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Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management

TRANSPORTATION SCIENCE 2013年第2期47卷 181-197页

作者： Escudero, L. F. Monge, J. F. Morales, D. Romero Wang, J. Univ Rey Juan Carlos Dept Estadist & Invest Operativa Madrid Spain Univ Miguel Hernandez Ctr Invest Operativa Elche Alicante Spain Univ Oxford Said Business Sch Oxford England

This paper studies a multistage stochastic programming (SP) model for large-scale network revenue management. We solve the model by means of the so-called expected future value (EFV) decomposition via scenario analysis, estimating the impact of the decisions made at a given stage on the objective function value related to the future stages. The EFV curves are used to define bid prices on bundles of resources directly, as opposed to the traditional additive approach. We compare our revenues to those obtained by additive bid prices, such as the bid prices derived from the deterministic equivalent model (DEM) of the compact representation of the SP model. Our computational experience shows that the revenues obtained by our approach are better for middle-range values of the load factor of demand, whereas the differences among all the approaches we have tested are insignificant for extreme values. Moreover, our approach requires significantly less computation time than does the optimization of DEM by plain use of optimization engines. Problem instances with 72 pairs of bundle-fare classes have been solved in less than one minute, with 800 pairs in less than five minutes, and with 4,000 pairs in less than one hour. The time taken by DEM was, in general, of one order of magnitude higher. Finally, for the three largest problem instances, and after two hours, the expected revenue returned by DEM was below that obtained by EFV by 13.47%, 17.14%, and 38.94%, respectively.

关键词： network revenue management scenario trees stochastic dynamic programming expected future value curves nonadditive bid prices load factor of demand

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Joint Production and Preventive Maintenance Strategy for Manufacturing Systems With stochastic Demand

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2013年第3期135卷 031016-031016页

作者： Jin, Xiaoning Ni, Jun Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

This paper seeks to make joint decisions on preventive maintenance level and production quantity for manufacturing systems subject to stochastic demand in a finite-horizon. Standard models for scheduling preventive maintenance typically ignore the throughput target variation due to demand uncertainty and specify instead a constant demand rate. We show that maintenance decisions should be integrated with production decisions to accommodate the demand uncertainty. To achieve this objective, preventive maintenance (PM) flexibility is introduced as the opportunity to select and implement maintenance tasks at different levels, which can be viewed as real options to the manufacturer. PM levels can be defined according to the degree to which the machine condition is stored by maintenance. A preventive maintenance can be a minimal, imperfect, or perfect one. By leveraging PM flexibility, this paper proposes a model to determine optimal production quantity and PM level for a single-product manufacturing system with a finite planning horizon. A real option analysis (ROA) is developed to quantify the benefits and costs of PM flexibility. We derive optimal joint decisions for maintenance and production that maximize the overall expected profit of the system. We compare the proposed PM-flexible model with the conventional PM-fixed model in a case study. The results demonstrate the condition that the PM-flexible model outperforms the PM-fixed model in terms of option value (expected operating profits). We also show how the growth in demand volatility affects the optimal decisions and overall profitability. These results have important implications for making maintenance and production decisions, especially in industries that feature high demand volatility.

关键词： real option analysis flexibility preventive maintenance stochastic dynamic programming

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Impact of Driveability Constraints on Local Optimal Energy Management Strategies for Hybrid Powertrains

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IFAC Proceedings Volumes 2013年第21期46卷 23-28页

作者： D. Bardini T. Leroy IFP Energies nouvelles 1&4 avenue de Bois Préau 92852 Rueil-Malmaison France

The paper deals with the issue of energy management in hybrid electric vehicles, considering possible solutions for addressing the power split in real-life conditions. The focus is on the comparison between two local optimal on-line energy management strategies: stochastic dynamic programming (SDP) and Equivalent Consumption Minimization Strategy (ECMS). First, results of fuel consumption for simulation on 100 random driving cycles for the two strategies, tuned for best fuel consumption, are provided. Then, some driveability considerations, regarding engine utilization profile and dwell time, are made. The results of the simulation of the two strategies incorporating driveability constraints are discussed, showing a substantial change of situation with respect to the initial case.

