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Monitoring epidemic processes under political measures

STATISTICS IN MEDICINE 2024年第11期43卷 2122-2160页

作者： Chukhrova, Nataliya Plate, Oskar Johannssen, Arne Univ Southern Denmark Maersk Mc Kinney Moller Inst Fac Engn Odense Denmark Univ Hamburg Fac Business Adm Hamburg Germany Univ Southern Denmark Maersk Mc Kinney Moller Inst Fac Engn DK-5230 Odense Denmark

Statistical modeling of epidemiological curves to capture the course of epidemic processes and to implement a signaling system for detecting significant changes in the process is a challenging task, especially when the process is affected by political measures. As previous monitoring approaches are subject to various problems, we develop a practical and flexible tool that is well suited for monitoring epidemic processes under political measures. This tool enables monitoring across different epochs using a single statistical model that constantly adapts to the underlying process, and therefore allows both retrospective and on-line monitoring of epidemic processes. It is able to detect essential shifts and to identify anomaly conditions in the epidemic process, and it provides decision-makers a reliable method for rapidly learning from trends in the epidemiological curves. Moreover, it is a tool to evaluate the effectivity of political measures and to detect the transition from pandemic to endemic. This research is based on a comprehensive COVID-19 study on infection rates under political measures in line with the reporting of the Robert Koch Institute covering the entire period of the pandemic in Germany.

关键词： control charts coronavirus pandemic COVID-19 epidemiological curve infection rate prescriptive analytics

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Robust portfolio optimization model for electronic coupon allocation

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INFOR 2024年第4期62卷 646-660页

作者： Uehara, Yuki Nishimura, Naoki Li, Yilin Yang, Jie Jobson, Deddy Ohashi, Koya Matsumoto, Takeshi Sukegawa, Noriyoshi Takano, Yuichi Univ Tsukuba Masters Program Policy & Planning Sci Tsukuba Japan Univ Tsukuba Inst Syst & Informat Engn Tsukuba Japan Recruit Co Ltd Prod Dev Management Off Data Management & Planning Off Chiyoda City Tokyo Japan Mercari Inc Data Sci Minato City Tokyo Japan Hosei Univ Dept Adv Sci Chiyoda Tokyo Japan

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six type of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean-variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.

关键词： Coupon allocation portfolio optimization robust optimization treatment effect prescriptive analytics E-commerce

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System thinking: Crafting scenarios for prescriptive analytics 1

System thinking: Crafting scenarios for prescriptive analyti...

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1st International Workshop on Patent Mining and Its Applications, IPaMin 2014, Co-located with Konvens 2014

作者： Weber, Jens Cho, Minhee Lee, Mikyoung Song, Sa-Kwang Geierhos, Michaela Jung, Hanmin University of Paderborn Fuerstenallee 11 PaderbornD-33102 Germany 245 Daehak-ro Yuseong-gu Daejeon305-806 Korea Republic of

This paper focuses on the first step in combining prescriptive analytics with scenario techniques in order to provide strategic development after the use of InSciTe, a data prescriptive analytics application. InSciTe supports the improvement of researchers' individual performance by recommending new research directions. Standardized influential factors are presented as a foundation for automated scenario modelling such as the prototypical report generation function of InSciTe. Additionally, a use-case is shown which validates the potential of the standardized influential factors for raw scenario development. Copyright © 2014 for the individual papers by the papers' authors.

关键词： prescriptive analytics Role model group Scenario technique Standardized influential factors

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From process mining to augmented process execution

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SOFTWARE AND SYSTEMS MODELING 2023年第6期22卷 1977-1986页

作者： Chapela-Campa, David Dumas, Marlon Univ Tartu Tartu Estonia

Business process management (BPM) is a well-established discipline comprising a set of principles, methods, techniques, and tools to continuously improve the performance of business processes. Traditionally, most BPM decisions and activities are undertaken by business stakeholders based on manual data collection and analysis techniques. This is time-consuming and potentially leads to suboptimal decisions, as only a restricted subset of data and options are considered. Over the past decades, a rich set of data-driven techniques has emerged to support and automate various activities and decisions across the BPM lifecycle, particularly within the process mining field. More recently, the uptake of artificial intelligence (AI) methods for BPM has led to a range of approaches for proactive business process monitoring. Given their common data requirements and overlapping goals, process mining and AI-driven approaches to business process optimization are converging. This convergence is leading to a promising emerging concept, which we call (AI-)augmented process execution: a collection of data analytics and artificial intelligence methods for continuous and automated improvement and adaptation of business processes. This article gives an outline of research at the intersection between process mining and AI-driven process optimization, classifies the researched techniques based on their scope and objectives, and positions augmented process execution as an additional layer on top of this stack.

