The Experts below are selected from a list of 9198 Experts worldwide ranked by ideXlab platform
Kalyanmoy Deb - One of the best experts on this subject based on the ideXlab platform.
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evolutionary algorithms in large scale Open Pit Mine scheduling
Genetic and Evolutionary Computation Conference, 2010Co-Authors: Christie Myburgh, Kalyanmoy DebAbstract:With many years of research and application to real-world problems, evolutionary algorithms (EAs) have solved various problems having thousands of variables, hard heuristic constraints, and complex evaluation procedures. This paper reports another successful application of EAs in Open Pit Mine scheduling. Typically an ore body is discretized as a 3D block model which, depending on factors such as the amount of data obtained, size of deposit, block dimensions etc. can be made up of over one million blocks, thereby requiring an optimization algorithm to handle over a million variables. Open Pit Mine scheduling is a complex task which is subject to very strict hard geometrical and other practical mining constraints. To the best of our knowledge there are currently no algorithm or software package that can cater for the large number of constraints and sheer scale of the data sets represented by Open Pit Mine scheduling. Most packages are limited in the size of block model and the kind of objective and constraint functions they can efficiently handle. The proposed optimization algorithm and the resulting software (evORElution -- a trademark product of ORElogy) is developed by using the theoretical and fundamental results of evolutionary algorithms and has already been successfully used to produce complex multi-objective schedules for several large Open Pit iron ore Mines involving hundreds of thousands to millions of variables.
Christie Myburgh - One of the best experts on this subject based on the ideXlab platform.
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GECCO - Evolutionary algorithms in large-scale Open Pit Mine scheduling
Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO '10, 2010Co-Authors: Christie MyburghAbstract:With many years of research and application to real-world problems, evolutionary algorithms (EAs) have solved various problems having thousands of variables, hard heuristic constraints, and complex evaluation procedures. This paper reports another successful application of EAs in Open Pit Mine scheduling. Typically an ore body is discretized as a 3D block model which, depending on factors such as the amount of data obtained, size of deposit, block dimensions etc. can be made up of over one million blocks, thereby requiring an optimization algorithm to handle over a million variables. Open Pit Mine scheduling is a complex task which is subject to very strict hard geometrical and other practical mining constraints. To the best of our knowledge there are currently no algorithm or software package that can cater for the large number of constraints and sheer scale of the data sets represented by Open Pit Mine scheduling. Most packages are limited in the size of block model and the kind of objective and constraint functions they can efficiently handle. The proposed optimization algorithm and the resulting software (evORElution -- a trademark product of ORElogy) is developed by using the theoretical and fundamental results of evolutionary algorithms and has already been successfully used to produce complex multi-objective schedules for several large Open Pit iron ore Mines involving hundreds of thousands to millions of variables.
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evolutionary algorithms in large scale Open Pit Mine scheduling
Genetic and Evolutionary Computation Conference, 2010Co-Authors: Christie Myburgh, Kalyanmoy DebAbstract:With many years of research and application to real-world problems, evolutionary algorithms (EAs) have solved various problems having thousands of variables, hard heuristic constraints, and complex evaluation procedures. This paper reports another successful application of EAs in Open Pit Mine scheduling. Typically an ore body is discretized as a 3D block model which, depending on factors such as the amount of data obtained, size of deposit, block dimensions etc. can be made up of over one million blocks, thereby requiring an optimization algorithm to handle over a million variables. Open Pit Mine scheduling is a complex task which is subject to very strict hard geometrical and other practical mining constraints. To the best of our knowledge there are currently no algorithm or software package that can cater for the large number of constraints and sheer scale of the data sets represented by Open Pit Mine scheduling. Most packages are limited in the size of block model and the kind of objective and constraint functions they can efficiently handle. The proposed optimization algorithm and the resulting software (evORElution -- a trademark product of ORElogy) is developed by using the theoretical and fundamental results of evolutionary algorithms and has already been successfully used to produce complex multi-objective schedules for several large Open Pit iron ore Mines involving hundreds of thousands to millions of variables.
Adolfo Arata - One of the best experts on this subject based on the ideXlab platform.
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availability based simulation and optimization modeling framework for Open Pit Mine truck allocation under dynamic constraints
International journal of mining science and technology, 2013Co-Authors: Rodrigo Mena, Enrico Zio, Fredy Kristjanpoller, Adolfo ArataAbstract:Abstract We present a novel system productivity simulation and optimization modeling framework in which equipment availability is a variable in the expected productivity function of the system. The framework is used for allocating trucks by route according to their operating performances in a truck–shovel system of an Open-Pit Mine, so as to maximize the overall productivity of the fleet. We implement the framework in an originally designed and specifically developed simulator–optimizer software tool. We make an application on a real Open-Pit Mine case study taking into account the stochasticity of the equipment behavior and environment. The total system production values obtained with and without considering the equipment reliability, availability and maintainability (RAM) characteristics are compared. We show that by taking into account the truck and shovel RAM aspects, we can maximize the total production of the system and obtain specific information on the production availability and productivity of its components.
Wenbo Liu - One of the best experts on this subject based on the ideXlab platform.
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ICPS - Research on Open-Pit Mine Vehicle Scheduling Problem with Approximate Dynamic Programming
2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), 2019Co-Authors: Fengyuan Shi, Wenbo LiuAbstract:Open-Pit Mine vehicle scheduling problem is mainly about allocating and designing the schedule of trucks and electric forklift under the constraints, which includes the decision of transportation route, distribution of electric forklifts and trucks number at different mining area with the objective of maximizing equipment utilization rate and reducing production cost. In addition, the various real-time practical changes in mining lead Open-Pit Mine vehicle scheduling problem a dynamic scheduling problem. In this paper, the mathematical model of Open-Pit Mines vehicle scheduling problem using continuous time modeling is established. The transport process, characteristics and requirements of vehicle scheduling problem in Open-Pit Mines are analyzed. For large-scale examples, an approximate dynamic programming model is established by ADP algorithm based on Q-Learning. Numerical experiments of different extraction methods of feature vector and update methods of coefficient vector are carried out, and the results are compared with the results by using solver. The experimental results present that the ADP algorithm designed in this paper can effectively solve the large scale Open-Pit Mine vehicle scheduling problem.
Enrico Zio - One of the best experts on this subject based on the ideXlab platform.
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availability based simulation and optimization modeling framework for Open Pit Mine truck allocation under dynamic constraints
International journal of mining science and technology, 2013Co-Authors: Rodrigo Mena, Enrico Zio, Fredy Kristjanpoller, Adolfo ArataAbstract:Abstract We present a novel system productivity simulation and optimization modeling framework in which equipment availability is a variable in the expected productivity function of the system. The framework is used for allocating trucks by route according to their operating performances in a truck–shovel system of an Open-Pit Mine, so as to maximize the overall productivity of the fleet. We implement the framework in an originally designed and specifically developed simulator–optimizer software tool. We make an application on a real Open-Pit Mine case study taking into account the stochasticity of the equipment behavior and environment. The total system production values obtained with and without considering the equipment reliability, availability and maintainability (RAM) characteristics are compared. We show that by taking into account the truck and shovel RAM aspects, we can maximize the total production of the system and obtain specific information on the production availability and productivity of its components.
