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A New dynamic programming Decomposition Method for the Network Revenue Management Problem with Customer Choice Behavior

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PRODUCTION AND OPERATIONS MANAGEMENT 2010年第5期19卷 575-590页

作者： Kunnumkal, Sumit Topaloglu, Huseyin Indian Sch Business Hyderabad 500032 Andhra Pradesh India Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA

In this paper, we propose a new dynamic programming decomposition method for the network revenue management problem with customer choice behavior. The fundamental idea behind our dynamic programming decomposition method is to allocate the revenue associated with an itinerary among the different flight legs and to solve a single-leg revenue management problem for each flight leg in the airline network. The novel aspect of our approach is that it chooses the revenue allocations by solving an auxiliary optimization problem that takes the probabilistic nature of the customer choices into consideration. We compare our approach with two standard benchmark methods. The first benchmark method uses a deterministic linear programming formulation. The second benchmark method is a dynamic programming decomposition idea that is similar to our approach, but it chooses the revenue allocations in an ad hoc manner. We establish that our approach provides an upper bound on the optimal total expected revenue, and this upper bound is tighter than the ones obtained by the two benchmark methods. Computational experiments indicate that our approach provides significant improvements over the performances of the benchmark methods.

关键词： network revenue management customer choice approximate dynamic programming pricing airline

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An inventory system with single distributor and multiple retailers: Operating scenarios and performance comparison

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2010年第1期128卷 434-444页

作者： Geng, Wei Qiu, Minmin Zhao, Xiaobo Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China SW Jiaotong Univ Sch Econ & Management Chengdu 610031 Peoples R China IBM China Res Lab Beijing 100193 Peoples R China

We consider a single-distributor multi-retailer inventory system in six operating scenarios for a finite horizon. Five scenarios are in decentralized control and the other one is in centralized control. approximate dynamic programming (ADP) procedures are developed to obtain system performances for scenarios in decentralized control, and a stochastic dynamic programming approach is applied to the scenario in centralized control. The efficiency and effectiveness of ADP procedures are demonstrated by numerical results. Taking the system performance in centralized control as a benchmark, system performances in decentralized control are investigated and compared for different operating scenarios. The results provide useful insights in system planning and operations. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Inventory Operating scenario Performance approximate dynamic programming

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Online Solution of Nonlinear Two-Player Zero-Sum Games Using Synchronous Policy Iteration 49

Online Solution of Nonlinear Two-Player Zero-Sum Games Using...

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49th IEEE Conference on Decision and Control (CDC)

作者： Vamvoudakis, Kyriakos G. Lewis, F. L. Univ Texas Arlington Automat & Robot Res Inst Ft Worth TX 76118 USA

ISBN: (纸本)9781424477463

In this paper we present an online gaming algorithm based on policy iteration to solve the continuous-time (CT) two-player zero-sum game with infinite horizon cost for nonlinear systems with known dynamics. That is, the algorithm learns online in real-time the solution to the game design HJI equation. This method finds in real-time suitable approximations of the optimal value, and the saddle point control policy and disturbance policy, while also guaranteeing closed-loop stability. The adaptive algorithm is implemented as an actor/critic structure which involves simultaneous continuous-time adaptation of critic, control actor, and disturbance neural networks. We call this online gaming algorithm 'synchronous' zero-sum game policy iteration. A persistence of excitation condition is shown to guarantee convergence of the critic to the actual optimal value function. Novel tuning algorithms are given for critic, actor and disturbance networks. The convergence to the optimal saddle point solution is proven, and stability of the system is also guaranteed. Simulation examples show the effectiveness of the new algorithm.

关键词： Synchronous Zero-Sum Game Policy Iteration H-infinity Hamilton-Jacobi-Isaacs equation approximate dynamic programming Policy Iteration Nash-equilibrium Persistence of Excitation

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Adaptive Routing Scheme for Emerging Wireless Ad hoc Networks

Adaptive Routing Scheme for Emerging Wireless Ad hoc Network...

