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Optimal Scalability of FiWi Networks Based on Multistage stochastic programming and Policies

JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING 2017年第12期9卷 1172-1183页

作者： Peralta, Arturo Inga, Esteban Hincapie, Roberto Univ Politecn Salesiana GITEL Res Grp Cuenca Ecuador Univ Politecn Salesiana GIREI Res Grp Quito Ecuador Univ Pontificia Bolivariana GIDATI Res Grp Medellin Colombia

The large demand of bandwidth due to new and future applications from services being implemented such as smart grid (SG), smart cities (SC), and the Internet of Things has triggered a focus of interest from the scientific community on issues related to the planning and scalability of the communications infrastructure. In this paper, we present three new and important contributions regarding the deployment of FiWi networks that support users who benefit from the services provided by SG and SC. We first propose a planning model, and then formulate a mixed linear programming optimization model using multistage stochastic programming, and finally propose an algorithm, called MOA-FiWi, which has the capacity to handle medium and large instances of the problem and give feasible solutions for the optimization process, where scalability indices between 85%-95% are reached with respect to the projected stochastic values. The proposed model considers the scalability and capital and operating expense, as well as the uncertainty management in the different stages of time-space through multistage stochastic programming. The proposed model presents flexibility in decision making as the time stages progress. This allows for the planning of green fields, as well as the updating of networks that already have communication infrastructure.

关键词： FiWi networks Planning Scalability Smart grid stochastic programming

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A Multiobjective stochastic programming Model for Hydropower Hedging Operations under Inexact Information

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WATER RESOURCES MANAGEMENT 2017年第14期31卷 4649-4667页

作者： Xu, Bin Zhong, Ping-an Wu, Yenan Fu, Fangming Chen, Yuting Zhao, Yunfa Hohai Univ Coll Hydrol & Water Resources 1Xikang Rd Nanjing 210098 Jiangsu Peoples R China Nanjing Hydraul Res Inst 223Guangzhou Rd Nanjing 210029 Jiangsu Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili 1Xikang Rd Nanjing 210098 Jiangsu Peoples R China China Yangtze Power Co Ltd Beijing 100032 Peoples R China

This study develops a multiobjective stochastic programming model for informing hedging decisions for hydropower operations under an electricity market environment considering the benefit from selling energy production and the cost of penalizing energy shortfall. Aiming to determine the optimal strategy that hedges the risk of energy shortfall while keeping a high level of direct revenue from energy production under uncertain streamflows and inexact penalizing price conditions, competing objectives of minimizing energy shortfall percentage and maximizing direct revenue from energy production are analyzed. The conflict is resolved by determining the optimal level of energy shortfall percentage such that the net benefit of the hydropower system is maximized. The first-order optimality condition of maximized system net revenue is derived, which states that the marginal benefit of hedging equals the marginal cost of hedging at optimality. The tradeoff ratio between the competing objectives serves as the marginal cost of hedging and the penalizing price of energy shortfall represents the marginal benefit of hedging. Using the optimality condition, sensitivity tests are conducted for investigating the influence of different ranges of penalizing prices and reservoir initial storages on hedging decisions. The proposed method is evaluated on the operations of the Three Gorges cascade hydropower system during the drawdown season. Results show that: (1) minimizing the energy shortfall percentage adversely affects the maximization in system direct revenue from energy production, and the conflicting results are related to the depletion strategies of reservoir storage;(2) to reduce the energy shortfall percentage to the lowest level could result in significant reduction in total energy production and the direct revenue, especially when reservoir initial storages are low;and (3) the optimal level of energy shortfall percentage would decrease as penalizing price increases, when the

关键词： Hydropower operation Hedging policy stochastic programming Shadow price Inexact information

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Asset liability management for Tanzania : pension funds by stochastic programming

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Afrika Statistika 2018年第3期13卷 1733-1758页

