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An approximate dynamic programming approach to resource management in multi-cloud scenarios

INTERNATIONAL JOURNAL OF CONTROL 2017年第3期90卷 492-503页

作者： Pietrabissa, Antonio Delli Priscoli, Francesco Di Giorgio, Alessandro Giuseppi, Alessandro Panfili, Martina Suraci, Vincenzo Univ Roma La Sapienza Dept Comp Control & Management Engn Antonio Ruber Rome Italy

The programmability and the virtualisation of network resources are crucial to deploy scalable Information and Communications Technology (ICT) services. The increasing demand of cloud services, mainly devoted to the storage and computing, requires a new functional element, the Cloud Management Broker (CMB), aimed at managing multiple cloud resources to meet the customers' requirements and, simultaneously, to optimise their usage. This paper proposes a multi-cloud resource allocation algorithm that manages the resource requests with the aim of maximising the CMB revenue over time. The algorithm is based on Markov decision process modelling and relies on reinforcement learning techniques to find online an approximate solution.

关键词： Cloud networks resource management reinforcement learning Markov decision process approximate dynamic programming

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Lattice point sets for state sampling in approximate dynamic programming

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OPTIMAL CONTROL APPLICATIONS & METHODS 2017年第6期38卷 1193-1207页

作者： Cervellera, Cristiano Gaggero, Mauro Maccio, Danilo Natl Res Council Italy Inst Intelligent Syst Automat Via Marini 6 I-16149 Genoa Italy

This paper investigates the use of lattice point sets as an efficient method to sample uniformly the state space of discrete-time dynamic systems for the solution of finite-horizon optimal control problems using approximate dynamic programming. Lattice point sets are a kind of discretization method, commonly employed for efficient numerical integration, providing a regular and balanced sampling of the state space based on the repetition of elementary unit cells. A convergence analysis of the approximate solution of the control problem to the optimal one is provided, pointing out that such sampling schemes allow one to efficiently exploit possible regularities of the cost-to-go functions. Furthermore, it is shown that a higher accuracy may be obtained through suitable transformations of the state vector of the dynamic system. Another advantage of lattice point sets over other sampling schemes is the possibility of evaluating a priori the goodness of a given set over another through the explicit computation of a specific parameter. Simulation results concerning the optimal control of a water reservoirs system are presented to show the effectiveness of the proposed approach.

关键词： approximate dynamic programming finite-horizon optimal control lattice sampling

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Time-dependent green vehicle routing problem with stochastic vehicle speeds: An approximate dynamic programming algorithm

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2017年第Jul.期54卷 82-98页

作者： Cimen, Mustafa Soysal, Mehmet Hacettepe Univ Management Sci Ankara Turkey Hacettepe Univ Operat Management Ankara Turkey

This paper addresses a Time Dependent Capacitated Vehicle Routing Problem with stochastic vehicle speeds and environmental concerns. The problem has been formulated as a Markovian Decision Process. As distinct from the traditional attempts on the problem, while estimating the amount of fuel consumption and emissions, the model takes time dependency and stochasticity of the vehicle speeds into account. The Time Dependent Capacitated Vehicle Routing Problem is known to be NP-Hard for even deterministic settings. Incorporating uncertainty to the problem increases complexity, which renders classical optimization methods infeasible. Therefore, we propose an approximate dynamic programming based heuristic as a decision aid tool for the problem. The proposed Markovian Decision Model and approximate dynamic programming based heuristic are flexible in terms that more environmentally friendly solutions can be obtained by changing the objective function from cost minimization to emissions minimization. The added values of the proposed decision support tools have been shown through computational analyses on several instances. The computational analyses show that incorporating vehicle speed stochasticity into decision support models has potential to improve the performance of resulting routes in terms of travel duration, emissions and travel cost. In addition, the proposed heuristic provides promising results within relatively short computation times. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Green vehicle routing Stochastic time dependent capacitated vehicle routing problem approximate dynamic programming Markovian Decision Process

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Novel time-space network flow formulation and approximate dynamic programming approach for the crane scheduling in a coil warehouse

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第2期262卷 424-437页

作者： Yuan, Yuan Tang, Lixin Northeastern Univ Inst Ind & Syst Engn Shenyang Peoples R China