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29th IEEE International Symposium on Reliable Distributed Systems

作者： Eslamnour, Behdis Jagannathan, S. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO USA

ISBN: (纸本)9780769542508

Node mobility causes fading wireless channels, which in turn renders topology changes in emerging wireless ad hoc networks. In this paper, on-line estimators and Markov models are utilized to estimate fading channel conditions. Using the estimated channel conditions as well as queue occupancy, available energy and link delay, approximate dynamic programming (ADP) techniques are utilized to find dynamic routes, while solving discrete-time Hamilton-Jacobi-Bellman (HJB) equation forward-in-time for route cost in multichannel multi-interface networks. The performance of the proposed load balancing method in the presence of fading channels and the performance of the optimal route selection approach for multichannel multi-interface wireless ad hoc network is evaluated by extensive simulations and comparing it to AODV.

关键词： Adaptive dynamic Routing approximate dynamic programming Wireless Ad hoc Networks Multi-path Load Balancing Multi-channel Multi-interface Routing

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Integer programming Model and dynamic programming Based Heuristic Algorithm for the Heavy Plate Shuffling Problem in the Iron and Steel Industry

Integer Programming Model and Dynamic Programming Based Heur...

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International Conference on Logistics Systems and Intelligent Management

作者： Zhao, Ren Tang, Lixin Northeastern Univ Logist Inst Liaoning Key Lab Mfg Syst & Logist Shenyang 110004 Peoples R China

ISBN: (纸本)9781424473281

This paper presents the heavy plate shuffling problem (HPSP), which is motivated by the logistics operations of the heavy plate outgoing in the finished product warehouse. The required plates are taken out according to the given retrieval sequence If a required plate is located under other plates in a stack, shuffling occurs. HPSP determines the restorage spaces for plates to be shuffled while minimizing the total shuffling number and the total crane traveling distance. The problem is formulated as an integer-programming model by considering the practical stacking and shuffling constraints on the heave plate. Furthermore, a two-phase heuristic algorithm based on dynamic programming is proposed to solve it. In the first phase, an initial solution is generated by approximate dynamic programming. And then it will be improved through an iterated local search in the second phase. Compared with the current used approach, computational results show that the proposed new method reduces the total increased shuffling number by 10.18% and the total traveling distance by 3.27% on average, respectively.

关键词： Finished Product Warehouse Heavy Plate Shuffling approximate dynamic programming Heuristics

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PH Optimal Control in the Clarifying Process of Sugar Cane Juice Based on DHP

PH Optimal Control in the Clarifying Process of Sugar Cane J...

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6th International Conference on Intelligent Computing

作者： Lin, Xiaofeng Teng, Qianli Song, Chunning Song, Shaojian Liu, Huixia Guangxi Univ Sch Elect Engn Nanning 530004 Guangxi Peoples R China Guangxi Univ Sch Light Ind & Food Engn Nanning 530004 Guangxi Peoples R China

ISBN: (纸本)9783642149214

This paper proposes the use of error back proragation (BP) neural network to efficiently control the pH in the clarifying process of sugar cane juice In particular approximate dynamic programming (ADP) is Implemented to solve this nonlinear control problem The neural network model of the clarifying process of sugar cane juice and a neural network controller based on the idea of ADP to achieve optimal control are developed The strategy and naming procedures of dual heuristic programming (DHP) are discussed The re suit is the plant has been effectively controlled using DHP

关键词： approximate dynamic programming dual heuristic programming optimal control clarifying process

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Optimal Control for Industrial Sucrose Crystallization with Action Dependent Heuristic dynamic programming

Optimal Control for Industrial Sucrose Crystallization with ...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Lin, Xiaofeng Zhang, Heng Wei, Li Liu, Huixia Guangxi Univ Sch Elect Engn Nanning 530004 Guangxi Provinc Peoples R China Guangxi Univ Light Ind & Food Engn Inst Nanning Peoples R China

ISBN: (纸本)9781424467129

This paper applies a neural-network-based approximate dynamic programming (ADP) method, namely, the action dependent heuristic dynamic programming (ADHDP), to an industrial sucrose crystallization optimal control problem. The industrial sucrose crystallization is a nonlinear and slow time-varying process. It is quite difficult to establish a precise mechanism model of the crystallization because of complex internal mechanism and interacting variables. We developed a neural network model of the crystallization based on the data from the actual sugar boiling process of sugar factory. The ADHDP is a learning- and approximation-based approach which can solve the optimization control problem of nonlinear system. The paper covers the basic principle of this learning scheme and the design of neural network controller based on the approach. The result of simulation shows the controller based on action dependent heuristic dynamic programming approach can optimize industrial sucrose crystallization.

