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A Fully Polynomial-Time Approximation Scheme for Single-Item stochastic Inventory Control with Discrete Demand

MATHEMATICS OF OPERATIONS RESEARCH 2009年第3期34卷 674-685页

作者： Halman, Nir Klabjan, Diego Mostagir, Mohamed Orlin, Jim Simchi-Levi, David MIT Dept Civil & Environm Engn Cambridge MA 02139 USA Hebrew Univ Jerusalem Sch Business Adm IL-91905 Jerusalem Israel Northwestern Univ Dept Ind & Management Sci Evanston IL 60208 USA CALTECH Div Humanities & Social Sci Pasadena CA 91125 USA MIT Alfred P Sloan Sch Management Cambridge MA 02139 USA

The single-item stochastic inventory control problem is to find an inventory replenishment policy in the presence of independent discrete stochastic demands under periodic review and finite time horizon. In this paper... 详细信息

关键词： stochastic inventory management approximation algorithms stochastic dynamic programming

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Managing the impact of invasive species: the value of knowing the density-impact curve

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ECOLOGICAL APPLICATIONS 2009年第2期19卷 376-386页

作者： Yokomizo, Hiroyuki Possingham, Hugh P. Thomas, Matthew B. Buckley, Yvonne M. CSIRO Sustainable Ecosyst St Lucia Qld 4067 Australia Univ Queensland Sch Integrat Biol Ctr Ecol Brisbane Qld 4072 Australia Penn State Univ Ctr Infect Dis Dynam University Pk PA 16802 USA Penn State Univ Dept Entomol Chem Ecol Lab University Pk PA 16802 USA

Economic impacts of invasive species worldwide are substantial. Management strategies have been incorporated in population models to assess the effectiveness of management for reducing density, with the implicit assumption that economic impact of the invasive species will also decline. The optimal management effort, however, is that which minimizes the sum of both the management and impact costs. The relationship between population density and economic impact (what we call the "density-impact curve'') is rarely examined in a management context and could take several nonlinear forms. Here we determine the effects of population dynamics and density-impact curves of different shapes on optimal management effort and discover cases where management is either highly effective or a waste of resources. When an inaccurate density-impact curve is used, the increase in total costs due to over- or underinvestment in management can be large. We calculate the increase in total costs incurred if the density-impact curve is incorrect and find that the greater the maximum impact caused by an invasive species, the more important it is not only to reduce its density, but also to know the shape of the density-impact relationship accurately. Lack of information regarding the relationship between density and economic impact causes the most acute problems for invaders that cause high impact at low density, where management typically will be too little, too late. For species that are only problematic at high density, ignorance of the density-impact curve can lead to overinvestment in management with little reduction in impact.

关键词： cost of impact density dependence invasive species modeling economic impact of pests and their control stochastic dynamic programming value of information weed management

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Optimal policy for a dynamic, non-stationary, stochastic inventory problem with capacity commitment

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2009年第2期199卷 400-408页

作者： Xu, Ningxiong Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA

This paper studies a single-product, dynamic, non-stationary, stochastic inventory problem with capacity commitment, in which a buyer purchases a fixed capacity from a supplier at the beginning of a planning horizon and the buyer's total cumulative order quantity over the planning horizon is constrained with the capacity. The objective of the buyer is to choose the capacity at the beginning of the planning horizon and the order quantity in each period to minimize the expected total cost over the planning horizon. We characterize the structure of the minimum sum of the expected ordering, storage and shortage costs in a period and thereafter and the optimal ordering policy for a given capacity. Based on the structure, we identify conditions under which a myopic ordering policy is optimal and derive an equation for the optimal capacity commitment. We then use the optimal capacity and the myopic ordering policy to evaluate the effect of the various parameters on the minimum expected total cost over the planning horizon. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Additive function Capacity Inventory Myopic policy stochastic dynamic programming

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Production and changeover control policies of a class of failure prone buffered flow-shops

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PRODUCTION PLANNING & CONTROL 2009年第8期20卷 785-800页

作者： Hajji, Adnene Gharbi, Ali Kenne, Jean-Pierre Hidehiko, Yamamoto Univ Quebec Dept Automated Prod Engn LCCSP Lab Ecole Technol Super Quebec City PQ Canada Univ Quebec Dept Mech Engn LITP Lab Ecole Technol Super Quebec City PQ Canada Gifu Univ Fac Engn Dept Human & Informat Syst Gifu 50111 Japan

