International Journal of Simulation and Process Modelling
Treatment process in health centres is mostly carried out in such a way that the patient is hospi... more Treatment process in health centres is mostly carried out in such a way that the patient is hospitalised in several parts of the wards during treatment. When patients are obliged to stay in a department until a bed becomes available in the next department, 'bed-blocking' occurs. Hospitals often suffer from the lack of proper planning and ineffective management of hospital beds and medical resources. In this research, we tackle this issue by introducing a multi-objective simulation optimisation model to determine the optimal number of elective admissions, the appropriate number of beds, and the number of patients who are forced to discharge in each ward. The model minimises hospital costs and the number of patients who are blocked. Due to the complexity of the issue, two meta-heuristic approaches are developed to solve the model. In this regard, five simulation optimisation algorithms have been implemented to solve the model and compare the results. By solving the presented algorithms, Pareto optimal frontiers are obtained in various states.
Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz... more Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in th...
The problem investigated in this paper is one of the simultaneous lot-sizing and scheduling probl... more The problem investigated in this paper is one of the simultaneous lot-sizing and scheduling problems with earliness/tardiness penalties along with the sequence-dependent setup time and cost. Minimising earliness and tardiness is of interest both in just-in-time production systems and in make-to-order ones. A mathematical model is presented similar to the travelling salesman problem. The problem objective is to determine the production lot-size and their schedules. In spite of its wide applications, this problem has not yet been reported in the literature. To solve the problem, two meta-heuristic algorithms: tabu search and ant colony system are presented. The efficiencies of the algorithms are investigated for different sets of problems. The results show that each of the ant colony system and the tabu search obtained optimal solutions for 196 and 203 problems, respectively, out of among the 333 problem instances. Despite the differences between these two methods, neither can be statistically preferred.
Consumption forecasting is a critical issue in commodity markets on which financial decision-make... more Consumption forecasting is a critical issue in commodity markets on which financial decision-makers depend for accuracy. To adequately handle the complexity and uncertainty associated with real-world market problems, forecasting needs to be capable of handling complex situations. The steel industry is a strategic one for Iran playing a critical role in the national economy. Using time series models, this study aims to forecast the future trend of Iran’s crude steel consumption. Although autoregressive integrated moving average (ARIMA) models are regarded as the most important time series models and are extensively employed in forecasting financial markets, they are hampered by certain limitations that detract from their popularity. They are based on the assumption that a linear relationship holds between future values of a time series and its current and past values. Moreover, they depend heavily on a large amount of historical data to provide the desired results. To overcome the limitations in such conventional models, fuzzy autoregressive integrated moving average models have been proposed as improved versions of the ARIMA models. Unfortunately, the former are also plagued by very wide forecasted intervals in cases where there are outliers that create instability in the data. The present paper proposes a hybrid model which is a combination of computational intelligence tools and soft computing techniques. In such a form they take advantage of their unique properties which, when exploited, can provide more accurate financial forecasts. The main objective of the proposed model is to identify nonlinear patterns with probabilistic classifiers to obtain narrower intervals than would be otherwise possible under the traditional FARIMA models. The empirical results obtained from applying the proposed model to forecasting Iran’s steel consumption provide significantly improved accuracy.
International Journal of Planning and Scheduling, 2016
Lot-sizing and scheduling are two important issues in production planning problems. This study co... more Lot-sizing and scheduling are two important issues in production planning problems. This study considers the simultaneous lot-sizing and scheduling problem in a capacitated flow shop environment with outsourcing. We propose a new exact formulation for the simultaneous lot-sizing and scheduling problem in a flow shop environment with sequence dependent setups as a mixed integer program. For demonstrating the efficiency of the proposed model, we compare it with a former model pertaining to a flow shop environment. The proposed model's efficiency is better than the former model because the number of continuous and binary variables, the number of constraints, and the solving CPU time are less than the former model. Since finding the exact solution for medium- and large-size instances is impossible within a reasonable time due to the complexity of the problem, four MIP-based rolling horizon heuristics are provided. The computational results show the effectiveness of heuristic algorithms.
2015 International Conference on Industrial Engineering and Operations Management (IEOM), 2015
In this paper, minimizing total weighted tardiness in single machine problem has been considered.... more In this paper, minimizing total weighted tardiness in single machine problem has been considered. Jobs have different size, also batch processing assumption is considered. We developed a new Mixed Integer Linear Programming (MILP) to the problem. The model solves the problem faster than previous model; due to the proposed model restricted the solution space. Some instances problems are generated in order to evaluate the proposed model. Comparing the solution time of the proposed model with the old model shows the efficiency of the new model. Computational result is shown that the proposed model decrease CPU time at more than 90% instances. In some instance CPU time decreased about 70%.
Journal of Industrial and Systems Engineering, 2011
Both theoretical and empirical findings have suggested that combining different models can be an ... more Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. ...
Journal of Mathematical and Computational Science, 2012
Forecasting accuracy is one of the most important features of forecasting models; hence, never ha... more Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models ...
... An artificial neural network (p, d, q) model for timeseries forecasting. ... Chakraborty, Meh... more ... An artificial neural network (p, d, q) model for timeseries forecasting. ... Chakraborty, Mehrotra, Mohan, and Ranka (1992) conduct an empirical study on multivariate time series forecasting with ... that neural networks can be a very useful addition to the time series forecasting toolbox. ...