关键词： Driveability Energy Management Equivalent Consumption Minimization Strategy Pontryagin's Minimum Principle stochastic dynamic programming

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A stochastic Security Game for Kalman Filtering in Networked Control Systems (NCSs) under Denial of Service (DoS) Attacks

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IFAC Proceedings Volumes 2013年第20期46卷 106-111页

作者： Shichao Liu P. Xiaoping Liu Abdulmotaleb El Saddik Department of Systems and Computer Engineering Carleton University Ottawa ON K1S 5B6 Canada School of Information Technology and Engineering University of Ottawa Ottawa ON K1N 6N5 Canada

In this paper, we investigate the problem how to design optimal security mechanism for the Kalman filtering of Networked Control Systems (NCSs) with a DoS attacker under limited resources. A two-player zero-sum stochastic game is formulated to model not only the dynamic interaction between the network administrator (as a defender) and the DoS attacker (as an attacker), but also the transitions between the two states (on/off) of the communication link. In specific, we take the degraded dynamic performance of physical systems into consideration when we design the network security mechanism. In this stochastic game, the cost function consists of two parts: one component is the network resource cost, the other one is the degraded dynamic performance of the Kalman filtering of the NCS, indexing by the trace of the estimation error covariance matrix. The optimal defense and attack strategies are obtained by using stochastic dynamic programming (SDP). A linear NCS is used to illustrate the procedure of the proposed stochastic game formulation and solving it by SDP.

关键词： Networked control systems denial-of-service (DoS) attacks stochastic game stochastic dynamic programming Kalman Filter

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Analytical modelling and control of a hybrid manufacturing/remanufacturing system

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IFAC Proceedings Volumes 2013年第9期46卷 401-406页

作者： Annie Francie Kouedeu Jean-Pierre Kenne Pierre Dejax Victor Songmene Vladimir Polotski Mechanical Engineering Department Laboratory of Integrated Production Technologies University of Quebec École de Technologie Supérieure 1100 Notre Dame Street West Montreal (Quebec) Canada H3C 1K3 LUNAM École des Mines de Nantes IRCCyN La Chantrerie 4 Rue Alfred Kastler PO Box 20722 - 44307 Nantes Cedex 3 - France

A production system consisting of parallel machines deteriorating with the production dependent failure rates of the machine is investigated in this paper and extended within the framework of a manufacturing/remanufacturing system within a closed loop reverse logistics system The machines produce one type of final products. The demand rate for the final commodity is constant and unmet demand is backlogged. The goal is to find the production rates of both machines so as to minimize a long term average expected cost which includes backlogging as well as inventory costs. In the proposed model, the main machine (characterized by a higher production rate) is more robust than the second machine. The failure rate of the main machine depends on its production rate, while the failure rate of the second machine is constant. This paper proposes a stochastic dynamic programming formulation of the problem and derives the optimal production policies numerically. A simulation example is included, and sensitivity analyses with respect to the system parameters are examined to illustrate the effectiveness of the proposed methodology.

关键词： Production planning Remanufacturing stochastic dynamic programming Numerical methods Reverse logistics

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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

作者： Daphney-Stavroula Zois Marco Levorato Urbashi Mitra Ming Hsieh Department of Electrical Enginering University of Southern California

ISBN: (纸本)9781479903573

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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Optimal operation of hydrothermal systems with Hydrological Scenario Generation through Bootstrap and Periodic Autoregressive Models

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第3期222卷 606-615页

作者： Souza, Reinaldo Castro Marques Marcato, Andre Luis Dias, Bruno Henriques Cyrino Oliveira, Fernando Luiz Univ Fed Juiz de Fora Juiz De Fora Brazil Pontifical Catholic Univ Rio de Janeiro Rio De Janeiro Brazil Univ Fed Fluminense Niteroi RJ Brazil

In electrical power systems with strong hydro generation, the use of adequate techniques to generate synthetic hydrological scenarios is extremely important for the evaluation of the ways the system behaves in order to meet the forecast energy demand. This paper proposes a new model to generate natural inflow energy scenarios in the long-term operation planning of large-sized hydrothermal systems. This model is based on the Periodic Autoregressive Model, PAR (p), where the identification of the p orders is based on the significance of the Partial Autocorrelation Function (PACF) estimated via Bootstrap, an intensive computational technique. The scenarios generated through this new technique were applied to the operation planning of the Brazilian Electrical System (BES), using the previously developed methodology of stochastic dynamic programming based on Convex Hull algorithm (SDP-CHull). The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders models in most cases which retains the statistical characteristics of the original series. Additionally it presents a closer total mean operation cost when compared to the cost obtained via historic series. (C) 2012 Elsevier B.V. All rights reserved.