This article deals with a stochastic optimal control problem for a class of buffered multi-parts flow-shops manufacturing system. The involved machines are subject to random breakdowns and repairs. The flow-shop under consideration is not completely flexible and hence requires setup time and cost in order to switch the production from a part type to another, this changeover is carried on the whole line. Our objective is to find the production plan and the sequence of setups that minimise the cost function, which penalises inventories/backlogs and setups. A continuous dynamic programming formulation of the problem is presented. Then, a numerical scheme is adopted to solve the obtained optimality conditions equations for a two buffered serial machines two parts case. A complete heuristic policy, based on the numerical observations which describe the optimal policies in system states, is developed. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy. Moreover, based on our observations and existent research studies extension to cover more complex flow-shops is henceforth possible. The robustness of such a policy is illustrated through sensitivity analysis.

关键词： production and setup control buffered flow-shops stochastic dynamic programming numerical methods production control real-time scheduling

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Urban water management: optimal price and investment policy under climate variability

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AUSTRALIAN JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS 2009年第2期53卷 175-192页

作者： Hughes, Neal Hafi, Ahmed Goesch, Tim Australian Bur Agr & Resource Econ Canberra ACT 2601 Australia

Australian urban water utilities face a significant challenge in designing appropriate demand management and supply augmentation policies in the presence of significant water scarcity and climate variability. This article considers the design of optimal demand management and supply augmentation policies for urban water. In particular, scarcity pricing is considered as a potential alternative to the predominant demand management policy of water restrictions. A stochastic dynamic programming model of an urban water market is developed based on data from the ACT region. Given a specification of the demand and supply for urban water state dependent optimal price and investment policies are estimated. The results illustrate how the optimal urban water price varies inversely with the prevailing storage level and how the optimal timing of investment differs significantly between rain dependent and rain independent augmentation options.

关键词： demand management scarcity pricing stochastic dynamic programming supply augmentation urban water

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Structured decision making as a conceptual framework to identify thresholds for conservation and management

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ECOLOGICAL APPLICATIONS 2009年第5期19卷 1079-1090页

作者： Martin, Julien Runge, Michael C. Nichols, James D. Lubow, Bruce C. Kendall, William L. Univ Florida Florida Cooperat Fish & Wildlife Res Unit Gainesville FL 32611 USA US Geol Survey Patuxent Wildlife Res Ctr Laurel MD 20708 USA Colorado State Univ Nat Resource Ecol Lab Ft Collins CO 80523 USA

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of the decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (system models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component

关键词： adaptive resource management ecological thresholds patch occupancy models stochastic dynamic programming structured decision making

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Optimal adaptive management for the translocation of a threatened species

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ECOLOGICAL APPLICATIONS 2009年第2期19卷 515-526页

作者： Rout, Tracy M. Hauser, Cindy E. Possingham, Hugh P. Univ Melbourne Sch Bot Commonwealth Environm Res Facil Appl Environm Dec Parkville Vic 3010 Australia Univ Queensland Sch Integrat Biol Commonwealth Environm Res Facil Appl Environm Dec St Lucia Qld 4072 Australia Univ Melbourne Australian Ctr Excellence Risk Anal Parkville Vic 3010 Australia

Active adaptive management (AAM) is an approach to wildlife management that acknowledges our imperfect understanding of natural systems and allows for some resolution of our uncertainty. Such learning may be characterized by risky strategies in the short term. Experimentation is only considered acceptable if it is expected to be repaid by increased returns in the long term, generated by an improved understanding of the system. By setting AAM problems within a decision theory framework, we can find this optimal balance between achieving our objectives in the short term and learning for the long term. We apply this approach to managing the translocation of the bridled nailtail wallaby (Onychogalea fraenata), an endangered species from Queensland, Australia. Our task is to allocate captive-bred animals, between two sites or populations to maximize abundance at the end of the translocation project. One population, at the original site of occupancy, has a known growth rate. A population potentially could be established at a second site of suitable habitat, but we can only learn the growth rate of this new population by monitoring translocated animals. We use a mathematical programming technique called stochastic dynamic programming, which determines optimal management decisions for every possible management trajectory. We find optimal strategies under active and passive adaptive management, which enables us to examine the balance between learning and managing directly. Learning is more often optimal when we have less prior information about the uncertain population growth rate at the new site, when the growth rate at the original site is low, and when there is substantial time remaining in the translocation project. Few studies outside the area of optimal harvesting have framed AAM within a decision theory context. This is the first application to threatened species translocation.

