Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it pos...Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem.This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project.Despite of the rapid theoretical and technical advances in this field,heuristics is still the only viable approach for large scale industrial applications.This work presents an approach combining genetic algorithms(GAs) and Lagrangian relaxation(LR) to optimally determine the CLTPSP of open pit mines.GAs are stochastic,parallel search algorithms based on the natural selection and the process of evolution.LR method is known for handling large-scale separable problems; however,the convergence to the optimal solution can be slow.The proposed Lagrangian relaxation and genetic algorithms(LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers.This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP.Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence.Subsequently,highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs.展开更多
The most common location algorithms based on received signal strength(RSS)are location identification based on dynamic active radio frequency identification(LANDMARC)and virtual reference elimination(VIRE).However,bot...The most common location algorithms based on received signal strength(RSS)are location identification based on dynamic active radio frequency identification(LANDMARC)and virtual reference elimination(VIRE).However,both the original algorithms suffer from some drawbacks.In this paper,several aspects of the two original algorithms have been modified to reduce the positioning errors.Firstly,Lagrange interpolation has been used instead of linear interpolation.Secondly,adaptive threshold has been introduced in the new algorithm.Thirdly,insert virtual reference tags to improve the location accuracy of the boundary of the sensing area.Finally,combine LANDMARC with VIRE to absorb both advantages.Compared with the original algorithms,on average,simulated results show that the modified algorithms can improve the location performance efficiently and achieve the goal of accurate positioning in indoor environment.展开更多
文摘Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem.This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project.Despite of the rapid theoretical and technical advances in this field,heuristics is still the only viable approach for large scale industrial applications.This work presents an approach combining genetic algorithms(GAs) and Lagrangian relaxation(LR) to optimally determine the CLTPSP of open pit mines.GAs are stochastic,parallel search algorithms based on the natural selection and the process of evolution.LR method is known for handling large-scale separable problems; however,the convergence to the optimal solution can be slow.The proposed Lagrangian relaxation and genetic algorithms(LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers.This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP.Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence.Subsequently,highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs.
基金supported by the National Natural Science Foundation of China (61003237)
文摘The most common location algorithms based on received signal strength(RSS)are location identification based on dynamic active radio frequency identification(LANDMARC)and virtual reference elimination(VIRE).However,both the original algorithms suffer from some drawbacks.In this paper,several aspects of the two original algorithms have been modified to reduce the positioning errors.Firstly,Lagrange interpolation has been used instead of linear interpolation.Secondly,adaptive threshold has been introduced in the new algorithm.Thirdly,insert virtual reference tags to improve the location accuracy of the boundary of the sensing area.Finally,combine LANDMARC with VIRE to absorb both advantages.Compared with the original algorithms,on average,simulated results show that the modified algorithms can improve the location performance efficiently and achieve the goal of accurate positioning in indoor environment.
