جستجوی مقالات مرتبط با کلیدواژه « Non-dominated sorting genetic algorithm » در نشریات گروه « صنایع »

Pair formation is an important step in pair trading that has only been examined manually or through numerical instructions. These methods fail in the multivariate mode and do not consider conflicting goals in the problem structure. In this research, a method is presented to create multivariate pair combinations by considering contradictory multiple goals in stock pair trading.

In this study, the statistical sample is limited to the top 30 companies listed on the Tehran Stock Exchange due to the need for high-frequency transactions. The problem is developed in the form of a Mixed Integer Programming (MIP) model, and due to non-convex constraints and exponential solution space, a multi-objective genetic algorithm is used to obtain multivariate pair combinations. To achieve multiple goals, the developed type of genetic algorithm, namely, The Chaotic Non-dominated Sorting Genetic Algorithm (CNSGA-II), was used. In this method, chaos theory is used to create the initial population of the genetic algorithm in order to obtain appropriate and high-precision solutions.

The results showed that the use of chaos theory could increase the degree of convergence in evolutionary algorithms. In addition, these results indicate the superiority of the multi-objective pair trading strategy based on the distance approach over the traditional single-objective model.

Originality/Value: 

In order to optimize pair trading, the Non-dominated Sorting Genetic Algorithm was used. Also, the initial population of individuals was created in a multi-objective genetic algorithm based on chaos theory.

Due to the competitiveness of the market, manufacturers have been forced to increase their activity effectiveness and efficiency. The shortening of the life cycle and the period of product supply to the market have forced manufacturers to increase the efficiency of their activities and production processes. As regards, the scheduling process and sequencing of efficient operations in manufacturing environments is one of the strategic issues for survival in the competitive market. Workshop environments such as job shop and flow shop are used in many industrial and service processes. One of the most challenging scheduling problems is the open shop scheduling one, but researches in this realm have not paid much attention to human resources. When there is no limit to the processing route of any job on shop machines, this model is referred to as an open shop. The open shop scheduling problem is a strategic issue. However, in most of available schedules in the literature, only workshop equipment, such as machines, is considered as limited resources, but in reality we are confronted with limited human and machine resources. In this study, a mixed-integer programming model is presented for the bi-objective open shop scheduling problem with limited human and machine dual resources. Small-sized problems are solved by using the exact epsilon-constraint method. According to the Np-hardness of this problem, two pareto-based meta-heuristics algorithms were used which are the Non-Dominated Sorting Genetic Algorithm (NSGAII) and Multi-objective Vibration Damping optimization (MOVDO). In order to analyze and compare the algorithms, we used four different indicators which include: The number of members of the first Pareto front, mean of ideal distance and diversity and spacing measures. Also, 30 problems in three scales (small, medium, large) have been generated. The computational results shows that the NSGAII is more functional and has better output in comparison to the other presented algorithm.

One of the reasons for the high waiting time for patients in hospitals is the the lack of sufficient staff in the hospital, so the inefficiency of costs and job satisfaction of hospital nursing staff stems from the use of traditional and unscientific methods in allocating nurses to shifts. The present study is designed to determine the minimum number of nurse required according to the number of patients referred at different times, determine the shift schedule with the least required hours and schedule shifts for nurses in each shift with the lowest cost for the emergency department. The research method of the present study is of the mathematical modeling and research community, patients referring to the emergency department and nurses of a medical center. Data analysis is a combination of predictive methods, queuing theory models, and linear numerical programming. To predict the number of patients referring to the emergency, the time series method and ARIMA tools were used, and the M/M/C/K model was used to examine the queue system with limited capacity. One of the most important results of this study is to determine the maximum number of nurses available in each shift. Another result of this study is the comparison of the performance of each of the meta-heuristic Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Bee Algorithm (BA) with respect to the defined indicators.

One of the reasons for the high waiting time for patients in hospitals is the the lack of sufficient staff in the hospital, so the inefficiency of costs and job satisfaction of hospital nursing staff stems from the use of traditional and unscientific methods in allocating nurses to shifts. The present study is designed to determine the minimum number of nurse required according to the number of patients referred at different times, determine the shift schedule with the least required hours and schedule shifts for nurses in each shift with the lowest cost for the emergency department. The research method of the present study is of the mathematical modeling and research community, patients referring to the emergency department and nurses of a medical center. Data analysis is a combination of predictive methods, queuing theory models, and linear numerical programming. To predict the number of patients referring to the emergency, the time series method and ARIMA tools were used, and the M/M/C/K model was used to examine the queue system with limited capacity. One of the most important results of this study is to determine the maximum number of nurses available in each shift. Another result of this study is the comparison of the performance of each of the meta-heuristic Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Bee Algorithm (BA) with respect to the defined indicators.