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SSRN 2022年

作者： Lessan, Javad Huang, Kai Rafiei, Rezvan DeGroote School of Business McMaster University HamiltonONL8S4M4 Canada TD West Tower TD Bank TorontoONM5K1B1 Canada Department of Civil Engineering University of British Columbia Vancouver Canada

We present a novel model that simultaneously addresses the problem of pre-disaster packaging and post-disaster distribution of relief items under demand uncertainty. Specifically, the box design and relief items delivery (BDRID) problem concerns the extent to which the boxing of relief items is a better logistical strategy relative to transporting them individually to disaster areas. We model the problem as a two-stage stochastic nonlinear program and then present the linear transformation of the problem. As part of the proposed solution approach, the sample average approximation (SAA) method is used to determine pre-disaster packaging decisions and the post-disaster distribution of relief items. We also propose polynomial-time algorithms that consistently solve the optimization model for relief items delivery an average of five times faster than the commercial optimization software CPLEX. Importantly, our comprehensive numerical experiments indicate cost savings between 6% and 30%, on average 20%, in favor of the packaging strategy. These findings offer practical managerial insights concerning the BDRID problem, with the potential to improve the efficiency of disaster response efforts. © 2022, The Authors. All rights reserved.

关键词： stochastic programming

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Two-Stage stochastic Optimization for Reactive Power Dispatch with Wind Power Uncertainties

Two-Stage Stochastic Optimization for Reactive Power Dispatc...

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International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Amit Saraswat Ravi Ucheniya Yogesh Gupta Manipal University Jaipur Rajasthan India

ISBN: (数字)9781728106663

ISBN: (纸本)9781728106670

The wind farms technology is an emerging technology for electrical power generation. However, the unpredictable nature of the wind speed affects the output performance of the wind farm. As a result, the power generation output of a wind farm is uncertain. In this paper, a scenario-based two-stage stochastic optimization framework is developed to minimize the total real power losses in the transmission network. The deterministic optimal reactive power dispatch problem is modified using scenario based stochastic process. In addition, a real-coded genetic algorithm based on principle component analysis is used to solve the two-stage stochastic optimal problem. It is tested on modified IEEE 30-bus power system. The presented case study and obtained test results proves the effectiveness of proposed method for getting optimal solutions in all considered scenarios.

关键词： genetic algorithms power generation dispatch principal component analysis reactive power stochastic processes stochastic programming transmission networks wind power wind power plants transmission networks Power generation dispatch Wind farms Reactive power Markov chain Wind power plants Wind energy Genetic Algorithm (GA) principal components analysis Power production stochastic Processes Power dispatch stochastic programming stochastic models Dispatching

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Online Learning in Weakly Coupled Markov Decision Processes: A Convergence Time Study 18

Online Learning in Weakly Coupled Markov Decision Processes:...

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Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems

作者： Xiaohan Wei Hao Yu Michael J. Neely University of Southern California Los Angeles CA USA

ISBN: (纸本)9781450358460

We consider multiple parallel Markov decision processes (MDPs) coupled by global constraints, where the time varying objective and constraint functions can only be observed after the decision is made. Special attention is given to how well the decision maker can perform in T slots, starting from any state, compared to the best feasible randomized stationary policy in hindsight. We develop a new distributed online algorithm where each MDP makes its own decision each slot after observing a multiplier computed from past information. While the scenario is significantly more challenging than the classical online learning context, the algorithm is shown to have a tight O(√T) regret and constraint violations simultaneously. To obtain such a bound, we combine several new ingredients including ergodicity and mixing time bound in weakly coupled MDPs, a new regret analysis for online constrained optimization, a drift analysis for queue processes, and a perturbation analysis based on Farkas' Lemma.

关键词： stochastic programming online learning Markov decision processes

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Optimal Sampling of Random Processes under stochastic Energy Constraints

Optimal Sampling of Random Processes under Stochastic Energy...

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IEEE Global Communications Conference

作者： Jing Yang Jingxian Wu Dept. of Electr. Eng. Univ. of Arkansas Fayetteville AR USA

ISBN: (纸本)9781479935130

In this paper, we study the optimal sampling policy for an energy harvesting sensing system, which is designed to estimate a wide-sense stationary random process by using discrete-time samples collected by a sensor. The energy in the sensor is consumed by taking observations and is replenished randomly with energy harvested from the ambient environment. Our goal is to identify the optimal sampling policy that minimizes the estimation mean squared error (MSE) under stochastic energy constraints. The problem can be formulated as a stochastic programming problem, which is generally difficult to solve. We identify an asymptotically optimal solution to the problem by exploiting the properties of random processes with power-law decaying covariance. Specifically, with the help of a newly derived inverse covariance matrix of the random process, it is discovered that the linear minimum MSE (MMSE) estimation of the random process demonstrates a Markovian property. That is, the optimal estimation of any point in a time segment bounded by two consecutive samples can be achieved by using the knowledge of only the two bounding samples while ignoring all other samples. Such a Markovian property enables us to identify a lower bound of the long term average MSE. Motivated by the structure of the MSE lower bound, we then propose a simple best-effort sampling scheme by considering the stochastic energy constraints. It is shown that the best-effort sampling scheme is asymptotically optimal in the sense that, for almost every energy harvesting sample path, it achieves the MSE lower bound as time becomes large.

