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24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Affenzeller, Michael Boegl, Michael Fischer, Lukas Sobieczky, Florian Yang, Kaifeng Zenisek, Jan Univ Appl Sci Upper Austria Sch Informat Commun & Media Softwarepk 11 A-4232 Hagenberg Austria RISC Software GmbH Softwarepk 32A A-4232 Hagenberg Austria Software Competence Ctr Hagenberg GmbH Softwarepk 32A A-4232 Hagenberg Austria

ISBN: (纸本)9781665465458

Business analytics is an extensive use of data acquired from diverse sources, statistical and quantitative analysis, explainable and predictive models, and fact-based management to make better strategic decisions for different stakeholders. To be able to model complex systems holistically in such a way that they can be fed into an efficient simulation-based optimization in the sense of prescriptive analytics, approaches and solutions that go beyond state-of-the-art are required. This paper introduces the basic technologies used in prescriptive analytics and proposes secure prescriptive analytics (SPA) that is based on component-based hierarchical modeling and dynamic optimization. Each element under the SPA framework is defined and illustrated by an example of production plan optimization.

关键词： Business analytics prescriptive analytics Secure prescriptive analytics Information Security Surrogate Models Simulation Optimization

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prescriptive analytics for optimal multi-use battery energy storage systems operation: State-of-the-art and research directions 27

Prescriptive analytics for optimal multi-use battery energy ...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Haug, Martin Bordin, Chiara Mishra, Sambeet Moisan, Julien UiT Arctic University of Norway Tromsø Norway University of South-Eastern Norway Porsgrunn Norway Arva AS Tromso Norway

This paper presents the state-of-the-art and latest advances in implementing multi-use practices on BESS applications to the power system grid. Representative papers on modeling and optimization methods were selected, most of them working with realistic use cases, but none reporting on real-world implementations. Some major findings from reviewing key representative papers are that current optimization methods were able to handle uncertainty related to prices and other parameters either in the look-ahead planning stage or real-time control for obtaining an optimal dispatch schedule, thus addressing previously pointed-out gaps. Further, a recommendation for future work is to implement current multi-use methods, found to be mature enough, to real-world BESS use cases for validation and gaining experimental experience. Furthermore, the multi-use performance can further be improved by joining the strong contributions from each paper's method. Current methods have shown great potential for bringing simultaneous benefits to the grid, as well as being economical for the owner. A continued exploration and inclusion of new future BESS applications in prescriptive analytics for BESS multi-use suggest robustness for future market changes. However, further assessments are needed to evaluate whether the technology is economical and green for each particular use case and also for a potential systematic upscaling of the technology as a means to facilitate the green shift. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： battery energy storage system BESS multi-use power grid applications prescriptive analytics review value stacking

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prescriptive analytics in airline operations: Arrival time prediction and cost index optimization for short-haul flights

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OPERATIONS RESEARCH PERSPECTIVES 2018年 5卷 265-279页

作者： Achenbach, Anna Spinler, Stefan WHU Otto Beisheim Sch Management Kuehne Fdn Endowed Chair Logist Management Burgpl 2 D-56179 Vallendar Germany

In this paper, we provide arrival time prediction combined with a cost index optimization model for short haul flights. Our work is based on flight data of a European network carrier. We focus on predicting the arrival time for incoming flights at two hub locations. Airlines focus on two aspects in their operations: Minimizing cost while ensuring on-time arrivals. Especially network carriers with hub connections need to ensure that incoming flights are on time for passenger, crew and aircraft transfer. The cost index is a tool for optimizing the aircraft's speed. A high cost index implies a faster flight. The cost of time is set in relation to the cost of fuel. Today there is no model for arrival time prediction and integrated cost index optimization. We consider three different flight distances to model the impact of cost index changes on gate arrival time. With our model airlines are able to reduce the cost index without any tangible impact on their overall schedule. We conclude that the optimal cost index level heavily depends on a flight's distance, fuel costs and delay costs. Especially for short haul flights we recommend lowering the cost index as a high cost index has limited impact on gate arrival time.

关键词： OR in airlines analytics Aircraft arrival time prediction prescriptive analytics Cost index optimization

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prescriptive analytics System for Scholar Research Performance Enhancement

Prescriptive Analytics System for Scholar Research Performan...

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作者： Lee, Mikyoung Cho, Minhee Gim, Jangwon Jeong, Do-Heon Jung, Hanmin 245 Daehak-ro Yuseong-gu Daejeon Korea Republic of

ISBN: (纸本)9783319078564

We introduce a prescriptive analytics system, InSciTe Advisory, to provide researchers with advice for their future research direction and strategy. It consists of two main parts: descriptive analytics and prescriptive analytics. Descriptive analytics provides results from research activity history as well as the research power index for the designated researcher. prescriptive analytics suggests a group of role model researchers to the researcher, as well as methods to adopt their best practices. The prescription for the researcher is provided according to 5W1H questions and their corresponding answers. All of the analytical results and their explanations related to the given researcher are automatically generated and saved to a report. This researcher-centric prescriptive analytics framework is expected to be a useful tool to understand the designated researcher from the perspective of prescriptive and descriptive analytics. We evaluated user satisfaction results for InSciTe Advisory and Elsvier Scival by five test users. The result of the evaluation demonstrated that user satisfaction of InSciTe Advisory is 126.5% higher than Scival. © Springer International Publishing Switzerland 2014.