关键词： OR in energy Long-term operation planning Periodic model Bootstrap stochastic dynamic programming

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Optimization of Perimeter Patrol Operations Using Unmanned Aerial Vehicles

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JOURNAL OF GUIDANCE CONTROL AND dynamicS 2012年第2期35卷 434-441页

作者： Kalyanam, Krishnamoorthy Chandler, Phil Pachter, Meir Darbha, Swaroop Infoscitex Corp Dayton OH 45431 USA USAF Res Lab Control Design & Anal Branch Wright Patterson AFB OH 45433 USA USAF Inst Technol Dept Elect Engn Wright Patterson AFB OH 45433 USA Texas A&M Univ Dept Mech Engn College Stn TX 77843 USA

This paper addresseses the following base perimeter patrol problem: a team of unmanned aerial vehicles (UAVs) equipped with cameras and a remotely located operator cooperatively perform the task of perimeter surveillance. There are m alert stations/sites on the perimeter where a nearby breaching of the perimeter by an intruder is flagged by an unattended ground sensor (UGS). To determine whether an incursion flagged by a UGS is a false alarm or a real threat, a patrolling UAV flies to the alert site to investigate the alert. The longer a UAV dwells (loiters) at an alert site, the more information it gathers;however, this also increases the delay in responding to other alerts. The decision problem for a UAV is to determine the optimal dwell time so as to maximize the expected payoff. In this paper, patrols consisting of one and two UAVs are considered. A stochastic dynamic programming approach is employed to obtain optimal policies for the patrolling UAVs. Theoretical performance bounds from queueing systems literature have been used to benchmark the optimal controller. Also, simulation results for the optimal patrols showing the expected information gained and response time for different alert arrival rates are presented.

关键词： UAV stochastic dynamic programming Sensors Markov Decision Process Monte Carlo Simulation Probability Density Functions MATLAB

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A fuzzy-Markov-chain-based analysis method for reservoir operation

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stochastic ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2012年第3期26卷 375-391页

作者： Fu, D. Z. Li, Y. P. Huang, G. H. N China Elect Power Univ MOE Key Lab Reg Energy Syst Optimizat SC Energy & Environm Res Acad Beijing 102206 Peoples R China

In this study, a fuzzy-Markov-chain-based stochastic dynamic programming (FM-SDP) method is developed for tackling uncertainties expressed as fuzzy sets and distributions with fuzzy probability (DFPs) in reservoir operation. The concept of DFPs used in Markov chain is presented as an extended form for expressing uncertainties including both stochastic and fuzzy characteristics. A fuzzy dominance index analysis approach is proposed for solving multiple fuzzy sets and DPFs in the proposed FM-SDP model. Solutions under a set of alpha-cut levels and fuzzy dominance indices can be generated by solving a series of deterministic submodels. The developed method is applied to a case study of a reservoir operation system. Solutions from FM-SDP provide a range of desired water-release policies under various system conditions for reservoir operation decision makers, reflecting dynamic and dual uncertain features of water availability simultaneously. The results indicate that the FM-SDP method could be applicable to practical problems for decision makers to obtain insight regarding the tradeoffs between economic and system reliability criteria. Willingness to obtain a lower benefit may guarantee meeting system-constraint demands;conversely, a desire to acquire a higher benefit could run into a higher risk of violating system constraints.

关键词： Decision making Dual uncertainties Fuzzy Markov chain stochastic dynamic programming Reservoir operation

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Partial Equilibrium in Risk-Based Production Decisions

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NAVAL RESEARCH LOGISTICS 2012年第1期59卷 1-17页

作者： Kamrad, Bardia Siddique, Akhtar Ernst, Ricardo Georgetown Univ McDonough Sch Business Washington DC 20057 USA Off Comptroller Currency Enterprise Risk Anal Div Washington DC 20219 USA

Characteristically, a small subset of operational problems admit risk neutrality when contingent claims methodology were used in their analysis. That is, for the majority of manufacturing and production problems, operating cash flows are not directly linked to prices of traded assets. However, to the extent that correlations can be estimated, the methodology's applicability to a broader set of operational problems is supported. Our article addresses this issue with the objective of extending the use of contingent claims techniques to a larger set of operational problems. In broad terms, this objective entails a partial equilibrium approach to the problem of valuing uncertain cash flows. To this end, we assume risk aversion and cast our approach within Merton's intertemporal capital asset pricing model. In this context, we formulate a "generic" production valuation model that is framed as an exercise in stochastic optimal control. The model is versatile in its characterization and can easily be adapted to accommodate a wide-ranging set of risk-based operational problems where the underlying sources of uncertainty are not traded. To obtain results, the model is recast as a stochastic dynamic program to be solved numerically. The article addresses a number of fundamental issues in the analysis risk based decision problems in operations. First, in the approach provided, decisions are analyzed under a properly defined risk structure. Second, the process of analysis leads to suitably adjusted probability distributions through which, appropriately discounted expectations are derived. Third, through consolidating existing concepts into a standard and adaptable framework, we extend the applicability of contingent claims methodology to a broader set of operational problems. The approach is advantageous as it obviates the need for exogenously specifying utility functions or discount rates. (C) 2011 Wiley Periodicals, Inc. Naval Research Logistics 59: 1-17, 2012

关键词： contingent claims analysis in production operations control theory Merton's intertemporal capital asset pricing model stochastic dynamic programming risk management in operations

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