International Journal of Simulation and Process Modelling
Treatment process in health centres is mostly carried out in such a way that the patient is hospi... more Treatment process in health centres is mostly carried out in such a way that the patient is hospitalised in several parts of the wards during treatment. When patients are obliged to stay in a department until a bed becomes available in the next department, 'bed-blocking' occurs. Hospitals often suffer from the lack of proper planning and ineffective management of hospital beds and medical resources. In this research, we tackle this issue by introducing a multi-objective simulation optimisation model to determine the optimal number of elective admissions, the appropriate number of beds, and the number of patients who are forced to discharge in each ward. The model minimises hospital costs and the number of patients who are blocked. Due to the complexity of the issue, two meta-heuristic approaches are developed to solve the model. In this regard, five simulation optimisation algorithms have been implemented to solve the model and compare the results. By solving the presented algorithms, Pareto optimal frontiers are obtained in various states.
Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz... more Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in th...
The problem investigated in this paper is one of the simultaneous lot-sizing and scheduling probl... more The problem investigated in this paper is one of the simultaneous lot-sizing and scheduling problems with earliness/tardiness penalties along with the sequence-dependent setup time and cost. Minimising earliness and tardiness is of interest both in just-in-time production systems and in make-to-order ones. A mathematical model is presented similar to the travelling salesman problem. The problem objective is to determine the production lot-size and their schedules. In spite of its wide applications, this problem has not yet been reported in the literature. To solve the problem, two meta-heuristic algorithms: tabu search and ant colony system are presented. The efficiencies of the algorithms are investigated for different sets of problems. The results show that each of the ant colony system and the tabu search obtained optimal solutions for 196 and 203 problems, respectively, out of among the 333 problem instances. Despite the differences between these two methods, neither can be statistically preferred.
Consumption forecasting is a critical issue in commodity markets on which financial decision-make... more Consumption forecasting is a critical issue in commodity markets on which financial decision-makers depend for accuracy. To adequately handle the complexity and uncertainty associated with real-world market problems, forecasting needs to be capable of handling complex situations. The steel industry is a strategic one for Iran playing a critical role in the national economy. Using time series models, this study aims to forecast the future trend of Iran’s crude steel consumption. Although autoregressive integrated moving average (ARIMA) models are regarded as the most important time series models and are extensively employed in forecasting financial markets, they are hampered by certain limitations that detract from their popularity. They are based on the assumption that a linear relationship holds between future values of a time series and its current and past values. Moreover, they depend heavily on a large amount of historical data to provide the desired results. To overcome the limitations in such conventional models, fuzzy autoregressive integrated moving average models have been proposed as improved versions of the ARIMA models. Unfortunately, the former are also plagued by very wide forecasted intervals in cases where there are outliers that create instability in the data. The present paper proposes a hybrid model which is a combination of computational intelligence tools and soft computing techniques. In such a form they take advantage of their unique properties which, when exploited, can provide more accurate financial forecasts. The main objective of the proposed model is to identify nonlinear patterns with probabilistic classifiers to obtain narrower intervals than would be otherwise possible under the traditional FARIMA models. The empirical results obtained from applying the proposed model to forecasting Iran’s steel consumption provide significantly improved accuracy.
International Journal of Planning and Scheduling, 2016
Lot-sizing and scheduling are two important issues in production planning problems. This study co... more Lot-sizing and scheduling are two important issues in production planning problems. This study considers the simultaneous lot-sizing and scheduling problem in a capacitated flow shop environment with outsourcing. We propose a new exact formulation for the simultaneous lot-sizing and scheduling problem in a flow shop environment with sequence dependent setups as a mixed integer program. For demonstrating the efficiency of the proposed model, we compare it with a former model pertaining to a flow shop environment. The proposed model's efficiency is better than the former model because the number of continuous and binary variables, the number of constraints, and the solving CPU time are less than the former model. Since finding the exact solution for medium- and large-size instances is impossible within a reasonable time due to the complexity of the problem, four MIP-based rolling horizon heuristics are provided. The computational results show the effectiveness of heuristic algorithms.
2015 International Conference on Industrial Engineering and Operations Management (IEOM), 2015
In this paper, minimizing total weighted tardiness in single machine problem has been considered.... more In this paper, minimizing total weighted tardiness in single machine problem has been considered. Jobs have different size, also batch processing assumption is considered. We developed a new Mixed Integer Linear Programming (MILP) to the problem. The model solves the problem faster than previous model; due to the proposed model restricted the solution space. Some instances problems are generated in order to evaluate the proposed model. Comparing the solution time of the proposed model with the old model shows the efficiency of the new model. Computational result is shown that the proposed model decrease CPU time at more than 90% instances. In some instance CPU time decreased about 70%.
Journal of Industrial and Systems Engineering, 2011
Both theoretical and empirical findings have suggested that combining different models can be an ... more Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. ...
Journal of Mathematical and Computational Science, 2012
Forecasting accuracy is one of the most important features of forecasting models; hence, never ha... more Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models ...
... An artificial neural network (p, d, q) model for timeseries forecasting. ... Chakraborty, Meh... more ... An artificial neural network (p, d, q) model for timeseries forecasting. ... Chakraborty, Mehrotra, Mohan, and Ranka (1992) conduct an empirical study on multivariate time series forecasting with ... that neural networks can be a very useful addition to the time series forecasting toolbox. ...
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Papers by Mehdi Bijari