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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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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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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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Fight Under Uncertainty: Restraining Misinformation and Pushing out the Truth

Fight Under Uncertainty: Restraining Misinformation and Push...

引用

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Huiling Zhang Alan Kuhnle J David Smith My T. Thai Department of Computer and Information Science and Engineering University of Florida Gainesville Florida

ISBN: (纸本)9781538660522

While online social networks (OSNs) have become an important platform for information exchange, the abuse of OSNs to spread misinformation has become a significant threat to our society. To restrain the propagation of misinformation in its early stages, we study the Distance-constrained Misinformation Combat under Uncertainty problem, which aims to both reduce the spread of misinformation and enhance the spread of correct information within a given propagation distance. The problem formulation considers the competitive diffusion of misinformation and correct information. It also accounts for the uncertainty in identifying initial misinformation adopters. For competitive propagation with major-threshold activation, we propose a solution based on stochastic programming and provide an upper-bound in the presence of uncertainty. We propose an efficient Combat Seed Selection algorithm to tackle general-threshold activation, in which we define a measure, “effectiveness”, to evaluate the contribution of nodes to the fight against misinformation. Through extensive experiments, we validate that our algorithm outputs high-quality solution with very fast computation.

关键词： information dissemination social networking (online) stochastic programming misinformation Information Dissemination fight stochastic programming stochastic models

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Rational consumer decisions in a peak time rebate program

arXiv

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arXiv 2018年

作者： Vuelvas, José Ruiz, Fredy Pontificia Universidad Javeriana Bogotá Colombia

A rational behavior of a consumer is analyzed when the user participates in a Peak Time Rebate (PTR) mechanism, which is a demand response (DR) incentive program based on a baseline. A multi-stage stochastic programming is proposed from the demand side in order to understand the rational decisions. The consumer preferences are modeled as a risk-averse function under additive uncertainty. The user chooses the optimal consumption profile to maximize his economic benefits for each period. The stochastic optimization problem is solved backward in time. A particular situation is developed when the System Operator (SO) uses consumption of the previous interval as the household-specific baseline for the DR program. It is found that a rational consumer alters the baseline in order to increase the well-being when there is an economic incentive. As results, whether the incentive is lower than the retail price, the user shifts his load requirement to the baseline setting period. On the other hand, if the incentive is greater than the regular energy price, the optimal decision is that the user spends the maximum possible energy in the baseline setting period and reduces the consumption at the PTR time. This consumer behavior produces more energy consumption in total considering all periods. In addition, the user with high uncertainty level in his energy pattern should spend less energy than a predictable consumer when the incentive is lower than the retail price. Copyright © 2018, The Authors. All rights reserved.

关键词： stochastic programming

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Chance-constrained optimization with tight confidence bounds

arXiv

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arXiv 2017年

作者： Cannon, Mark Engineering Science Dept. University of Oxford OX1 3PJ United Kingdom

Convex sample approximations of chance-constrained optimization problems are considered, in which chance constraints are replaced by sets of sampled constraints. We propose a randomized sample selection strategy that allows tight bounds to be derived on the probability that the solution of the sample approximation is feasible for the original chance constraints. These confidence bounds are shown to be tighter than the bounds that apply if samples are selected according to optimal or greedy discarding strategies. We further show that the same confidence bounds apply to solutions that are obtained from a two stage process in which a sample approximation of a chance-constrained problem is solved, then an empirical measure of the violation probability of the solution is obtained by counting the number of violations of an additional set of sampled constraints. We use this result to design a repetitive scenario approach that meets required tolerances on violation probability given any specified a priori and a posteriori probabilities. These bounds are tighter than confidence bounds available for previously proposed repetitive scenario approaches, and we show that the posterior bounds are exact for a particular problem subclass. The approach is illustrated through numerical examples, and extensions to problems involving multiple chance constraints are discussed.90C15, 90C25, 90C34, 68W20 Copyright © 2017, The Authors. All rights reserved.

关键词： stochastic programming

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Optimal Energy Management for Commercial Buildings Considering Comprehensive Comfort Levels in a Retail Electricity Market

arXiv

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arXiv 2018年

作者： Liang, Zheming Bian, Desong Zhang, Xiaohu Shi, Di Diao, Ruisheng Wang, Zhiwei GEIRI North America San JoseCA United States Department of Electrical and Computer Engineering University of Michigan-Dearborn MI United States

Demand response has been implemented by distribution system operators to reduce peak demand and mitigate contingency issues on distribution lines and substations. Specifically, the campus-based commercial buildings make the major contributions to peak demand in a distribution system. Note that prior works neglect the consumers’ comfort level in performing demand response, which limits their applications as the incentives are not worth as compared to the loss in comfort levels for most time. Thus, a framework to comprehensively consider both operating costs and comfort levels is necessary. Moreover, distributed energy resources are widely deployed in commercial buildings such as roof-top solar panels, plug-in electric vehicles, and energy storage units, which bring various uncertainties to the distribution systems, i.e., (i) output of renewables;(ii) electricity prices;(iii) arrival and departure of plug-in electric vehicles;(iv) business hour demand response signals and (v) flexible energy demand. In this paper, we propose an optimal demand response framework to enable local control of demand-side appliances that are usually too small to participate in a retail electricity market. Several typical small demand side appliances, i.e., heating, ventilation, and air conditioning systems, electric water heaters and plug-in electric vehicles, are considered in our proposed model. Their operations are coordinated by a central controller, whose objective is to minimize the total cost and maximize the customers’ comfort levels for multiple commercial buildings. A scenario-based stochastic programming is leveraged to handle the aforementioned uncertainties. Numerical results based on the practical data demonstrate the effectiveness of the proposed framework. In addition, the trade-off between the operation costs of commercial buildings and customers’ comfort levels is illustrated. Copyright © 2018, The Authors. All rights reserved.

