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Cooperative optimal pricing for stochastic access control in overlaid radio access networks

TELECOMMUNICATION SYSTEMS 2015年第1期60卷 3-16页

作者： Zhang, Haoran Huang, Liusheng Xu, Hongli Sun, Quan Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Suzhou Inst Adv Study Suzhou 215123 Jiangsu Peoples R China

As radio access technology has been greatly developed nowadays, dynamic spectrum allocation (DSA), with which secondary users (SUs) are able to access the spectrum allocated to primary users becomes an important method for service providers to efficiently utilize limited wireless resources. Meanwhile, call admission control of SUs in an environment with multiple possible heterogenous wireless networks supporting DSA is also evolving. In this paper, we propose a pricing-based resource allocation scheme for multiple cooperative providers. According to this scheme, SUs are charged based on current system state by service provider to join one of several radio access networks (RANs). We integrate stochastic call admission control with dynamic pricing and formulate the problem of maximizing the expectation of system revenue in the long run as an infinite horizon average reward problem which can be solved by stochastic dynamic programming. Characteristics of the optimal pricing policy are analyzed based on series of computation results under various system conditions. Simulations carried out for a small network model show that a maximum profit can be achieved under our scheme and this maximum value varies with primary user arriving rates, primary user blocking punishments and channel capacities. Using our scheme, the system income could be improved by nearly 60 % in particular cases. Also, if each RAN's probability of admitting a SU is carefully chosen, the system profit can be maximized and primary user blocking count minimized.

关键词： Radio access network dynamic spectrum allocation Pricing stochastic dynamic programming Profit

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A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts

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WATER RESOURCES RESEARCH 2014年第12期50卷 9267-9286页

作者： Xu, Wei Zhang, Chi Peng, Yong Fu, Guangtao Zhou, Huicheng Dalian Univ Technol Sch Hydraul Engn Dalian Peoples R China Chongqing Jiaotong Univ Coll River & Ocean Engn Chongqing Peoples R China Univ Exeter Ctr Water Syst Coll Engn Math & Phys Sci Exeter Devon England

This paper presents a new Two Stage Bayesian stochastic dynamic programming (TS-BSDP) model for real time operation of cascaded hydropower systems to handle varying uncertainty of inflow forecasts from Quantitative Precipitation Forecasts. In this model, the inflow forecasts are considered as having increasing uncertainty with extending lead time, thus the forecast horizon is divided into two periods: the inflows in the first period are assumed to be accurate, and the inflows in the second period assumed to be of high uncertainty. Two operation strategies are developed to derive hydropower operation policies for the first and the entire forecast horizon using TS-BSDP. In this paper, the newly developed model is tested on China's Hun River cascade hydropower system and is compared with three popular stochastic dynamic programming models. Comparative results show that the TS-BSDP model exhibits significantly improved system performance in terms of power generation and system reliability due to its explicit and effective utilization of varying degrees of inflow forecast uncertainty. The results also show that the decision strategies should be determined considering the magnitude of uncertainty in inflow forecasts. Further, this study confirms the previous finding that the benefit in hydropower generation gained from the use of a longer horizon of inflow forecasts is diminished due to higher uncertainty and further reveals that the benefit reduction can be substantially mitigated through explicit consideration of varying magnitudes of forecast uncertainties in the decision-making process.

关键词： Bayesian approach hydropower quantitative precipitation forecast stochastic dynamic programming uncertainty modeling

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Collaboration in tool development and capacity investments in high technology manufacturing networks

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第3期187卷 962-977页

作者： Dulluri, Sandeep Raghavan, N. R. Srinivasa Indian Inst Sci Dept Management Studies Bangalore 560012 Karnataka India

The procurement of capital intensive tools for hi-tech industries is one of the most complex tasks. Astronomical amounts of capital are invested in the processing equipment. Further, there is a large effort from the original equipment manufacturer (OEM) in customizing the high capital intensive equipment to suit their production process. This problem has received little attention from the quantitative decision making literature. For the first time we analyze the problem of OEM deciding on collaborating with the tool supplier via a special type of contract which we refer as "collaboration options". We show that there are benefits to both the OEM and the tool suppliers from the collaboration. We develop a two-stage strategic model for collaborative tool development and optimal capacity investment. In the first stage, we solve an optimal collaboration problem faced by the OEM. We formulate this problem as a discrete time stochastic stopping problem for a finite horizon. We use a policy iteration algorithm for deciding on optimal collaboration policy. In the second stage, we model the optimal investment decision of the OEM in tool development and capacity acquisition using real options theory. Unlike the traditional options literature, we consider the option value determined from the first stage model and decide on the optimal amount for investment. (C) 2006 Published by Elsevier B.V.