This article proposes an efficient event-based time-space network flow model with side constraints for the crane scheduling problem in a coil warehouse where the crane should carry out a set of coil storage, retrieval and shuffling requests, and determine the sequence of handling these requests as well as the positions to which the coils are moved. The model is formulated based on a graph such that each node represents a location in the warehouse at the end of a specific scheduling stage, and each edge indicates a crane's move between two locations in a stage. Variables reduction strategies are presented to accelerate solving the model. In order to solve large-sized instances of the problem, an exact dynamic programming approach based on optimal assignments between coils and positions in a bipartite network with cuts is designed by exploiting the problem structure. Then an approximate dynamic programming (ADP) approach is developed, in which an affine value function approximation is defined as the estimation of crane's traveling time for handling each coil, and updated via iterations by collecting information from the solutions of separate subproblems. Computational results show that the proposed model is tighter and can be solved much more quickly than a traditional model for a reduced crane scheduling problem in the literature and the standard time-space network flow model. Besides, the proposed algorithm can obtain high quality solutions for large-sized instances in a few minutes and is more efficient in solving the problem than a commercial software package. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Scheduling Logistics Coil warehouse Time-space network flow formulation approximate dynamic programming

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Joint Admission Control and Routing Via approximate dynamic programming for Streaming Video Over Software-Defined Networking

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IEEE TRANSACTIONS ON MULTIMEDIA 2017年第3期19卷 619-631页

作者： Yang, Jian Zhu, Kunjie Ran, Yongyi Cai, Weizhe Yang, Enzhong Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China

This paper considers the optimization problem of joint admission control and routing for the video streaming service in wired software-defined networking (SDN). With the aid of the network operating system, SDN is able to support the dynamic nature of future network functions and intelligent applications. Against this changing network landscape, we rely on Flow Visor-based virtualization in the context of OpenFlow-based wired SDN to design an open optimization architecture for the joint admission control and routing, which supports flexible and agile deployment of advanced joint admission control and routing strategies. Following this architecture, we interpret the joint admission control and routing problem into the Markov decision process for maximizing the overall " revenue." In order to solve the issue of the curses of dimensionality, we invoke the function approximation technique in the context of approximate dynamic programming to conceive an online learning framework. By applying kernel-based autonomous feature extraction into the function approximation, we develop an approximate dynamic programming-based joint admission control and routing for video streaming service, which is apt to be implemented in the proposed open architecture. An emulation platform based on Flow Visor, POX, and Mininet is constructed for demonstrating the success of the proposed solution. The experimental results are presented to show the performance improvement of the proposed scheme by comparing it with the Q-learning algorithm and open shortest path first-based benchmark scheme.

关键词： Admission control approximate dynamic programming routing algorithm software-defined networking (SDN) video streaming service

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Optimal Management for Parking-Lot Electric Vehicle Charging by Two-Stage approximate dynamic programming

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IEEE TRANSACTIONS ON SMART GRID 2017年第4期8卷 1722-1730页

作者： Zhang, Lei Li, Yaoyu Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA Walmart Labs Sunnyvale CA 94085 USA Univ Texas Dallas Dept Mech Engn Richardson TX 75080 USA

This paper targets the day-time charging scenario for plug-in electric vehicles at parking-lots near commercial places, where most vehicles have extended parking time. Compared with night-time charge scenarios for residential buildings, commercial building parking-lot charging during day-time feature significant stochastic vehicle arrival and departure, as well as highly dynamic electricity price. A two-stage approximate dynamic programming framework is proposed to determine the optimal charging strategy, utilizing the predicted short-term future information and long-term estimation from historical data. All the vehicles are desired to be charged to full prior to the departure time specified under constrained total charging capacity. The uncharged amount is subject to a significant penalty cost. Simulation scenarios are created by modeling the vehicle arrival behavior as Poisson process, including arrival time, departure time, and arrival state of charge. The simulation results show that the proposed method can significantly decrease the energy cost.

关键词： Charging management demand response electric vehicle approximate dynamic programming smart grid

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Optimal control of heavy haul train based on approximate dynamic programming

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ADVANCES IN MECHANICAL ENGINEERING 2017年第4期9卷 1-15页

作者： Wang, Xi Tang, Tao He, Hui Beijing Jiaotong Univ Natl Engn Res Ctr Rail Transportat Operat & Contr Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

This research investigates the optimal control problem of heavy haul train for the minimization of longitudinal forces. As the heavy haul train is much heavier and longer than ordinary train, the in-train forces should be carefully manipulated to reduce the train's maintenance cost and, most importantly, to ensure operation safety. Specifically, the limitations of pneumatically controlled braking system increase the need for the optimal control strategy to accounting for future grades, speed restrictions and uncertain disturbances. In this article, the stochastic dynamic programming model is adopt to set up a rigorous mathematical formulation for heavy haul train control, and approximate dynamic programming algorithm with lookup table representation is introduced to find the optimal solution of the considered problem. By handling the existed uncertainties in a mathematical way, the post-decision state variable is utilized to represent the state of the heavy haul train after we have made a control decision but before any exogenous information has arrived. Finally, the computational results demonstrate the effectiveness and performance of the proposed model and algorithm.

