The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Srinivas Peeta - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Resource Allocation problem for transportation network evacuation
Networks and Spatial Economics, 2014Co-Authors: Srinivas PeetaAbstract:Allocating movable response Resources Dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the Dynamic Resource Allocation problem for transportation evacuation planning and operations. To enable realism in practice, the proposed model includes spatiotemporal constraints related to the time required to reallocate Resources to another location, the minimum time allocated Resources should be at a location, and the minimum time gap between successive Allocations of Resources to a location. The proposed model is transformed into a two-stage optimization program for which a greedy-type heuristic algorithm is developed to solve the MILP approximately but efficiently. Results from computational experiments demonstrate the effectiveness of the proposed model and the efficiency of the heuristic solution algorithm.
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Dynamic Resource Allocation problem for transportation network evacuation
Networks and Spatial Economics, 2014Co-Authors: Srinivas PeetaAbstract:Allocating movable response Resources Dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the Dynamic Resource Allocation problem for transportation evacuation planning and operations. To enable realism in practice, the proposed model includes spatiotemporal constraints related to the time required to reallocate Resources to another location, the minimum time allocated Resources should be at a location, and the minimum time gap between successive Allocations of Resources to a location. The proposed model is transformed into a two-stage optimization program for which a greedy-type heuristic algorithm is developed to solve the MILP approximately but efficiently. Results from computational experiments demonstrate the effectiveness of the proposed model and the efficiency of the heuristic solution algorithm. Copyright Springer Science+Business Media New York 2014
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Dynamic Resource Allocation problem for transportation network evacuation
Transportation Research Board 93rd Annual MeetingTransportation Research Board, 2014Co-Authors: Srinivas PeetaAbstract:Allocating moveable response Resources Dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the Dynamic Resource Allocation problem for transportation evacuation planning and operations. To enable realism in practice, the proposed model includes spatio-temporal constraints related to the time required to reallocate Resources to another location, the minimum time allocated Resources should be at a location, and the minimum time gap between successive Allocations of Resources to a location. The proposed model is transformed into a two-stage optimization program for which a greedy-type heuristic algorithm is developed to solve the MILP approximately but efficiently. Results from computational experiments demonstrate the effectiveness of the proposed model and the efficiency of the heuristic solution algorithm.
Hui Liu - One of the best experts on this subject based on the ideXlab platform.
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downlink Dynamic Resource Allocation for multi cell ofdma system
Asilomar Conference on Signals Systems and Computers, 2003Co-Authors: Hui LiuAbstract:This paper presents an OFDMA radio Resource control (RRC) scheme where Dynamic Resource Allocation is realized at both a radio network controller (RNC) and base stations (BTSs). The scheme is semi-distributed in the sense that the RNC coordinates the mutual interference (inter-cell) at a super-frame level, whereas each BTS makes faster frame-level channel assignment decision based on the Resources' utility value to the users. Another contribution of the paper is a set of computationally efficient (linear-time) algorithms that perform Dynamic channel Allocation at the RNC and BTSs. Simulations show that the algorithm yields excellent performance for both real-time and non real-time services, even under very fast fading.
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Dynamic Resource Allocation with finite buffer constraint in broadband ofdma networks
Wireless Communications and Networking Conference, 2003Co-Authors: Hui LiuAbstract:This paper presents a Dynamic Resource Allocation scheme for OFDMA-based wireless broadband networks. The problem of maximizing the total packet throughput subject to individual user's outage probability constraint is formulated. The proposed algorithm assumes a finite buffer for the arrival packets and Dynamically allocates the radio Resource based on users' channel characteristics, traffic patterns and QoS requirements. By performing the radio Resource Allocation into two steps, namely bandwidth Allocation and channel assignment, efficient admission control is realized with low complexity. Specifically, the number of channels to be assigned to each user is first determined based on its traffic requirement and the average SNR. The second stage of the algorithm finds the best channel Allocation for the users. Simulations show that the algorithm yields significant lower outage probability and higher throughput than existing multiple access methods.
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downlink Dynamic Resource Allocation for multi cell ofdma system
Vehicular Technology Conference, 2003Co-Authors: Hui LiuAbstract:This paper presents an OFDMA radio Resource control (RRC) scheme where Dynamic Resource Allocation is realized at both a radio network controller (RNC) and base stations (BTSs). The scheme is semi-distributed in the sense that the RNC coordinates the mutual interference (inter-cell) at a super-frame level, whereas each BTS makes faster frame-level channel assignment decision based on Resources' utility value to users. Another contribution of the paper is a set of computationally efficient algorithms that perform Dynamic channel Allocation at RNC and BTSs. Simulations show that the algorithm yields excellent performance for both real-time and non real-time services, even under very fast fading.