关键词： active adaptive management Bayesian statistics bridled nailtail wallaby decision theory Onychogalea fraenata optimization Queensland, Australia reintroduction stochastic dynamic programming translocation wildlife management

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stochasticity and controllability of nutrient sources in foraging: host-feeding and egg resorption in parasitoids

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ECOLOGICAL MONOGRAPHS 2009年第3期79卷 465-483页

作者： Richard, Romain Casas, Jerome Univ Tours IRBI CNRS UMR 6035 F-37200 Tours France

The trade-off between current and future reproduction has led many organisms experiencing stochastic reproductive opportunities to be flexible in their resource acquisition and allocation rules. Many parasitoid wasps display flexibility in choosing to host-feed or oviposit on a host and possess an ovarian system enabling nutrient reallocation through egg resorption. The aim of this work is to assess the complementary adaptive values of host-feeding and egg resorption as functions of host density in a synovigenic (maturing eggs throughout its adult life) parasitoid, Eupelmus vuilleti (Hymenoptera: Eupelmidae), for which there is a uniquely large base of relevant knowledge. We developed a series of models of increasing complexity, starting from a simple analytical model without egg resorption and moving on to data-rich stochastic dynamic programming models (SDP), without and with resorption. The analytical model enabled the characterization of two, long-and short-term, foraging strategies which determine host usage. Oviposition is favored at low host densities (leading to the short-term strategy), while host-feeding is favored at high host densities (leading to the long-term strategy). The change of strategy occurs abruptly at intermediate host densities. The SPD models not only confirmed these predictions, but also identified smaller regions of decisions driven by day/night cycles and approaching death and predicted major shifts in daily activity patterns according to the chosen strategy. The fitness gain due to resorption is highest at intermediate host densities, where females adopt the riskier but more pro. table long-term strategy. Such a result contrasts with the generally held view, which assumes highest gains at the lowest host densities. A counterintuitive result is the higher prevalence of host-feeding associated with the ability to resorb eggs. Considering egg resorption as a last-resort strategy is underestimating its adaptive value, which is best understo

关键词： capital and income resources energy management hoarding host handling life-history strategies mixed diet oosorption optimal foraging resource allocation resource acquisition stochastic dynamic programming synovigeny

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Long-Term Hydropower Scheduling Based on Deterministic Nonlinear Optimization and Annual Inflow Forecasting Models

Long-Term Hydropower Scheduling Based on Deterministic Nonli...

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Bucharest PowerTech Conference

作者： Zambelli, Monica S. Luna, Ivette Soares, Secundino Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil

ISBN: (纸本)9781424422340

This paper proposes an operational policy for long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models using an open-loop feedback control framework. The optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, then disaggregating it on a monthly basis. In order to focus on the ability of the approach to handle the stochastic nature of the problem, a case study with a single-reservoir system is considered. The performance of the proposed approach is evaluated by simulation over the historical inflow records and compared to that of the stochastic dynamic programming approach. The results show that the proposed approach leads to a better operational performance of the plant, providing lower spillages and higher average hydropower efficiency and generation.

关键词： fuzzy inference systems inflow forecasting long-term hydro-thermal scheduling nonlinear optimization stochastic dynamic programming

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stochastic Optimization Based on Principal-agent Problem

Stochastic Optimization Based on Principal-agent Problem

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2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

作者： Ren, Xiaoyu Shao, Xinping Li, Shenghong Zhejiang Univ Dept Math Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781424442461

By the theory of stochastic dynamic programming, we provide the methods for deriving the optimal rules. In this paper, we make two models in dynamic state process to maximize the expected utility of the agent and then obtain the famous Hamilton-Jacobi-Bellman equation. Furthermore, we derive explicit form solution and closed-form solution of the optimal equations for given utility functions.

关键词： principal-agent problem stochastic dynamic programming stochastic differential equation stochastic optimal control HJB equation

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