作者： John Andongwisye Larsson Torbjӧrn Martin Singull Allen Mushi

We present a long-term model of asset liability management for Tanzania pension funds. The pension system is pay-as-you-go where contributions are used to pay current benefits. The pension plan is a final salary defined benefit. Two kinds of pension benefits, a commuted (at retirement) and a monthly (old age) pension are considered. A decisive factor for a long-term asset liability management is that, Tanzania pension funds face an increase of their members' life expectancy, which will cause the retirees to contributors dependence ratio to increase. We present a stochastic programming approach which allocates assets with the best return to raise the asset value closer to the level of liabilities. The model is based on work by Kouwenberg in 2001, with features from Tanzania pension system. In contrast to most asset liability management models for pension funds by stochastic programming, liabilities are modeled by using number of years of life expectancy for monthly benefit. Scenario trees are generated by using Monte Carlo simulation. Numerical results suggest that, in order to improve the long-term sustainability of the Tanzania pension fund system, it is necessary to make reforms concerning the contribution rate, investment guidelines and formulate target funding ratios to characterize the pension funds' solvency situation Nous présentons un modèle à long terme de gestion actif-passif pour les fonds de pension tanzaniens. Le système de retraite est un système de répartition qui utilise les cotisations pour payer les prestations courantes. Le régime de retraite est basé sur les points du dernier salaire. Deux types de prestations de retraite, une rente viagère (à la retraite) et une rente mensuelle (vieillesse) sont pris en compte. Un facteur décisif pour une gestion actif-passif à long terme est que les fonds de pension tanzaniens sont confrontés à une augmentation de l'espérance de vie de leurs membres, ce qui entraînera une augmentation du taux de dépendance des

关键词： 62P05 90C15 asset liability management Pay-as-you-go pension fund scenario trees stochastic programming

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Shop floor lot-sizing and scheduling with a two-stage stochastic programming model considering uncertain demand and workforce efficiency

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 111卷 263-271页

作者： Li, Yihua Hu, Guiping Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA 50011 USA

Efficient and flexible production planning is necessary for the manufacturing industry to stay competitive in today's global market. Shop floor lot-sizing and scheduling is one of the most challenging and rewarding subjects for the management. In this study, a two-stage stochastic programming model is proposed to solve a single-machine, multi-product shop floor lot-sizing and scheduling problem. Two sources of uncertainties - product demand from the market, and workforce efficiency - are considered simultaneously, which is the major contribution of this study. The workforce efficiency affects the system productivity, and we propose different distributions to model its uncertainty given insufficient information. The model aims to determine optimal lot sizes and the production sequence that minimizes expected total system costs over the planning horizon, including setup, inventory, and production costs. A case study is performed on a supply chain producing brake equipment in the automotive industry. The numerical results illustrate the usefulness of the stochastic model under volatile environment, and the solution quality is analyzed. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Manufacturing system Production planning Lot-sizing and scheduling Automotive industry stochastic programming

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Risk-averse stochastic programming approach for microgrid planning under uncertainty

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RENEWABLE ENERGY 2017年 101卷 399-408页

作者： Narayan, Apurva Ponnambalam, Kumaraswamy Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

In the planning of isolated microgrids aiming for a small carbon footprint, the penetration of renewable energy resources is expected to be high. Energy supply from renewable sources are highly variable and renewable energy sources have relatively a large capital investment although with a positive impact on the environment. In planning and designing of renewable energy based microgrids, we introduce the approach of two-stage stochastic programming to incorporate the various possible scenarios for renewable energy generation and cost in the planning of microgrids to tackle uncertainty. Most planning problems are similar to portfolio optimization problems. We wish to minimize risk in the investment due to uncertain nature of the resources and also minimize the expected cost of investment. Therefore, we introduced the idea of Markovitz (mean-variance) objective function to minimize the effect of uncertainties in the operation of the microgrid. The model is generic and can be used for any location to suit their geographical topography and demand/supply needs. The result shows the economic advantage of using the risk-averse stochastic programming approach over the deterministic approaches while satisfying environmental objectives. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： stochastic programming Microgrids planning Mean-variance Uncertainty Optimization