关键词： Heavy haul train optimal control air braking approximate dynamic programming

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Stochastic Generation Capacity Expansion Planning with approximate dynamic programming

Stochastic Generation Capacity Expansion Planning with Appro...

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IEEE/PES Transmission and Distribution Conference and Exposition (T and D)

作者： Bukenberger, Jesse Palmintier, Bryan Natl Renewable Energy Lab Golden CO 80401 USA

ISBN: (纸本)9781538655832

Failing to consider the many long-term uncertainties that affect the performance of a power generation portfolio can result in suboptimal generation expansion plans. Further, traditional deterministic approaches can omit flexible plans that are able to adapt to future events. In this paper, we explore the use of approximate dynamic programming (ADP) to create forward-looking generation expansion plans. A case study is included with three sequential decision periods;three generation technologies;and four uncertainties: demand growth, natural gas prices, renewable portfolio standards, and the adoption of customer-driven solar generation. The flexible plans found through ADP show a 3% reduction in total expected cost when compared to myopic planning heuristics while circumventing the computational burdens that accompany high-dimensional dynamic programs.

关键词： approximate dynamic programming Power Generation Planning Uncertainty Distributed Energy Resources Sampled Backward Induction dynamic programming

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An approximate dynamic programming approach for wind power dispatch in wind farms

An approximate dynamic programming approach for wind power d...

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第37届中国控制会议

作者： Wenhao Zhuo School of Electrical Engineering and Telecommunications The University of New South Wales

With appropriate control based on predicted data of renewable energy sources, battery energy storage systems（BESS） can increase the dispatchability of wind and solar power. In a deregulated electricity market, proper charge and discharge decisions of BESS made at different price periods are likely to increase the profit for renewable power plants. In this paper, we propose an energy trading strategy for wind farms with BESS to control the actual power sold to the electric grid. To maximize the income and minimize the battery lifetime consumption, an approximate dynamic programming algorithm is produced to solve the optimal charge and discharge actions of the BESS within a future period. The rolling horizon approach is utilized, and the algorithm works with both short-term and long-term predicted data that are updated in every control step. The use of an approximate technique is to avoid the long computation time resulted from a large-scale BESS, and to meet time constraints in the rolling horizon approach. The algorithm is tested with practical wind power and electricity price data.

关键词： Wind power control battery energy storage system battery lifetime estimation approximate dynamic programming

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approximate dynamic programming for the dispatch of military medical evacuation assets

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第3期254卷 824-839页

作者： Rettke, Aaron J. Robbins, Matthew J. Lunday, Brian J. Air Force Inst Technol Dept Operat Sci 2950 Hobson Way Wright Patterson AFB OH 45433 USA

Military medical planners must consider the dispatching of aerial military medical evacuation (MEDEVAC) assets when preparing for and executing major combat operations. The launch authority seeks to dispatch MEDEVAC assets such that prioritized battlefield casualties are transported quickly and efficiently to nearby medical treatment facilities. We formulate a Markov decision process (MDP) model to examine the MEDEVAC dispatching problem. The large size of the problem instance motivating this research suggests that conventional exact dynamic programming algorithms are inappropriate. As such, we employ approximate dynamic programming (ADP) techniques to obtain high quality dispatch policies relative to current practices. An approximate policy iteration algorithmic strategy is applied that utilizes least squares temporal differencing for policy evaluation. We construct a representative planning scenario based on contingency operations in northern Syria both to demonstrate the applicability of our MDP model and to examine the efficacy of our proposed ADP solution methodology. A designed computational experiment is conducted to determine how selected problem features and algorithmic features affect the quality of solutions attained by our ADP policies. Results indicate that the ADP policy outperforms the myopic policy (i.e., the default policy in practice) by up to nearly 31% with regard to a lifesaving performance metric, as compared for a baseline scenario. Moreover, the ADP policy provides decreased MEDEVAC response times and utilization rates. These results benefit military medical planners interested in the development and implementation of cogent MEDEVAC tactics, techniques, and procedures for application in combat situations with a high operations tempo. Published by Elsevier B.V.

关键词： approximate dynamic programming Emergency medical dispatch Least squares temporal differences Markov decision processes Military medical evacuation (MEDEVAC)

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