigated the effect of an uncertain and fuzzy interest rate on portfolio composition in the New York Stock Exchange. The results showed that the multi-period mean-semivariance model with a fuzzy interest rate can be used for portfolio optimization. Portfolios composed by this model are more efficient. Due to uncertain interest rate, such portfolios are more optimal than the portfolios composed by other methods and algorithms.
Resource-constrained time-cost-quality-energy-environment tradeoff in project scheduling by considering blockchain technology: A case study of healthcare project.
Blockchain Technology (BCT) is expanding day by day and is used in all pillars of life and projects. In this research, we survey applicable of BCT in project management for the first time. We presented a Resource-Constrained Time-Cost-Quality-Energy-Environment tradeoff problem in project scheduling by considering BCT (RCTCQEEBCT). We utilize hybrid robust stochastic programming, worst case and Conditional Value at Risk (CVaR) to cope with uncertainty and risks. This type of robustification and risk-averse is presented in this research. A real case study is presented in a healthcare project. We utilize GAMS-CPLEX to solve the model. Finally, we analyze finish time, conservative coefficient, the confidence level of CVaR and the number of scenarios. The most important research result is that applying BCT decreases cost, energy, and pollution and increases quality. Moreover, the total gap between RCTCQEEBCT and without BCT is approximately 2.6%. When compacting finish time happens or if the conservative coefficient increases to 100%, costs, energy, and pollution environment increase, but quality decreases. If the confidence level of CVaR increase, the cost, energy and environment function functions grow up and quality is approximately not changed.
Predictable Maintenance: A Bayesian Network-based Model.
The increasing progress of industries and their complexity has made the maintenance and repairing tasks very challenging, complex, and time-consuming. This paper develops a new maintenance prediction model based on the capabilities of Bayesian networks (BN). The models include several variables that experts determine and the influence on each other’s-called conditional probability tables-which are learned from historical data. The model is implemented in a case study of the automobile repair department to show its performance. The model is evaluated through a sensitivity analysis, and the results show the proficiency of the proposal mode.
Surveying the Effect of COVID-19 On the Tehran Stock Exchange.
The aim of us from preparing and researching this paper is to study the effect of Covid 19 patient rate changes and mortality on the Tehran stock market. The global economy and some stock markets reacted quickly to this pandemic, and some collapsed first. The first case was officially announced in Iran on February 20, 2020. By using daily data from the rate of patient and mortality rates from WHO published and return of companies registered at Tehran stock market exchange (TSE) from 19-02-2020 to 08-09-2021 by using ARCH and GARCH models, we want to survey these effects on TSE. This paper has tested the effect of the Covid 19 outbreak on the volatiles inactive companies at TSE. The results show that Covid 19 has negatively affected returns of TSE companies’ prices in both tests.
B. Conference
A.1. Published Articles
A combined deep CNN-LSTM Network for classifying T1 weighted Magnetic Resonance Brain tumor, (In Persian).
One of the main challenges in treating tumors and assessing disease progression is diagnosing tumor size And distinguish tumor types from each other. Manual tumor segmentation in three-dimensional Magnetic Resonance images (volume MRI) is a time-consuming and tedious task. Its accuracy depends heavily on the operator’s experience doing it. The need for an accurate and fully automatic method for segmenting brain tumors and measuring tumor size is strongly felt. This paper first uses a combined CNN-LSTM method to detect HG and LG tumors in 3D brain images. Then it used the UNET Neural Network to improve the location of the tumor in the brain. In this article, we use BRATS 2018 database images, And manual segmentation is used as the Grand truth. in this paper, we showed that the proposed method could effectively perform segmentation.
Viable closed-loop supply chain network design: a case study in automotive industry.
This research suggested a viable closed-loop supply chain network design that considers resiliency, sustainability, and agility for supply chain network design for the first time. The previous section shows a lack of research in resilience, sustainability and agile CLSCND. In the present study, we have customers, retailers, manufacturers, suppliers, recovery center, repairing center, disposal center and second customer. We present VCLSCND through (flexible capacity (resilience strategy), sustainability constraints, and agility. Finally, we add uncertainty through the robust scenario by defining a new form of the objective function. The results of this research show that when agility coefficient increase, cost function grow up. Moreover, when conservative coefficient increase, cost function grow up in Figure 3, too. Eventually, the cost function grows when maximum emission decreases because the model should activate a new facility. As a result, the cost function increase. The variation on the availability coefficient is considered, and we found that by decreasing the availability coefficient, the cost function increases because the model should activate a new facility.