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A two-stage stochastic programming model for periodic replenishment control system under demand uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 107卷 313-326页

作者： Cunha, P. S. A. Raupp, F. M. P. Oliveira, F. Pontificia Univ Catolica Rio de Janeiro Dept Engn Ind Rua Marques Sao Vicente 225 BR-22451900 Rio De Janeiro RJ Brazil Lab Nacl Comp Cient Av Getulio Vargas333 Quitandinha BR-25651075 Petropolis RJ Brazil

The inventory control system consists of defining strategies to manage inventory replenishment such that costs involved in ordering, holding, and meeting the demands are optimized. Although several methods for inventory management are proposed in the literature, the assumptions made necessary can hinder their applicability in practice. Motivated by this fact, we propose a methodology for determining the optimal parameters of an inventory control system for single-item one-echelon supply chains using two stage stochastic programming, considering periodic review and uncertain demand. The proposed approach is flexible enough to consider backlogs or lost sales cases without limitations on the number of outstanding orders and allows the consideration of uncertainties in a more comprehensive manner when compared to currently available methods. The optimal review periodicity and the inventory target level are determined using a nonlinear mixed-integer programming model;which takes into account the uncertain nature of the item demand levels through a finite set of scenarios. We present how this model can be reformulated as a deterministic equivalent mixed-integer linear programing model and how the Sample Average Approximation method can be used to incorporate the uncertainty into it. To validate the proposed approach, we perform a simulation-based sensitivity analysis in the neighborhood of the solutions obtained. We also compare them with those obtained applying the Hadley-Whitin method, considering the premises that are necessary for the later. Further, we present results from a case in which the stochastic demand is not stationary. The results obtained provide strong evidence of the potential of the proposed approach. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Inventory policy Replenishment control system Periodic review Uncertain demand stochastic programming

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A dynamic spare parts ordering and pricing policy based on stochastic programming with multi-choice parameters

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MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 2017年第2期23卷 196-221页

作者： Li, Ling Liu, Min Shen, Weiming Cheng, Guoqing Tongji Univ Sch Elect & Informat Engn Lab Intelligent Maintenance Syst & Serv Shanghai Peoples R China Jingdezhen Ceram Inst Sch Informat Engn Jingdezhen Peoples R China

This paper studies the uncertain and random factors in real-life spare parts supply networks which are abstract and complex dynamical systems, then quantifies these factors in a mathematical model. To seek a dynamic spare parts ordering and pricing policy from a distributor's viewpoint, stochastic programming with multi-choice parameters is applied to formulate this objective optimization problem. In our model, the optimal objective is to maximize the total expected profit of the members of the spare parts supply network, and the decision variables are distributor's selling price and ordering quantity in different periods. By using the methods of expectation operator of the fuzzy variable, Lagrange interpolating polynomial and global criteria, the model is solved, and the optimal ordering and pricing policy is obtained. The results of the numerical example and contrast experiments validate the feasibility and efficiency of the proposed model. Some significant conclusions drawn from the results of parameter sensitivity analysis can be referred by management practitioners. This general model can be applied in other fields of supply chain management, where random and uncertain factors need to be considered.

关键词： Dynamic ordering and pricing policy spare parts stochastic programming multi-choice programming

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Improving operating policies of large-scale surface-groundwater systems through stochastic programming

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WATER RESOURCES RESEARCH 2017年第2期53卷 1407-1423页

作者： Macian-Sorribes, H. Tilmant, A. Pulido-Velazquez, M. Univ Politecn Valencia Res Inst Water & Environm Engn IIAMA Valencia Spain Univ Laval Dept Civil & Water Engn Quebec City PQ Canada