关键词： covariance matrices discrete time systems energy harvesting mean square error methods random processes stochastic processes stochastic programming telecommunication power management MSE lower bound Markovian property asymptotically optimal solution discrete-time samples energy harvesting sensing system estimation mean squared error inverse covariance matrix optimal sampling power-law decaying covariance stochastic energy constraints stochastic programming problem time segment wide-sense stationary random process Energy harvesting Estimation Linear programming Optimization Random processes Sensors stochastic processes

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Sensitivity analysis of stochastic constraint and variational systems via generalized differentiation

arXiv

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arXiv 2021年

作者： Mordukhovich, Boris S. Pérez-Aros, Pedro Department of Mathematics Wayne State University DetroitMI48202 United States Instituto de Ciencias de la Ingeniería Universidad de o'Higgins Rancagua Chile

This paper conducts sensitivity analysis of random constraint and variational systems related to stochastic optimization and variational inequalities. We establish efficient conditions for well-posedness, in the sense of robust Lipschitzian stability and/or metric regularity, of such systems by employing and developing coderivative characterizations of well-posedness properties for random multifunctions and efficiently evaluating coderivatives of special classes of random integral set-valued mappings that naturally emerge in stochastic programming and stochastic variational *** Codes 49J53, 90C15, 90C34, 49J52 © 2021, CC BY.

关键词： stochastic programming

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stochastic Short-term Incentive-based Demand Response Scheduling of Load-serving Entities

Stochastic Short-term Incentive-based Demand Response Schedu...

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IEEE Power and Energy Society General Meeting

作者： Mohammad Ali Fotouhi Ghazvini Pedro Faria Hugo Morais Zita Vale GECAD IPP – Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781479913022

In competitive electricity markets it is necessary for a profit-seeking load-serving entity (LSE) to optimally adjust the financial incentives offering the end users that buy electricity at regulated rates to reduce the consumption during high market prices. The LSE in this model manages the demand response (DR) by offering financial incentives to retail customers, in order to maximize its expected profit and reduce the risk of market power experience. The stochastic formulation is implemented into a test system where a number of loads are supplied through LSEs.

关键词： Day-ahead market demand response demandside bidding load-serving entities stochastic programming. Demand-Responsive power markets End users stochastic programming stochastic models Test systems SIAE gene stochastic

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Combining penalty-based and gauss-seidel methods for solving stochastic mixed-integer problems

arXiv

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arXiv 2017年

作者： Oliveira, F. Christiansen, J. Dandurand, B. Eberhard, A. Mathematical Sciences School of Science RMIT University

In this paper, we propose a novel decomposition approach for mixed-integer stochastic programming (SMIP) problems that is inspired by the combination of penalty-based Lagrangian and block Gauss-Seidel methods (PBGS). In this sense, PBGS is developed such that the inherent decomposable structure that SMIPs present can be exploited in a computationally efficient manner. The performance of the proposed method is compared with the Progressive Hedging method (PH), which also can be viewed as a Lagrangian-based method for obtaining solutions for SMIP. Numerical experiments performed using instances from the literature illustrate the efficiency of the proposed method in terms of computational performance and solution quality. Copyright © 2017, The Authors. All rights reserved.

关键词： stochastic programming

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Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

arXiv

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arXiv 2022年

作者： Li, Huiran Yang, Yanwu School of Business Administration and Customs Affair Shanghai Customs College Shanghai201204 China School of Management Huazhong University of Science and Technology Wuhan430074 China

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and clickthrough rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit;(b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations;(c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA *** Codes 68Txx © 2022, CC BY-NC-SA.

关键词： stochastic programming

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Product Recovery Network Model under stochastic Environment

Product Recovery Network Model under Stochastic Environment

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The Seventh International Conference on Information and Management Sciences

作者： Xiaohu Yang Department of Statistics Xi’an University of Finance and Economics Xi’an 710061 China

＜正＞Product recovery has received increasing atten- tions in the past *** of the existing models on the subject are ***,randomness is one of the characteristics of product recovery ***- ing this situation,this paper proposes a generalized product recovery network with stochastic quantities of returned prod- ucts and stochastic transportation *** on different decision-making criteria,three stochastic programming mod- els are formulated to characterize this problem.

关键词： stochastic programming Product recovery Reverse logistics Genetic algorithm

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Representing Weather-Year Variation in Whole-Farm Optimisation Models: Options and Importance

SSRN

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SSRN 2023年

作者： Young, Michael Young, John Kingwell, Ross S. Vercoe, Philip E. School of Agriculture and Environment The University of Western Australia 35 Stirling Highway Perth6009 Australia Institute of Agriculture The University of Western Australia 35 Stirling Highway Perth6009 Australia Australian Export Grains Innovation Centre Australia Department of Primary Industries and Regional Development WA Australia Farming Systems Analysis Service Kentdale WA Australia

CONTEXTFarm management occurs against a backdrop of weather-year variation. How important is it for farming system models to capture this variation and the management tactics matched to that variation?OBJECTIVEThis study seeks to compare farm optimisation frameworks that differently represent weather-year variation and quantify the value of short-term tactical management in response to unfolding weather *** different farm optimisation frameworks that describe a farming system in Western Australia are constructed and compared. The models range from steady-state deterministic programming to complex multi-year stochastic programming with detailed representation of weather-year variation and associated farm management tactics. The models are subject to various applications to test their differences and *** AND CONCLUSIONSModelling results reveal significant differences in farm profit and optimal farm management when weather-year variation and the relevant management tactics are considered. Including tactical decision-making increases expected farm profit by at least 14%.SIGNIFICANCEThis study demonstrates the usefulness of multi-year discrete stochastic programming when applied to mixed enterprise farm businesses in the Great Southern region of Western Australia. The study quantifies the value of applying farm management tactics in response to weather-year variation and demonstrates that steady-state deterministic, single year stochastic and multi-year stochastic models generate different estimates of profitability and different strategic and tactical management decisions. © 2023, The Authors. All rights reserved.

关键词： stochastic programming

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