123 research outputs found

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Operational Research: methods and applications

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    This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Operational research:methods and applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    The Power of Two-sided Recruitment in Two-sided Markets

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    We consider the problem of maximizing the gains from trade (GFT) in two-sided markets. The seminal impossibility result by Myerson shows that even for bilateral trade, there is no individually rational (IR), Bayesian incentive compatible (BIC) and budget balanced (BB) mechanism that can achieve the full GFT. Moreover, the optimal BIC, IR and BB mechanism that maximizes the GFT is known to be complex and heavily depends on the prior. In this paper, we pursue a Bulow-Klemperer-style question, i.e. does augmentation allow for prior-independent mechanisms to beat the optimal mechanism? Our main result shows that in the double auction setting with mm i.i.d. buyers and nn i.i.d. sellers, by augmenting O(1)O(1) buyers and sellers to the market, the GFT of a simple, dominant strategy incentive compatible (DSIC), and prior-independent mechanism in the augmented market is least the optimal in the original market, when the buyers' distribution first-order stochastically dominates the sellers' distribution. Furthermore, we consider general distributions without the stochastic dominance assumption. Existing hardness result by Babaioff et al. shows that no fixed finite number of agents is sufficient for all distributions. In the paper we provide a parameterized result, showing that O(log(m/rn)/r)O(log(m/rn)/r) agents suffice, where rr is the probability that the buyer's value for the item exceeds the seller's value

    Operational Research: Methods and Applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Operational Research: Methods and Applications

    Get PDF
    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    Artificial intelligence for decision making in energy demand-side response

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    This thesis examines the role and application of data-driven Artificial Intelligence (AI) approaches for the energy demand-side response (DR). It follows the point of view of a service provider company/aggregator looking to support its decision-making and operation. Overall, the study identifies data-driven AI methods as an essential tool and a key enabler for DR. The thesis is organised into two parts. It first provides an overview of AI methods utilised for DR applications based on a systematic review of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR application area of the AI methods. The end of the first part of the thesis discusses the advantages and potential limitations of the reviewed AI techniques for different DR tasks and how they compare to traditional approaches. The second part of the thesis centres around designing machine learning algorithms for DR. The undertaken empirical work highlights the importance of data quality for providing fair, robust, and safe AI systems in DR — a high-stakes domain. It furthers the state of the art by providing a structured approach for data preparation and data augmentation in DR to minimise propagating effects in the modelling process. The empirical findings on residential response behaviour show better response behaviour in households with internet access, air-conditioning systems, power-intensive appliances, and lower gas usage. However, some insights raise questions about whether the reported levels of consumers’ engagement in DR schemes translate to actual curtailment behaviour and the individual rationale of customer response to DR signals. The presented approach also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an aggregator in leveraging small-scale flexibility resources by providing an automated end-to-end framework to select the set of consumers for demand curtailment during Demand-Side Response (DR) signals in a dynamic environment while considering a long-term view of their selection process
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