关键词： Descriptive analytics InSciTe Advisory prescriptive analytics Research Performance Enhancement User Satisfaction Evaluation

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HadoopSec 2.0: prescriptive analytics-based multi-model sensitivity-aware constraints centric block placement strategy for Hadoop

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期39卷 8477-8486页

作者： Revathy, P. Mukesh, Rajeswari Hindustan Inst Technol & Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

Like many open-source technologies such as UNIX or TCP/IP, Hadoop was not created with Security in mind. Hadoop however evolved from the other tools over time and got widely adopted across large enterprises. Some of Hadoop's architectural features present Hadoop its unique security issues. Given this security vulnerability and potential invasion of confidentiality due to malicious attackers or internal customers, organizations face challenges in implementing a strong security framework for Hadoop. Furthermore, given the method in which data is placed in Hadoop Cluster adds to the only growing list of these potential security vulnerabilities. Data privacy is compromised when these critical and data-sensitive blocks are accessed either by unauthorized users or for that matter even misuse by authorized users. In this paper, we intend to address the strategy of data block placement across the allotted DataNodes. prescriptive analytics algorithms are used to determine the Sensitivity Index of the Data and thereby decide on data placement allocation to provide impenetrable access to an unauthorized user. This data block placement strategy aims to adaptively distribute the data across the cluster using innovative ML techniques to make the data infrastructure extra secured.

关键词： Big data Hadoop security data placement strategy for sensitive data sensitivity in Hadoop prescriptive analytics

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Explainable subgradient tree boosting for prescriptive analytics in operations management

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期312卷 1119-1133页

作者： Notz, Pascal M. Pibernik, Richard Julius Maximilians Univ Wurzburg Chair Logist & Quantitat Methods Sanderring 2 D-97070 Wurzburg Germany Zaragoza Logist Ctr Ave Ranillas 5Edificio 5A Zaragoza 50018 Spain

Motivated by the success of gradient boosting approaches in machine learning and driven by the need for explainable prescriptive analytics approaches in operations management (OM), we propose subgradient tree boosting (STB) as an explainable prescriptive analytics approach to solving convex stochastic optimization problems that frequently arise in OM. The STB approach combines the well-known method of subgradient descent in function space with sample average approximation, and prescribes decisions from a problem-specific loss function, historical demand observations, and prescriptive features. The approach provides a decision-maker with detailed explanations for the prescribed decisions, such as a breakdown of individual features' impact. These explanations are particularly valuable in practice when the decisionmaker has the discretion to adjust the recommendations made by a decision support system. We show how subgradients can be derived for common single-stage and two-stage stochastic optimization problems;demonstrate the STB approach's applicability to two real-world, complex capacity-planning problems in the service industry;benchmark the STB approach's performance against those of two prescriptive approaches-weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM);and show how the STB approach's prescriptions can be explained by estimating the impact of individual features. The results suggest that the quality of the STB approach's prescriptions is comparable to that of wSAA's and kERM's prescriptions while also providing explanations. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Decision support systems prescriptive analytics Machine learning Gradient boosting Explainability

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Design and Development of We-CDSS Using Django Framework: Conducing Predictive and prescriptive analytics for Coronary Artery Disease

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IEEE ACCESS 2022年 10卷 119575-119592页

作者： Divyashree, N. Prasad, Nandini K. S. Dr Ambedkar Inst Technol Bengaluru Karnataka India

Clinical Decision Support System (CDSS) has become the ultimate point of care for healthcare sectors to assist the public in terms of Medical care. Through this research work, we propose the design and development of a Web-based Clinical Decision Support System (We-CDSS) using the Django framework with an aim to make CDSS accessible for both common people and clinicians on computers and mobile devices that integrates Predictive analytics involving the LWGMK-NN algorithm for predicting Coronary Artery Disease, and prescriptive analytics involving prescription rules. The proposed research work consists of three phases. The first phase presents the design and development of a web-based CDSS outlook using the Django framework. The second and final phase embeds Predictive analytics and prescriptive analytics into the We-CDSS to manage the functionalities of predictive decisions, and the functionalities of prescriptive processes that evaluate the predictive results and recommends personalized lifestyle suggestions to reduce the risk of coronary artery disease.