关键词： Sucrose crystallization Sugar boiling Neural networks approximate dynamic programming Action dependent heuristic dynamic programming

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American option pricing under stochastic volatility: an empirical evaluation

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COMPUTATIONAL MANAGEMENT SCIENCE 2010年第2期7卷 189-206页

作者： AitSahlia, Farid Goswami, Manisha Guha, Suchandan Univ Florida Dept Ind & Syst Engn Weil Hall 303 Gainesville FL 32611 USA

Over the past few years, model complexity in quantitative finance has increased substantially in response to earlier approaches that did not capture critical features for risk management. However, given the preponderance of the classical Black-Scholes model, it is still not clear that this increased complexity is matched by additional accuracy in the ultimate result. In particular, the last decade has witnessed a flurry of activity in modeling asset volatility, and studies evaluating different alternatives for option pricing have focused on European-style exercise. In this paper, we extend these empirical evaluations to American options, as their additional opportunity for early exercise may incorporate stochastic volatility in the pricing differently. Specifically, the present work compares the empirical pricing and hedging performance of the commonly adopted stochastic volatility model of Heston (Rev Financial Stud 6:327-343, 1993) against the traditional constant volatility benchmark of Black and Scholes (J Polit Econ 81:637-659, 1973). Using S&P 100 index options data, our study indicates that this particular stochastic volatility model offers enhancements in line with their European-style counterparts for in-the-money options. However, the most striking improvements are for out-of-the-money options, which because of early exercise are more valuable than their European-style counterparts, especially when volatility is stochastic.

关键词： Stochastic volatility Indirect inference Model calibration American option pricing S&P 100 index approximate dynamic programming

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American option pricing under stochastic volatility: an efficient numerical approach

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COMPUTATIONAL MANAGEMENT SCIENCE 2010年第2期7卷 171-187页

作者： AitSahlia, Farid Goswami, Manisha Guha, Suchandan Univ Florida Dept Ind & Syst Engn Weil Hall 303 Gainesville FL 32611 USA

This paper develops a new numerical technique to price an American option written upon an underlying asset that follows a bivariate diffusion process. The technique presented here exploits the supermartingale representation of an American option price together with a coarse approximation of its early exercise surface that is based on an efficient implementation of the least-squares Monte-Carlo algorithm (LSM) of Longstaff and Schwartz (Rev Financ Stud 14:113-147, 2001). Our approach also has the advantage of avoiding two main issues associated with LSM, namely its inherent bias and the basis functions selection problem. Extensive numerical results show that our approach yields very accurate prices in a computationally efficient manner. Finally, the flexibility of our method allows for its extension to a much larger class of optimal stopping problems than addressed in this paper.

关键词： American option pricing Optimal stopping approximate dynamic programming Stochastic volatility Doob-Meyer decomposition Monte Carlo

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Discrete Dual-Heuristic programming in 3DOF Manipulator Control

Discrete Dual-Heuristic Programming in 3DOF Manipulator Cont...

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10th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2010)

作者： Gierlak, Piotr Szuster, Marcin Zylski, Wieslaw Rzeszow Univ Technol Dept Appl Mech & Robot PL-35959 Rzeszow Poland

ISBN: (纸本)9783642132315

In this paper we propose a discrete tracking control system for 3 degrees of freedom (DOF) robotic manipulator control. The control system is composed of Adaptive Critic Design (ACD), a PD controller and a supervisory term derived from the Lyapunov stability theory. ACD in Dual Heuristic programming (DHP) configuration consists of two structures realized in a form of neural networks (NN): actor - generates a control signal and critic approximates a derivative of the cost function with respect to the state. The control system works on-line, does not require a preliminary learning and uses the 3DOF manipulator dynamics model for a state prediction in ACD structure. Verification of the proposed control algorithm was realized on a SCORBOT 4PC manipulator.

关键词： approximate dynamic programming Dual Heuristic programming Neural Networks Robotic Manipulators Tracking Control

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