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IMACS World Congress/Modelling and Simulation Society-of-Australia-and-New-Zealand (MSSANZ)/18th MODSIM09 Biennial Conference on Modelling and Simulation

作者： Nicol, S. C. Chades, I. Linke, S. Possingham, H. P. Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9780975840078

For metapopulation management problems with small state spaces, it is typically possible to model the problem as a Markov decision process (MDP), and find an optimal control policy using stochastic dynamic programming (SDP). SDP is an iterative procedure that seeks to optimise a value function at each timestep by trying each of the actions defined in the MDP. Although SDP gives the optimal solution to conservation management questions in a stochastic world, its applicability has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. A large state space makes optimal SDP solutions computationally expensive to compute because optimal SDP techniques require the value function to be updated for the entire state space for every time step. The high computational requirements of large SDP problems means that only simple population management problems can be analysed. In this paper we present an application of the on-line sparse sampling algorithm proposed by Kearns, Mansour & Ng (2002), which can be used to approximate the optimal solution of a MDP for a given starting state. The algorithm is particularly attractive for problems with large state spaces as it has a running time that is independent of the size of the state space of the problem. We apply the algorithm of Kearns et al. (2002) to a hypothetical fish metapopulation where we define the management objective to be to maximise the number of occupied patches during the management horizon. Our model has multiple management options to combat the threats of water abstraction and waterhole sedimentation. We compare the performance of the optimal solution to the results of the on-line sparse sampling algorithm for a simple 3-waterhole case. We find that the approximation algorithm out

关键词： Markov decision processes stochastic dynamic programming Metapopulation Optimal management On-line sparse sampling algorithm

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The optimal asset allocation of the main types of pension funds: a unified framework

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GENEVA RISK AND INSURANCE REVIEW 2007年第2期32卷 113-128页

作者： Romaniuk, Katarzyna Univ Paris 01 PRISM UFR 06 F-75005 Paris France

The existing literature deals with the optimal investment strategy of defined benefit (DB) or defined contribution (DC) pension plans. This article's objective is to compare the optimal policies of different types of pension plans. This is done by first defining an original framework, which is based on the distinction between the nature of the guarantee-which can be internal or external-offered by or to a pension fund. This framework allows to establish links between optimization programs of DC, DB and targeted money purchase schemes. The case of an internal guarantee appears as a standard portfolio insurer's problem. The second kind of guarantee, not analyzed in the literature yet with regard to the resulting optimal policy, is characterized by the existence of an option in the final wealth definition. Four funds are present in the internal guarantee optimal allocation: the speculative component, the preference independent guarantee- and contribution-hedge terms and the preference dependent state variable-hedge fund. The external guarantee program, solved with an original method using the principles of standard options theory, yields an optimal policy incorporating the delta of the option embodied in the final wealth definition. The conclusion is that the resulting optimal portfolio policy becomes riskier.

关键词： pension funds defined benefit defined contribution asset allocation internal and external guarantees stochastic dynamic programming options theory

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Upper and lower bounds for convex value functions of derivative contracts

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ECONOMIC MODELLING 2013年 34卷 69-75页

作者： Ben-Ameur, Hatem de Frutos, Javier Fakhfakh, Tarek Diaby, Vacaba HEC Montreal Montreal PQ Canada Univ Valladolid Inst Math IMUVA E-47002 Valladolid Spain FSEG Sfax Sfax Tunisia ISG Tunis Tunis Tunisia

The aim of this paper is to compute upper and lower bounds for convex value functions of derivative contracts. Laprise et al. (2006) compute bounds for American-style vanilla options by selected portfolios of call options. We provide an alternative interpretation of their numerical procedure as a stochastic dynamic program for which the Bellman value function is approximated by selected piecewise linear interpolations at each decision date. The stochastic dynamic program does not (directly) depend on portfolios of call options, but rather on a key ingredient: some transition parameters of the underlying asset. More in line with the literature on dynamic programming, our procedure is contract free and is well designed to accommodate all one-dimensional convex value functions of derivative contracts. In support of this, we revisit the numerical investigation of Laprise et al. (2006) and enlarge their findings to include options embedded in bonds under affine term-structure models of interest rates. (C) 2012 Elsevier B.V. All rights reserved.

