This paper presents the development and implementation of an innovative mixed integer programming based mathematical model for an open pit mining operation with Grade Engineering framework.Grade Engineering comprises ...This paper presents the development and implementation of an innovative mixed integer programming based mathematical model for an open pit mining operation with Grade Engineering framework.Grade Engineering comprises a range of coarse-separation based pre-processing techniques that separate the desirable(i.e.high-grade)and undesirable(i.e.low-grade or uneconomic)materials and ensure the delivery of only selected quantity of high quality(or high-grade)material to energy,water,and cost-intensive processing plant.The model maximizes the net present value under a range of operational and processing constraints.Given that the proposed model is computationally complex,the authors employ a data preprocessing procedure and then evaluate the performance of the model at several practical instances using computation time,optimality gap,and the net present value as valid measures.In addition,a comparison of the proposed and traditional(without Grade Engineering)models reflects that the proposed model outperforms the traditional formulation.展开更多
Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus o...Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.展开更多
文摘This paper presents the development and implementation of an innovative mixed integer programming based mathematical model for an open pit mining operation with Grade Engineering framework.Grade Engineering comprises a range of coarse-separation based pre-processing techniques that separate the desirable(i.e.high-grade)and undesirable(i.e.low-grade or uneconomic)materials and ensure the delivery of only selected quantity of high quality(or high-grade)material to energy,water,and cost-intensive processing plant.The model maximizes the net present value under a range of operational and processing constraints.Given that the proposed model is computationally complex,the authors employ a data preprocessing procedure and then evaluate the performance of the model at several practical instances using computation time,optimality gap,and the net present value as valid measures.In addition,a comparison of the proposed and traditional(without Grade Engineering)models reflects that the proposed model outperforms the traditional formulation.
基金supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155885, Artificial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA))supported by the National Natural Science Foundation of China under Grant No. 61971264the National Natural Science Foundation of China/Research Grants Council Collaborative Research Scheme under Grant No. 62261160390
文摘Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.
