The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Zimu Zhu - One of the best experts on this subject based on the ideXlab platform.
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optimal investing after retirement under time varying risk Capacity Constraint
Social Science Research Network, 2020Co-Authors: Weidong Tian, Zimu ZhuAbstract:This paper explores an optimal investing problem for a retiree facing longevity risk and living standard risk. We formulate the optimal investing problem as an optimal portfolio choice problem under a time-varying risk Capacity Constraint. Under the specific condition on model parameters, we show that the value function is a $C^2$ solution of the HJB equation and derive the optimal investment strategy in terms of second-order ordinary differential equations. The optimal portfolio is nearly neutral to the stock market movement if the portfolio's value is at a sufficiently high level; but, if the portfolio is not worth enough to sustain the retirement spending, the retiree actively invests in the stock market for the higher expected return. In addition, we solve an optimal portfolio choice problem under a leverage Constraint and show that the optimal portfolio would lose significantly in stressed markets. This paper shows that the time-varying risk Capacity Constraint has important implications for asset allocation in retirement.
Vincent K. N. Lau - One of the best experts on this subject based on the ideXlab platform.
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On the design of MIMO block-fading channels with feedback-link Capacity Constraint
IEEE Transactions on Communications, 2004Co-Authors: Vincent K. N. Lau, Youjian Liu, Tai-ann ChenAbstract:In this paper, we propose a combined adaptive power control and beamforming framework for optimizing multiple-input/multiple-output (MIMO) link Capacity in the presence of feedback-link Capacity Constraint. The feedback channel is used to carry channel state information only. It is assumed to be noiseless and causal with a feedback Capacity Constraint in terms of maximum number of feedback bits per fading block. We show that the hybrid design could achieve the optimal MIMO link Capacity, and we derive a computationally efficient algorithm to search for the optimal design under a specific average power Constraint. Finally, we shall illustrate that a minimum mean-square error spatial processor with a successive interference canceller at the receiver could be used to realize the optimal Capacity. We found that feedback effectively enhances the forward channel Capacity for all signal-to-noise ratio (SNR) values when the number of transmit antennas (n/sub T/) is larger than the number of receive antennas (n/sub R/). The SNR gain with feedback is contributed by focusing transmission power on active eigenchannel and temporal power waterfilling . The former factor contributed, at most, 10log/sub 10/(n/sub T//n/sub R/) dB SNR gain when n/sub T/>n/sub R/, while the latter factor's SNR gain is significant only for low SNR values.
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optimal transmission design for mimo block fading channels with feedback Capacity Constraint
Information Theory Workshop, 2003Co-Authors: Vincent K. N. LauAbstract:We focus on investigating the optimal MIMO transmission strategy and the optimal feedback strategy for forward channel Capacity and forward error exponent of MIMO block fading channels when the feedback link is causal and has a Capacity Constraint. We assume a forward MIMO block fading channel where the channel state information is estimated at the receiver and partially fed back to the transmitter. The feedback link is assumed to be noiseless and causal with a feedback Capacity Constraint in terms of maximum number of feedback bits per fading block. We show that the design of the optimal feedback scheme is identical to the design of the vector quantizer - S.P. Loyld's algorithm (see IEEE Trans. Inf. Theory, 1982) - with a modified distortion measure. It is shown that in the general case, the optimal feedback strategy has a general form of power water-filling cascaded with a beamforming matrix as well. Furthermore, we show that the SNR gain with feedback is contributed by focusing transmission power on the active eigenchannel and temporal power water-filling. The former factor contributed at most log/sub 10/(n/sub T/)n/sub R/ dB SNR gain, when n/sub T/>n/sub R/ in all SNR regions, while the latter contribution is significant only in the low SNR region. Finally, the MMSE receiver could be used to achieve the optimal Capacity in the general case of partial feedback.
Tai-ann Chen - One of the best experts on this subject based on the ideXlab platform.