关键词： stochastic dynamic programming inverse real options optimal trajectory of investments volatility

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Container relocation problem with time windows for container departure

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第3期252卷 1031-1039页

作者： Ku, Dusan Arthanari, Tiru S. Univ Auckland Sch Business Dept Informat Syst & Operat Management Private Bag 92019 Auckland 1421 New Zealand

The blocks relocation problem is a classic combinatorial optimisation problem that occurs in daily operations for facilities that use block stacking systems. In the block stacking method, blocks can be stored on top of each other in order to utilise the limited surface of a storage area. When there is a predetermined pickup order among the blocks, this stacking method inevitably leads to the reshuffling moves for blocks stored above the target block and the minimisation of such unproductive reshuffling moves is of a primary concern to industry practitioners. A container terminal is a typical place where this problem arises, thus the problem being also referred to as the container relocation problem. In this study, we consider departure time windows for containers, which are usually revealed by the truck appointment system in port container terminals. We propose a stochastic dynamic programming model to calculate the minimum expected number of reshuffles for a stack of containers which all have departure time windows. The model is solved with a search-based algorithm in a tree search space, and an abstraction heuristic is proposed to improve the time performance. To overcome the computational limitation of exact methods, we develop a heuristic called the expected reshuffling index (ERI) and evaluate its performance. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Logistics Container relocation problem Truck appointment system stochastic dynamic programming Abstraction heuristics

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A reinforcement learning methodology for a human resource planning problem considering knowle dge-based promotion

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SIMULATION MODELLING PRACTICE AND THEORY 2017年 79卷 87-99页

作者： Karimi-Majd, Amir-Mohsen Mahootchi, Masoud Zakery, Amir Amirkabir Univ Technol Ind Engn & Management Syst Dept 424 Hafez Ave Tehran 158754413 Iran

This paper addresses a combined problem of human resource planning (HRP) and production-inventory control for a high-tech industry, wherein the human resource plays a critical role. The main characteristics of this resource are the levels of "knowledge" and the learning process. The learning occurs during the production process in which a worker can promote to the upper knowledge level. Workers in upper levels have more productivity in the production. The objective is to maximize the expected profit by deciding on the optimal numbers of workers in various knowledge levels to fulfill both production and training requirement. As taking an action affects next periods' decisions, the main problem is to find the optimal hiring policy of non-skilled workers in long-time horizon. Thus, we develop a reinforcement learning (RL) model to obtain the optimal decision for hiring workers under the demand uncertainty. The proposed interval-based policy of our RL model, in which for each state there are multiple choices, makes it more flexible. We also embed some managerial issues such as layoffand overtime-working hours into the model. To evaluate the proposed methodology, stochastic dynamic programming (SDP) and a conservative method implemented in a real case study are used. We study all these methods in terms of four criteria: average obtained profit, average obtained cost, the number of newhired workers, and the standard deviation of hiring policies. The numerical results confirm that our developed method end up with satisfactory results compared to two other approaches. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Reinforcement learning Production-inventory control Human resource planning stochastic dynamic programming Knowledge-intensive

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dynamic Contract Trading in Spectrum Markets

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2014年第10期59卷 2856-2862页

作者： Kasbekar, G. S. Sarkar, S. Kar, K. Muthuswamy, P. K. Gupta, A. Indian Inst Technol Dept Elect Engn Bombay 400076 Maharashtra India Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Rensselaer Polytech Inst Troy NY 12180 USA

We propose a structured spectrum market and consider two basic types of spectrum contracts that can help attain desired flexibilities and trade-offs in terms of service quality, spectrum usage efficiency and pricing: long-term guaranteed-bandwidth contracts, and short-term opportunistic-access contracts. A primary provider (seller) and a secondary provider (buyer) creates and maintains a portfolio composed of an appropriate mix of these two types of contracts. We address the question of how the spectrum contract portfolio of a seller (buyer) in the spectrum market should be dynamically adjusted, so as to maximize return (minimize cost) subject to meeting the bandwidth demands of its own subscribers. We formulate the above question as a stochastic dynamic programming problem, and obtain structural properties of the optimal dynamic trading strategy that takes into account the current market prices of the contracts and the subscriber demand process in the decision-making.

关键词： Contract trading portfolio optimization spectrum markets stochastic dynamic programming

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Minimise long-term loss or maximise short-term gain? Optimal translocation strategies for threatened species

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ECOLOGICAL MODELLING 2007年第1期201卷 67-74页

作者： Rout, T. M. Hauser, C. E. Possingham, H. P. Univ Queensland Ctr Ecol St Lucia Qld 4072 Australia

Translocation is a useful management option for conservation of threatened animal species. It can be used to increase the range of a species, augment the numbers in a critical population, or establish new populations and hence spread the risk of extinction through local catastrophes. As it is an important and expensive conservation tool, translocation management decisions must be carefully considered, with the objective of the translocation project in mind. By analysing the translocation problem within a decision-theory framework, we find optimal management decisions that are rational and transparent. We illustrate our approach using a case study of the bridled nailtail wallaby (Onychoyalea fraenata). Our particular translocation question is: if we have a set number of wallabies to translocate in each time period and two translocation sites, how many wallabies should we put at each site given the state of each population to maximise the benefit to the species? We model the translocated populations with first-order Markov chain stochastic population models, and use stochastic dynamic programming to determine the optimal management decisions. We look at two sites with different growth rates - one increasing and one decreasing - and compare the optimal strategies for two different objective functions. The first is a long-term persistence objective function, which maximises the persistence of translocated populations a large number of time steps after the end of the translocation program. The second maximises total population size at the end of the translocation program. Although these objective functions are similar, they generate surprisingly different optimal translocation strategies. When maximising the long-term persistence of the translocated populations, translocation decisions are not important as long as an increasing population is established. This indicates that site quality - rather than the number and timing of translocations - primarily determines the long