The management of large-scale water resource systems with surface and groundwater resources requires considering stream-aquifer interactions. Optimization models applied to large-scale systems have either employed deterministic optimization (with perfect foreknowledge of future inflows, which hinders their applicability to real-life operations) or stochastic programming (in which stream-aquifer interaction is often neglected due to the computational burden associated with these methods). In this paper, stream-aquifer interaction is integrated in a stochastic programming framework by combining the stochastic Dual Dynamic programming (SDDP) optimization algorithm with the Embedded Multireservoir Model (EMM). The resulting extension of the SDDP algorithm, named Combined Surface-Groundwater SDDP (CSG-SDDP), is able to properly represent the stream-aquifer interaction within stochastic optimization models of large-scale surface-groundwater resource systems. The algorithm is applied to build a hydroeconomic model for the Jucar River Basin (Spain), in which stream-aquifer interactions are essential to the characterization of water resources. Besides the uncertainties regarding the economic characterization of the demand functions, the results show that the economic efficiency of the operating policies under the current system can be improved by better management of groundwater and surface resources.

关键词： stochastic programming stream-aquifer interaction SDDP embedded multireservoir model

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A stochastic programming Approach for Risk Management in Mobile Cloud Computing

A Stochastic Programming Approach for Risk Management in Mob...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Dinh Thai Hoang Niyato, Dusit Wang, Ping Wang, Shaun Shuxun Diep Nguyen Dutkiewicz, Eryk Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Nanyang Business Sch Singapore Singapore Univ Technol Sch Comp & Commun Sydney NSW Australia

ISBN: (纸本)9781538617342

The development of mobile cloud computing has brought many benefits to mobile users as well as cloud service providers. However, mobile cloud computing is facing some challenges, especially security-related problems due to the growing number of cyberattacks which can cause serious losses. In this paper, we propose a dynamic framework together with advanced risk management strategies to minimize losses caused by cyberattacks to a cloud service provider. In particular, this framework allows the cloud service provider to select appropriate security solutions, e.g., security software/hardware implementation and insurance policies, to deal with different types of attacks. Furthermore, the stochastic programming approach is adopted to minimize the expected total loss for the cloud service provider under its financial capability and uncertainty of attacks and their potential losses. Through numerical evaluation, we show that our approach is an effective tool in not only dealing with cyberattacks under uncertainty, but also minimizing the total loss for the cloud service provider given its available budget.

关键词： Cyber insurance cybersecurity cloud services mobile cloud stochastic programming

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Robust waste transfer station planning by stochastic programming

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Chemical Engineering Transactions 2018年 70卷 889-894页

作者： Kudela, Jakub Šomplák, Radovan Nevrlý, Vlastimír Lipovský, Tomáš Institute of Mathematics Faculty of Mechanical Engineering Brno University of Technology-VUT Brno Technická 2896/2 Vc Brno616 69 Czech Republic Institute of Automation and Computer Science Faculty of Mechanical Engineering Brno University of Technology-VUT Brno Technická 2896/2 Brno616 69 Czech Republic Institute of Process Engineering Faculty of Mechanical Engineering Brno University of Technology-VUT Brno Technická 2896/2 Brno616 69 Czech Republic

Regarding the infrastructure planning in waste management, the future situation in the Czech Republic or in some other waste developing countries is unknown due to the undecided support from the government or the EU. Furthermore, the production of waste depends on many factors (e.g. separation rate) that are uncertain. The level of recycling will be influenced by technical progress while taking into account the compromise between maximum waste material recovery and the economic impact of the process. Nevertheless, it is important to plan the transportation infrastructure that will be able to support the realization of future projects/development. Such a feature might be the transfer station grid, which can decrease the total transportation costs/impact on the environment. The grid design should be robust with respect to all the possible technological solutions (establishment of waste treatment facilities). In this paper, the mathematical model for grid design of transfer stations is proposed. The model will be presented as a two-stage mixed-integer stochastic programming problem. The model will be tested through a case study on the current situation and possible legislation changes regarding waste management in the Czech Republic. The model scale will be on the micro-regional level proposing a robust transfer station grid design. The realization of these projects takes into consideration possible investments and decides about the capacity of the facility with regards to future government support and donations. The output in the form of recommendation will serve possible investors, municipalities and/or stakeholders from the field of waste management to plan more sustainable projects. Copyright © 2018, AIDIC Servizi S.r.l.

关键词： stochastic programming

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