A new memetic algorithm for mitigating tandem automated guided vehicle system partitioning problem

Abstract Automated Guided Vehicle System (AGVS) provides the flexibility and automation demanded by Flexible Manufacturing System (FMS). However, with the growing concern on responsible management of resource use, it is crucial to manage these vehicles in an efficient way in order reduces travel time and controls conflicts and congestions. This paper presents the development process of a new Memetic Algorithm (MA) for optimizing partitioning problem of tandem AGVS. MAs employ a Genetic Algorithm (GA), as a global search, and apply a local search to bring the solutions to a local optimum point. A new Tabu Search (TS) has been developed and combined with a GA to refine the newly generated individuals by GA. The aim of the proposed algorithm is to minimize the maximum workload of the system. After all, the performance of the proposed algorithm is evaluated using Matlab. This study also compared the objective function of the proposed MA with GA. The results showed that the TS, as a local search, significantly improves the objective function of the GA for different system sizes with large and small numbers of zone by 1.26 in average

Impact of Computer Game on Incidence of Behavioral Disorders among Male Elementary School Students

Introduction Behavioral disorders, are the most common and debilitating diseases that affect a variety of functions, especially on children's school performance, and create many problems for teachers, parents and children, and have negative effects on their learning, communication, and social performance. These disorders can be observed in the early years of primary school and between the ages of 8 and 15 years to reach their peak. Previous studies on the behavioral problems in Iran, reported the prevalence of these disorders is very high among elementary school students and about 10 to 31%. This study examines four types of behavioral disorders such as, conduct disorder, delinquent tendencies, restlessness, and distraction. Etiology of behavioral disorder is very complex. One of the risk factors in incidence and prevalence of behavioral disorders, which has attracted much attention in recent years, is the use of computer games. However, few studies have been done on the social and psychological effects of video games on Iran, little research has been conducted on the effects of video games on the incidence of behavioral disorders in children. This study aimed to determine the relationship between computer games and behavioral disorders such as conduct disorder, delinquent tendencies, restlessness, and distraction.   Materials and Methods   This study was a cross-sectional case-control study. Data were collected from 314 primary school students in the academic year 2013 and 2014 in Yazd city. These students were selected using multi-stage cluster sampling method. Measuring tools were: questionnaire of doing computer games, and behavioral disorder appraisal test. Data were analyzed by using of MANOVA method.   Discussion of Results and Conclusions Three hundred and fourteen students from third grade to sixth grade studied. Students were distributed: 27.1% in the third grade, 28.4% in fourth grade, 23.2%t in fifth grade and 21.2% in the sixth grade of primary school. Most students (95.2%) have computer at home. There was significant difference between students interested in computer games sports for delinquent tendencies. The prevalence of delinquent tendencies were more common among students who play sports. Also, the prevalence of conduct disorder was higher among students who play adventure computer games, cars and motorcycles. Our results showed that the prevalence of restless disorder was higher in students who were doing war computer games. Also, there was significant difference between students interested in computer games events, cars and motorcycles regarding to conduct disorder. Our results showed a significant difference between students with different levels of computer games regarding to many behavioral disorders. Behavioral problems among students was significantly associated with the duration of the computer game. There was significant difference between students with different levels of computer games, regarding to conduct disorder, restlessness disorder, and distraction disorder. About the mechanism of the effect of computer games on behavioral disorders, there are three basic hypotheses. Arousal hypothesis holds, monitor of electronic media, may be less attractive other activities, such as work or school. Depersonalized theory, believes that the use of computer games could be depersonalization process. During this process, the child is affected by the excitement of the game, trying to destroy his rival, forget itself, and does not pay attention to what others do not even hear them. This process can lead to impaired attention deficit, distraction, emotional and anxiety, and conduct disorders in children and adolescents. According to the theory of displacement, time spent on the computer and video games may simply replace the time that is spent on other activities, that are activities that control growth of abnormal behavior and behavior to provide impulse control. Due to the growing use of computer games, easy access to the games in our society can be one of the causes of behavioral disorders in children. Therefore, it is necessary, parents prevent children from engaging in too much computer games and make them aware of the negative consequences of excessive use of computer games

A Framework for Open-Pit Mine Production Scheduling under Semi-Mobile In-Pit Crushing and Conveying Systems with the High-Angle Conveyor