关键词： threatened and endangered species wildlife management translocation decision theory stochastic dynamic programming

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A Factorial-based dynamic Analysis Method for Reservoir Operation Under Fuzzy-stochastic Uncertainties

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WATER RESOURCES MANAGEMENT 2013年第13期27卷 4591-4610页

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

In this study, a factorial-based fuzzy-stochastic dynamic programming (FFS-DP) method is developed for tackling multiple uncertainties including fuzziness, randomness and their interaction in reservoir operation management (ROM). FFS-DP is framed on the integration of stochastic dynamic programming, fuzzy-Markov chain, vertex analysis and factorial analysis techniques. It can not only deal with the conventional optimization problem for reflecting dynamic and uncertain features in ROM, but also obtain detailed effects of uncertain parameters and their interactions on the system performance. The developed method is applied to a case study of a reservoir operation system, where the local authority is in charge of allocating relative scant water to the downstream municipality. The results obtained can help the local authority identify desired water release policies under uncertain system conditions. Besides, the results simultaneously indicate that significant factors and their interactions can be identified in ROM. Moreover, the results can be further analyzed for generating optimal parameter inputs to obtain maximized system benefits.

关键词： Factorial analysis Fuzzy Markov chain Reservoir operation management stochastic dynamic programming Vertex analysis Uncertainty

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dynamic reserve site selection under contagion risk of deforestation

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ECOLOGICAL MODELLING 2007年第1期201卷 75-81页

作者： Sabbadin, R. Spring, D. Rabier, C. -E. [a]INRA de Toulouse Unité de Biométrie et Intelligence Artificielle BP 52627-31326 Castanet-Tolosan Cedex Castanet-Tolosan France[b]Australian Centre for Biodiversity School of Biological Sciences PO Box 18 Monash University Clayton Vic. 3800 Australia

Recently, dynamic reserve site selection models based on stochastic dynamic programming (SDP) have been proposed. The models consider a random development pattern in which the probability that a site will be developed is independent of the development status of other sites. However, development often takes the form of a contagion process in which the sites most likely to be developed are near sites that already have been developed. To consider site selections in such cases, we propose improved algorithms that make use of a graph representation of the sites network. The first formulation is an exact, dynamic programming algorithm, with which theoretical and experimental complexities are evaluated. The exact method can be applied only to small problems (less than 10 sites), but real-world problems may have hundreds or thousands of sites, implying that heuristic selection methods must be used. We provide a general framework for describing such heuristic solution methods, and propose a new heuristic method based on a parameterised reinforcement learning algorithm. The method allows us to compute a heuristic function by performing and exploiting many simulations of the deforestation process. We show that the method can be applied to problems with hundreds of sites, and demonstrate experimentally that it outperforms previously proposed heuristic methods in terms of the average number of species conserved. [Copyright &y& Elsevier]

关键词： Reserve selection Uncertainty Contagion process stochastic dynamic programming Reinforcement learning

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Operational level-based policies in production rate control of unreliable manufacturing systems with set-ups

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2006年第3期44卷 545-567页

作者： Gharbi, A Kenné, JP Hajji, A Univ Quebec Ecole Technol Super Automated Prod Engn Dept Prod Syst Design & Control Lab Montreal PQ H3C 1K3 Canada Univ Quebec Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada

This paper deals with the control of the production rates and set-up actions of an unreliable multiple-machine, multiple-product manufacturing system. Each part type can be processed for a specified period on one of the involved machines. When switching the production from one type to another, each machine requires both set-up time and set-up cost. Our objective is to determine the production rates and a sequence of set-ups in order to minimize the total set-up and surplus cost. As an analytical or even a numerical solution of the problem is very difficult to find, a combined approach is presented. The proposed approach is based on stochastic optimal control theory, discrete event simulation, experimental design and response surface methodology. It is proved experimentally that an extended version of the Hedging Corridor Policy is more realistic and guarantees better performance for two study cases. The first consists of the unreliable one-machine case facing exponential failure and repair time distribution. The second, which is more complex and where the optimal control theory may not be easily used to obtain the optimal control policy, consists of five machines facing non-exponential failure and repair time distributions. To illustrate the contribution of the paper and the robustness of the obtained control policy, numerical examples and sensitivity analysis are presented.

关键词： production rates set-up policy stochastic dynamic programming numerical methods simulation experimental design

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