关键词： stochastic programming

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Decision-Based vs. Distribution-Driven Clustering for stochastic Energy System Design Optimization

arXiv

引用

arXiv 2024年

作者： Jürgens, Boyung Seele, Hagen Schricker, Hendrik Reinert, Christiane von der Assen, Niklas Institute of Technical Thermodynamics RWTH Aachen University Schinkelstraße 8 Aachen52062 Germany

stochastic programming is widely used for energy system design optimization under uncertainty but can exponentially increase the computational complexity with the number of scenarios. Common scenario reduction techniques, like moments-matching or distribution-driven clustering, preselect representative scenarios based on input parameters. In contrast, decision-based clustering groups scenarios by similarity in resulting model decisions. Decision-based clustering has shown potential in network design and fleet planning. However, its potential in energy system design remains unexplored. In our work, we examine the effectiveness of decision-based clustering in energy system design using a four-step method: 1) Determine the optimal design for each scenario;2) Aggregate and normalize installed capacities as features reflecting optimal decisions;3) Use these features for k-medoids clustering to identify representative scenarios;4) Utilize these scenarios to optimize cost in stochastic programming. We apply our method to a real-world industrial energy system modeled as a mixed-integer linear program. We incorporate uncertainty by scaling time series with representative factors. We generate 500 single-year scenarios via Monte Carlo sampling, which we reduce using decision-based clustering. For benchmarking, we conduct distribution-driven k-medoids clustering based on the representative factors. In our case studies, both clustering methods yield designs with similar cost efficiency, although decision-based clustering requires substantially more computational resources. To our knowledge, this is the first application of decision-based clustering on energy system design optimization. Future research should investigate the conditions under which decision-based clustering yields more cost-efficient designs compared to distribution-driven clustering. © 2024, CC BY-NC-ND.

关键词： stochastic programming

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Robust Scheduling of Power System with Significant Wind Power Penetration

Robust Scheduling of Power System with Significant Wind Powe...

引用

IEEE Power and Energy Society General Meeting

作者： Haoyong Chen Hao Li Rong Ye Bo Luo the School of Electric Power South China University of Technology Guangzhou 510641 China

ISBN: (纸本)9781467327275

Wind power generation brings new challenges to power system scheduling due to the uncertainty caused by its imprecise prediction. Especially when multiple wind farms are taken into consideration, models and optimization methods which are good at dealing with deterministic scheduling problems do not work. stochastic programming (SP) has been usually applied to solve the problem with wind power uncertainty, which has many advantages and limitations. A new concept of Robust Scheduling (RS) is first proposed in this paper and the mathematical model is established. The Mixed Integer programming (MIP) is adopted for solving the model. The paper also defines the extreme scenarios set for description of wind power uncertainty. By using it we can achieve robust schedule by avoiding the use of the probabilistic distribution of wind power, which is often hard to acquire. Case study based on the modified IEEE118 bus system proves that the RS model and optimization method are effective and can acquire a robust schedule over wind power uncertainty.

关键词： Index Terms-multiple wind farms security constrained unit commitment robust optimization stochastic programming extreme scenario Wind energy power systems Wind farms Optimization methods optimization method Uncertainty

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A Reliability Theory of Compromise Decisions for Large-Scale stochastic Programs

arXiv

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arXiv 2024年

作者： Diao, Shuotao Sen, Suvrajeet Department of Industrial Engineering and Management Sciences Northwestern University EvanstonIL60208 United States Epstein Department of Industrial and Systems Engineering University of Southern California Los AngelesCA90089 United States

stochastic programming models can lead to very large-scale optimization problems for which it may be impossible to enumerate all possible scenarios. In such cases, one adopts a sampling-based solution methodology in which case the reliability of the resulting decisions may be suspect. For such instances, it is advisable to adopt methodologies that promote variance reduction. One such approach goes under a framework known as "compromise decision", which requires multiple replications of the solution procedure. This paper studies the reliability of stochastic programming solutions resulting from the "compromise decision" process. This process is characterized by minimizing an aggregation of objective function approximations across replications, presumably conducted in parallel. We refer to the post-parallel-processing problem as the problem of "compromise decision". We quantify the reliability of compromise decisions by estimating the expectation and variance of the "pessimistic distance" of sampled instances from the set of true optimal decisions. Such pessimistic distance is defined as an estimate of the largest possible distance of the solution of the sampled instance from the "true" optimal solution set. The Rademacher average of instances is used to bound the sample complexity of the compromise decision. © 2024, CC BY.

关键词： stochastic programming

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