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On the design of MIMO block-fading channels with feedback-link Capacity Constraint
IEEE Transactions on Communications, 2004Co-Authors: Vincent K. N. Lau, Youjian Liu, Tai-ann ChenAbstract:In this paper, we propose a combined adaptive power control and beamforming framework for optimizing multiple-input/multiple-output (MIMO) link Capacity in the presence of feedback-link Capacity Constraint. The feedback channel is used to carry channel state information only. It is assumed to be noiseless and causal with a feedback Capacity Constraint in terms of maximum number of feedback bits per fading block. We show that the hybrid design could achieve the optimal MIMO link Capacity, and we derive a computationally efficient algorithm to search for the optimal design under a specific average power Constraint. Finally, we shall illustrate that a minimum mean-square error spatial processor with a successive interference canceller at the receiver could be used to realize the optimal Capacity. We found that feedback effectively enhances the forward channel Capacity for all signal-to-noise ratio (SNR) values when the number of transmit antennas (n/sub T/) is larger than the number of receive antennas (n/sub R/). The SNR gain with feedback is contributed by focusing transmission power on active eigenchannel and temporal power waterfilling . The former factor contributed, at most, 10log/sub 10/(n/sub T//n/sub R/) dB SNR gain when n/sub T/>n/sub R/, while the latter factor's SNR gain is significant only for low SNR values.
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optimal partial feedback design for mimo block fading channels with feedback Capacity Constraint
International Symposium on Information Theory, 2003Co-Authors: Tai-ann ChenAbstract:In this paper, we focus in investigating the optimal MIMO transmission and the optimal feedback strategies for MIMO link Capacity when the feedback link is causal and has a Capacity Constraint. We show that the design of the optimal feedback scheme is identical to the design of vector quantizer (Lloyd's algorithm) with a modified distortion measure. In the general case, the optimal feedback strategy has a general form of power water-filling cascaded with beamforming matrix as well.
Zhaowei Miao - One of the best experts on this subject based on the ideXlab platform.
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truck dock assignment problem with time windows and Capacity Constraint in transshipment network through crossdocks
International Conference on Computational Science and Its Applications, 2006Co-Authors: Andrew Lim, Zhaowei MiaoAbstract:In this paper, we consider the over-constrained truck dock assignment problem with time windows and Capacity Constraint in transshipment network through crossdocks where the number of trucks exceeds the number of docks available, the Capacity of the crossdock is limited, and where the objective is to minimize the total shipping distances. The problem is first formulated as an Integer Programming (IP) model, and then we propose a Tabu Search (TS) and a Genetic algorithms (GA) that utilize the IP Constraints. Computational results are provided, showing that the heuristics perform better than the CPLEX Solver in both small-scale and large-scale test sets. Therefore, we conclude that the heuristic search approaches are efficient for the truck dock assignment problem. Areas: Heuristics, industrial applications of AI.
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truck dock assignment problem with time windows and Capacity Constraint in transshipment network through crossdocks
Lecture Notes in Computer Science, 2006Co-Authors: Andrew Lim, Zhaowei MiaoAbstract:In this paper, we consider the over-constrained truck dock assignment problem with time windows and Capacity Constraint in transshipment network through crossdocks where the number of trucks exceeds the number of docks available, the Capacity of the crossdock is limited, and where the objective is to minimize the total shipping distances. The problem is first formulated as an Integer Programming (IP) model, and then we propose a Tabu Search (TS) and a Genetic algorithms (GA) that utilize the IP Constraints. Computational results are provided, showing that the heuristics perform better than the CPLEX Solver in both small-scale and large-scale test sets. Therefore, we conclude that the heuristic search approaches are efficient for the truck dock assignment problem.
Zhuang Zhou - One of the best experts on this subject based on the ideXlab platform.
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pricing models in a sustainable supply chain with Capacity Constraint
Journal of Cleaner Production, 2019Co-Authors: Jing Wang, Zhuang ZhouAbstract:Abstract In this paper, we investigate a closed-loop supply chain with one supplier and one third-party collector who compete with each other and have more pricing power than the manufacturer with production Capacity Constraints. The optimal pricing, recycling and remanufacturing strategies are derived. Three pricing models are proposed to discuss strategies under different scenarios: the supplier-led Stackelberg game model, the third-party collector-led Stackelberg game model and the Nash game model. Through theoretical analysis and numerical studies, it is found that (1) strategies such as advertising and promotion can gain more market preference for managers yet cause a dilemma for the supplier who must balance between profit and supply chain sustainability when choosing these strategies; (2) the supplier or the third-party collector gain more profit when they act as the follower and the supplier-led game scenario is the best scenario for the recycling and remanufacturing activities in the supply chain; (3) it is also a dilemma for the supplier to balance between profit and supply chain sustainability when choosing a decision sequence.