关键词： Clinical decision support systems We-CDSS predictive analytics prescriptive analytics coronary artery disease Django LWGMK-NN

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Reinforcement-Learning-Informed prescriptive analytics for Air Traffic Flow Management

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第3期21卷 4188-4202页

作者： Wang, Yuan Cai, Weilin Tu, Yilei Mao, Jianfeng Chinese Univ Hong Kong Shenzhen CUHK Shenzhen Sch Data Sci Shenzhen 518172 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China Shenzhen Res Inst Big Data Shenzhen 110004 Peoples R China Swiss Fed Inst Technol Dept Comp Sci CH-8092 Zurich Switzerland

Air Traffic Flow Management (ATFM) is a complex sequential decision-making problem that involves dynamically matching flights with sectors under changing environmental conditions. Finding an optimal solution for ATFM is challenging due to its dynamic nature and operational constraints. Reinforcement learning is a well-suited approach for sequential decision-making problems. However, ATFM poses three potential challenges: 1) large state space, 2) combinatorial action space, and 3) variational feasible action set, resulting from numerous agents with tightly-coupled constraints. These challenges can hinder the effectiveness of direct application of reinforcement learning methods. While prescriptive analytics can readily handle hard constraints via a mathematical optimization model, but it is computationally intractable for online sequential decision-making problems under changing environments. To address these challenges, we propose a novel framework, Reinforcement-Learning-Informed prescriptive analytics (RLIPA), in which an "informing" scheme is devised to integrate reinforcement learning and prescriptive analytics and leverage their strengths in predicting future reward and coping with hard constraints respectively. RLIPA is a general framework that can be adapted to other problems beyond ATFM, which typically involves many agents with tightly-coupled hard constraints. We demonstrate the usage and performance of RLIPA using numerical results and a real case study in comparison to two baseline *** to Practitioners-To improve Air Traffic Flow Management (ATFM) and reduce flight congestion, we propose a new method called reinforcement-learning-informed prescriptive analytics (RLIPA). RLIPA is a general framework that facilitates online sequential decision-making problems with multiple agents coupled with hard constraints. The approach consists of two stages: first, estimating future potential rewards for each agent via reinforcement learning, and second, in

关键词： Air traffic flow management reinforcement learning prescriptive analytics sequential decision-making problem

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Designing employee benefits to optimize turnover: A prescriptive analytics approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 197卷

作者： Latorre, Paolo Lopez-Ospina, Hector Maldonado, Sebastian Guevara, C. Angelo Perez, Juan Univ Andes Santiago Chile Univ Chile Sch Econ & Business Dept Management Control & Informat Syst Santiago Chile Inst Sistemas Complejos Ingn Santiago Chile Univ Chile Dept Ingn Civil Santiago Chile

Employee turnover significantly impacts organizations, particularly those with substantial investments in training their workforce. To mitigate these effects, we propose a prescriptive Human Resources analytics approach that optimizes employee benefits to minimize total costs, focusing on turnover management The methodology models employee decision-making using a discrete choice model, with parameters estimated through maximum likelihood. We solve the resulting nonlinear optimization problem with a heuristic tailored to the problem's complexity. We applied this methodology to a hospital case study, which was used to enhance the transportation system as an employee benefit, considering the associated turnover costs. The results demonstrate that our approach can reduce total costs, optimize the usage level of the designed benefits, and increase employee satisfaction. This research provides a robust framework for data-driven decision-making in HR, offering practical tools for improving employee retention strategies.

关键词： HR analytics prescriptive analytics Turnover minimization Facility location Nested logit

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Human-Augmented prescriptive analytics With Interactive Multi-Objective Reinforcement Learning

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IEEE ACCESS 2021年 9卷 100677-100693页

作者： Lepenioti, Katerina Bousdekis, Alexandros Apostolou, Dimitris Mentzas, Gregoris Natl Tech Univ Athens NTUA Inst Commun & Comp Syst ICCS Informat Management Unit IMU Zografos 15780 Greece Univ Piraeus Dept Informat Piraeus 18534 Greece

The rise of Artificial Intelligence (AI) enables enterprises to manage large amounts of data in order to derive predictions about future performance and to gain meaningful insights. In this context, descriptive and predictive analytics has gained a significant research attention;however, prescriptive analytics has just started to emerge as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time. Although machine learning for decision making has been identified as one of the most important applications of AI, up to now, prescriptive analytics is mainly addressed with domain-specific optimization models. On the other hand, existing literature lacks generalized prescriptive analytics models capable of being dynamically adapted according to the human preferences. Reinforcement Learning, as the third machine learning paradigm alongside supervised learning and unsupervised learning, has the potential to deal with the dynamic, uncertain and time-variant environments, the huge states space of sequential decision making processes, as well as the incomplete knowledge. In this paper, we propose a human-augmented prescriptive analytics approach using Interactive Multi-Objective Reinforcement Learning (IMORL) in order to cope with the complexity of real-life environments and the need for optimized human-machine collaboration. The decision making process is modelled in a generalized way in order to assure scalability and applicability in a wide range of problems and applications. We deployed the proposed approach in a stock market case study in order to evaluate the proactive trading decisions that will lead to the maximum return and the minimum risk that the user's experience and the available data can yield in combination.

关键词： Decision making Reinforcement learning Artificial intelligence Business Task analysis Data analysis Complexity theory Big data data analytics human-augmented analytics machine learning prescriptive analytics reinforcement learning

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