关键词： American options Derivative contracts Convex functions Upper and lower bounds stochastic dynamic programming Piecewise linear interpolations

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Time-Based Maintenance Models Under Uncertainty 19th

Time-Based Maintenance Models Under Uncertainty

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19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB)

作者： Buchholz, Peter Dohndorf, Iryna Scheftelowitsch, Dimitri TU Dortmund Informat 4 Dortmund Germany

ISBN: (纸本)9783319749471;9783319749464

Model based computation of optimal maintenance strategies is one of the classical applications of Markov Decision Processes. Unfortunately, a Markov Decision Process often does not capture the behavior of a component or system of components correctly because the duration of different operational phases is not exponentially distributed and the status of component is often only partially observable during operational times. The paper presents a general model for components with partially observable states and non-exponential failure, maintenance and repair times which are modeled by phase type distributions. Optimal maintenance strategies are computed using Markov decision theory. However, since the internal state of a component is not completely known, only bounds for the parameters of a Markov decision process can be computed resulting in a bounded parameters Markov decision process. For this kind of process optimal strategies can be computed assuming best, worst or average case behavior.

关键词： Maintenance models Markov decision processes stochastic dynamic programming Numerical methods

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A modeling framework for optimizing the flow-level scheduling with time-varying channels

A modeling framework for optimizing the flow-level schedulin...

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Conference on Performance Evaluation 2010

作者： Ayesta, Urtzi Erausqum, Martin Jacko, Peter BCAM Bilbao 48170 Spain IKERBASQUE Basque Fdn Sci Bilbao 48011 Spain Univ Basque Country UPV EHU Bilbao Spain

We introduce a comprehensive modeling framework for the problem of scheduling a finite number of finite-length jobs where the available service rate is time-varying The main motivation comes from wireless data networks where the service rate of each user varies randomly due to fading We employ recent advances on the restless bandit problem that allow us to obtain an opportunistic scheduling rule for the system without arrivals When the objective is to minimize the mean number of users in the system or to minimize the mean waiting time, we obtain a priority-based policy which we call the Potential Improvement (PI) rule, since the priority index equals the ratio between the current available service rate and the expected potential improvement of the service rate We also show that for certain objective functions the index rule takes the form of known opportunistic scheduling rules like Relatively Best (RB) or Proportionally Best" (PB) Thus our model provides a formal justification for the deployment of opportunistic scheduling rules in order to improve the flow-level performance in the presence of time-varying capacities We further analyze the performance of the PI rule in the presence of randomly arriving users When the service rates are constant PI is equivalent to the c mu-rule which is known to be optimal with any distribution of arrivals Using a recent characterization for the stability region of flow-level scheduling rules under random arrivals we show that PI achieves the maximum stability region We perform numerical experiments in a wide range of scenarios and compare the performance of PI with other popular disciplines like RB PB Score-Based (SB) and the c mu-rule Our results show that RB PB SB or the c mu-rule might outperform the others depending on the scenario, but regardless of this the performance of PI is always superior or equivalent to the best of these opportunistic rules (C) 2010 Elsevier B V All rights reserved

关键词： Multi-armed restless bandit problem Markov Decision Process stochastic dynamic programming Opportunistic scheduling Optimal scheduling c mu-rule Index policy

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KALMAN-LIKE STATE TRACKING AND CONTROL IN POMDPS WITH APPLICATIONS TO BODY SENSING NETWORKS

KALMAN-LIKE STATE TRACKING AND CONTROL IN POMDPS WITH APPLIC...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zois, Daphney-Stavroula Levorato, Marco Mitra, Urbashi Univ So Calif Ming Hsieh Dept Elect Enginering Los Angeles CA 90089 USA

ISBN: (纸本)9781479903566

In this paper, the problem of state tracking with controlled observations is considered for a system modeled by a discrete-time, finite-state Markov chain. The system state is 'hidden' and observed via conditionally Gaussian measurements that are shaped by the underlying state and an exogenous control input. Following an innovations approach, a Kalman-like filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion for a partially observable Markov Decision Process (POMDP). The optimal solution is determined via stochastic dynamic programming. Numerical results are presented for the application of physical activity detection in heterogeneous, wireless body area networks.

关键词： Markov models approximate MMSE POMDP stochastic dynamic programming innovations approach

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Study On Energy Consumption Of Sino - German Building In Uncertain Environment

Study On Energy Consumption Of Sino - German Building In Unc...