Mark M Westerfield - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Resource Allocation with hidden volatility
Journal of Financial Economics, 2021Co-Authors: Felix Zhiyu Feng, Mark M WesterfieldAbstract:Abstract We study a firm’s internal Resource Allocation using a Dynamic principal-agent model with endogenous cash flow volatility. The principal supplies the agent with Resources for productive use, but the agent has private control over both project volatility and Resource intensity and may misallocate Resources to obtain private benefits. The optimal contract can yield either overly risky or overly prudent project selection. It can be implemented with a constant pricing schedule (i.e., a static, decentralized, linear mechanism), giving the agent control over the Resource quantities, project risk, and agent’s equity share. The implementation rationalizes the use of hurdle rates above a firm’s cost of capital and transfer prices above marginal cost, while showing that hurdle rates or transfer prices may not vary with the agent’s risk choice.
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Dynamic Resource Allocation with hidden volatility
Social Science Research Network, 2020Co-Authors: Felix Zhiyu Feng, Mark M WesterfieldAbstract:We study a Dynamic continuous-time principal-agent model with endogenous cash flow volatility. The principal supplies the agent with capital for investment, but the agent can misallocate capital for private benefit and has private control over both the volatility of the project and the size of the investment. The optimal incentive-compatible contract can yield either overly risky or overly prudent project selection; it can be implemented as a time-varying cost of capital in the form of a hurdle rate. Our model captures stylized facts about the use of hurdle rates in capital budgeting and helps to reconcile the mixed empirical evidence on the correlations among firm size, risk and managerial compensation.
Michael J Neely - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Resource Allocation and power management in virtualized data centers
Network Operations and Management Symposium, 2010Co-Authors: Rahul Urgaonkar, Ulas C Kozat, Ken Igarashi, Michael J NeelyAbstract:We investigate optimal Resource Allocation and power management in virtualized data centers with time-varying workloads and heterogeneous applications. Prior work in this area uses prediction based approaches for Resource provisioning. In this work, we take an alternate approach that makes use of the queueing information available in the system to make online control decisions. Specifically, we use the recently developed technique of Lyapunov Optimization to design an online admission control, routing, and Resource Allocation algorithm for a virtualized data center. This algorithm maximizes a joint utility of the average application throughput and energy costs of the data center. Our approach is adaptive to unpredictable changes in the workload and does not require estimation and prediction of its statistics.
Renchao Xie - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Resource Allocation for heterogeneous services in cognitive radio networks with imperfect channel sensing
IEEE Transactions on Vehicular Technology, 2012Co-Authors: Renchao XieAbstract:Resources in cognitive radio networks (CRNs) should Dynamically be allocated according to the sensed radio environment. Although some work has been done for Dynamic Resource Allocation in CRNs, many works assume that the radio environment can perfectly be sensed. However, in practice, it is difficult for the secondary network to have the perfect knowledge of a Dynamic radio environment in CRNs. In this paper, we study the Dynamic Resource Allocation problem for heterogeneous services in CRNs with imperfect channel sensing. We formulate the power and channel Allocation problem as a mixed-integer programming problem under constraints. The computational complexity is enormous to solve the problem. To reduce the computational complexity, we tackle this problem in two steps. First, we solve the optimal power Allocation problem using the Lagrangian dual method under the assumption of known channel Allocation. Next, we solve the joint power and channel Allocation problem using the discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution. Another advantage of this method is that it can track the changing radio environment to Dynamically allocate the Resources. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.
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Dynamic Resource Allocation for heterogeneous services in cognitive radio networks with imperfect channel sensing
Global Communications Conference, 2011Co-Authors: Renchao XieAbstract:In cognitive radio networks (CRNs), perfect knowledge of a Dynamic radio environment is hard to know due to hardware limitation, short sensing time and network connectivity issues in CRNs. In this paper, we study the Dynamic Resource Allocation problem for heterogeneous services in CRNs with imperfect channel sensing. We formulate the optimization problem as a mixed integer programming problem under constraints. Then we solve the optimal joint power and channel Allocation problem using discrete stochastic optimization method. The proposed algorithm has low computation complexity and fast convergence to approximate to the optimal solution under imperfect channel sensing. Another advantage of this method is that it can track the changing radio environment to allocate the Resources Dynamically. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.