In-pit crushing and conveying (IPCC) systems have drawn attention to the modern mining industry due to the numerous benefits than conventional truck-and-shovel systems. However, the implementation of the IPCC system can reduce mining flexibility and introduce additional mining sequence requirements. This paper investigates the long-term production scheduling and the crusher relocation plan of open-pit mines using a semi-mobile IPCC system and high-angle conveyor. A series of candidate high-angle conveyor locations is generated around the pit limit, with a crusher located along each conveyor line. Each conveyor location is solved independently by an integer linear programming model for making production scheduling and crushing station decisions, aiming to maximize the net present value (NPV) considering the material handling and crushing station relocation costs. The production schedule with the highest NPV and the associated conveyor and crusher location is considered the optimum or near-optimum solution

A Clustering Algorithm for Block-Cave Production Scheduling

 Production scheduling is one of the most important steps in the block-caving design process. Optimum production scheduling could add significant value to a mining project. The goal of long-term mine production scheduling is to determine the mining sequence, which optimizes the company’s strategic objectives while honouring the operational limitations over the mine life. Mathematical programming with exact solution methods is considered a practical tool to model block-caving production scheduling problems; this tool makes it possible to search for the optimum values while considering all of the constraints involved in the operation. This kind of model seeks to account for real-world conditions and must respond to all practical problems which extraction procedures face. Consequently, the number of subjected constraints is considerable and has tighter boundaries, solving the model is not possible or requires a lot of time. It is thus crucial to reduce the size of the problem meaningfully by using techniques which ensure that the absolute solution has less deviation from the original model. This paper presents a clustering algorithm to reduce the size of the large-scale models in order to solve the problem in a reasonable time. The results show a significant reduction in the size of the model and CPU time. Application and comparison of the production schedule based on the draw control system with the clustering technique is presented using 2,487 drawpoints to be extracted over 32 years

Optimum Fleet Selection Using Machine Learning Algorithms—Case Study: Zenouz Kaolin Mine

This paper presents the machine learning (ML) method, a novel approach that could be a profitable idea to optimize fleet management and achieve a sufficient output to reduce operational costs, by diminishing trucks’ queuing time and excavators’ idle time, based on the best selection of the fleet. The performance of this method was studied at the Zenouz kaolin mine to optimize the type of loader and the number of trucks used to supply the processing plant’s ore demands. Accordingly, five years’ data, such as dates, weather conditions, number of trucks, routes, loader types, and daily hauled ore, were collected, adapted, and processed to train the following five practical algorithms: linear regression, decision tree, K-nearest neighbour, random forest, and gradient boosting algorithm. By comparing the results of the algorithms, the gradient boosting decision tree algorithm was determined to be the best fit and predicted test data values with 85% accuracy. Subsequently, 11,322 data were imported into the machine as various scenarios and daily hauled minerals as output results were predicted for each working zone individually. Finally, the data which had the minimum variation from the selected required scheduled value, and its related data concerning loader type and the number of demanded trucks, were indicated for each day of the working year

Optimum Fleet Selection Using Machine Learning Algorithms—Case Study: Zenouz Kaolin Mine

This paper presents the machine learning (ML) method, a novel approach that could be a profitable idea to optimize fleet management and achieve a sufficient output to reduce operational costs, by diminishing trucks’ queuing time and excavators’ idle time, based on the best selection of the fleet. The performance of this method was studied at the Zenouz kaolin mine to optimize the type of loader and the number of trucks used to supply the processing plant’s ore demands. Accordingly, five years’ data, such as dates, weather conditions, number of trucks, routes, loader types, and daily hauled ore, were collected, adapted, and processed to train the following five practical algorithms: linear regression, decision tree, K-nearest neighbour, random forest, and gradient boosting algorithm. By comparing the results of the algorithms, the gradient boosting decision tree algorithm was determined to be the best fit and predicted test data values with 85% accuracy. Subsequently, 11,322 data were imported into the machine as various scenarios and daily hauled minerals as output results were predicted for each working zone individually. Finally, the data which had the minimum variation from the selected required scheduled value, and its related data concerning loader type and the number of demanded trucks, were indicated for each day of the working year

Presentation of a multi-index clustering technique for the mathematical programming of block-cave scheduling

Long-term planning is one of the most important stages that determines the distribution of cash flows over the mine life and the feasibility of the project. However, it is not feasible in block caving to generate a production schedule that will provide optimal operating strategies without considering geotechnical constraints. This paper develops a mixed-integer linear programming (MILP) model to optimize the extraction sequence of drawpoints over multiple time horizons of block-cave mines with respect to the draw control systems. A multi-similarity index clustering technique to solve the MILP model in a reasonable time is also presented. Application and comparison of production scheduling based on the draw control system and clustering technique are illustrated using 325 drawpoints over 15 periods. The results show a significant reduction in the size of the MILP model, and in the time required to solve it. Keywords: Production scheduling, Block-cave mining, Multi-similarity index clustering, Draw control system, Production rate curv