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Control Conference Africa (CCA)

作者： Liang, Sun Liang Ye, Li Qi, Jia Hai Ning, Hu Jun Hang, Jin Shenyang Jianzhu Univ Dept Informat & Control Engn Shenyang 110004 Liaoning Peoples R China

According to the experience of developed countries, with the development of the city, the construction industry will go beyond the industrial, transportation and other industries and ultimately ranked first in social energy consumption, and air conditioning energy consumption is the main part of building energy consumption, reduce its operation energy consumption is of great significance to saving energy, improving energy utilization and implementing green building strategy. This paper studies the joint control of HVACs, lights, shading blinds and natural ventilation in the Sino - German Building. This paper has developed an effective environment for integrated control of HVACs, lights, shading blinds and natural ventilation in an uncertain environment. In this paper, an easy-to-use model suitable for optimization is established, and the joint control optimization problem is transformed into a multi-stage stochastic optimization problem. Using the improved Lagrangian relaxation method, the Lagrangian multiplier is used to loosen the constraints that are coupled together by air conditioning equipment such as a common machine. A new Lagrangian relaxation is shown, which can get better gradient direction. The optimal solution can be obtained by referring to stochastic dynamic programming and heuristic rules. Finally, the simulation results of the building simulation software DeST show that the joint optimization of the building end equipment can effectively reduce the building energy consumption. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： joint optimization uncertain environment Lagrangian relaxation stochastic dynamic programming

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dynamic Maintenance Scheduling for Power Systems Incorporating Hurricane Effects

Dynamic Maintenance Scheduling for Power Systems Incorporati...

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2014 IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Arab, Ali Tekin, Eylem Khodaei, Amin Khator, Suresh K. Han, Zhu Univ Houston Dept Ind Engn Houston TX 77204 USA Univ Denver Dept Elect & Comp Engn Denver CO 80208 USA Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA

ISBN: (纸本)9781479949342

An effective preventive maintenance program plays a critical role in improving the reliability of electric power systems. In this paper, the impact of potential damage due to hurricane is incorporated in the power system maintenance scheduling problem. The proposed model considers component deterioration, as well as two competing and independent failure modes, i.e., failure due to loss of reliability and failure due to hurricane damages. Furthermore, the interrelationship between the component, the grid, and the associated downtime cost dynamics are incorporated in the problem. The downtime cost of the power system due to component outage is formulated using mixed-integer programming. The obtained downtime cost is used in a stochastic dynamic programming model to derive the optimal maintenance policy for the component.

关键词： Asset management hurricane planning preventive maintenance grid resiliency power infrastructure stochastic dynamic programming

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SIMPLE PROOF FOR GOOFSPIEL - THE GAME OF PURE STRATEGY

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ADVANCES IN APPLIED PROBABILITY 1989年第3期21卷 711-712页

作者： DROR, M Ben Gurion University of the Negev

A simple proof is given for the best strategy when the game of pure strategy is played.

关键词： stochastic dynamic programming BEST STRATEGY

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dynamic allocation of same-day requests in multi-physician primary care practices in the presence of prescheduled appointments

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HEALTH CARE MANAGEMENT SCIENCE 2014年第1期17卷 31-48页

作者： Balasubramanian, Hari Biehl, Sebastian Dai, Longjie Muriel, Ana Univ Massachusetts Dept Mech & Ind Engn Amherst MA 01003 USA

Appointments in primary care are of two types: 1) prescheduled appointments, which are booked in advance of a given workday;and 2) same-day appointments, which are booked as calls come during the workday. The challenge for practices is to provide preferred time slots for prescheduled appointments and yet see as many same-day patients as possible during regular work hours. It is also important, to the extent possible, to match same-day patients with their own providers (so as to maximize continuity of care). In this paper, we present a mathematical framework (a stochastic dynamic program) for same-day patient allocation in multi-physician practices in which calls for same-day appointments come in dynamically over a workday. Allocation decisions have to be made in the presence of prescheduled appointments and without complete demand information. The objective is to maximize a weighted measure that includes the number of same-day patients seen during regular work hours as well as the continuity provided to these patients. Our experimental design is motivated by empirical data we collected at a 3-provider family medicine practice in Massachusetts. Our results show that the location of prescheduled appointments - i.e. where in the day these appointments are booked - has a significant impact on the number of same-day patients a practice can see during regular work hours, as well as the continuity the practice is able to provide. We find that a 2-Blocks policy which books prescheduled appointments in two clusters - early morning and early afternoon - works very well. We also provide a simple, easily implementable policy for schedulers to assign incoming same-day requests to appointment slots. Our results show that this policy provides near-optimal same-day assignments in a variety of settings.

关键词： Appointment scheduling primary care Same-day access Continuity of care stochastic dynamic programming Heuristics

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