关键词： Business process management Predictive analytics prescriptive analytics Autonomous systems

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Data-Driven Optimization for Commodity Procurement Under Price Uncertainty

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2023年第2期25卷 371-390页

作者： Mandl, Christian Minner, Stefan Tech Univ Munich TUM Sch Management Logist & Supply Chain Management D-80333 Munich Germany

Problem definition: We study a practice-motivated multiperiod stochastic commodity procurement problem under price uncertainty with forward and spot purchase options. Existing approaches are based on parametric pricemodels, which inevitably involve price model misspecification and generalization error. Academic/practical relevance: We propose a nonparametric, data-driven approach (DDA) that is consistent with the optimal procurement policy structure but without requiring the a priori specification and estimation of stochastic price processes. In addition to historical prices, DDAis able to leverage real-time feature data, such as economic indicators, in solving the problem. Methodology: This paper provides a framework for prescriptive analytics in dynamic commodity procurement, with optimal purchase policies learned directly from data as functions of features, via mixed integer linear programming (MILP) under costminimization objectives. Hence, DDAfocuses on optimal decisions rather than optimal predictions. Furthermore, we combine optimization with regularization from machine learning (ML) to extract decision-relevant data fromnoise. Results: Based on numerical experiments and empirical data, we show that there is a significant value of feature data for commodity procurement when procurement policy parameters are learned as functions of features. However, overfitting deteriorates the performance of data-driven solutions, which asks for ML extensions to improve out-ofsample generalization. Compared with an internal best-practice benchmark, DDA generates savings of on average 9.1 million euros per annum (4.33%) for 10 years of backtesting. Managerial implications: A practical benefit of DDA is that it yields simple but optimally structured decision rules that are easy to interpret and easy to operationalize. Furthermore, DDA is generalizable and applicable to many other procurement settings.

关键词： commodity procurement data-driven optimization machine learning prescriptive analytics

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Day-ahead aircraft routing with data-driven primary delay predictions

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期310卷 379-396页

作者： Birolini, Sebastian Jacquillat, Alexandre Univ Bergamo Dept Management Informat & Prod Engn Via 7 Pasubio 7b I-24044 Dalmine BG Italy MIT Sloan Sch Management 100 Main St Cambridge MA 02142 USA

Flight delays are major sources of disruptions in airline operations. To mitigate them, day-ahead aircraft routing aims to create flight sequences that can absorb delays and minimize their propagation. However, flight delays are unknown ahead of operations;moreover, predicting delays is complicated by the fact that historical data encompass both primary delays (arising from exogenous sources) and propagated delays (arising from cascading effects in an airline's network). This paper thus develops predictive and prescriptive analytics models to forecast primary delays and to optimize day-ahead aircraft routing toward delay mitigation. We develop a quantile regression model to reconstruct primary delays from historical data, and an ensemble machine learning model to predict them based on flight-level features, environmental features, and traffic features-estimated via a queuing model of airport operations. Then, we formulate deterministic and stochastic optimization models to support day-ahead aircraft routing. Using real-world data from Vueling Airlines , we evaluate the models out of sample against real-world counterfactuals. Results show that our predictive model achieves a mean absolute error of 7-8 minutes and that our prescriptive models can reduce delay costs by 3-5%. This paper shows the benefits of predictive and prescriptive analytics to enhance the robustness of airline operations by (i) creating shorter aircraft rotations, and (ii) strategically allocating schedule slack to avoid the propagation of long delays in later phases of the day. This research led to the deployment of the models in collaboration with the Vueling data science unit.

关键词： Transportation Aircraft routing prescriptive analytics Primary delay prediction

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Maximizing Online Revisiting and Purchasing: A Clickstream-Based Approach to Enhancing Customer Lifetime Value

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JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 2023年第2期40卷 470-502页

作者： Jabr, Wael Ghoshal, Abhijeet Cheng, Yichen Pavlou, Paul Penn State Univ Smeal Coll Business University Pk PA USA Univ Illinois Gies Coll Business Champaign IL USA Georgia State Univ J Mack Robinson Coll Business Atlanta GA USA Univ Houston Bauer Coll Business Houston TX USA Univ Houston Bauer Coll Business 4800 Calhoun Rd Houston TX 77004 USA

Online retailers are increasingly focused on maintaining a long-term relationship with customers, encouraging repeat visits rather than single-time purchases to increase customer lifetime value. To help retailers maximize the probabilities of customers' revisiting and purchasing, we develop a two-stage model to better characterize and predict these two fundamental customer activities. In the first stage, we characterize the propensity of a customer revisiting the retailer's website. In the second stage, we develop a stochastic model that predicts revisits while also incorporating individual customer heterogeneity in exerted search effort during repeated visits. This heterogeneity is based on individual customer preferences in the choice of consideration sets, product information, pricing, and the search environment. Using customer level clickstream data, we show that our approach is not only better at predicting repeat customer visits, compared to existing methods, but also explainable and managerially interpretable. Most importantly, using computationally efficient simulation-based prescriptive analytics, we leverage our modeling approach to propose practical intervention strategies that maximize the joint likelihoods of customers revisiting and purchasing at the individual customer level.

关键词： Clickstream data probabilistic modeling prescriptive analytics explainable modeling lifetime value online retail e-retail online repurchasing

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Generalization Bounds in the Predict-Then-Optimize Framework

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MATHEMATICS OF OPERATIONS RESEARCH 2023年第4期48卷 2043-2065页

作者： El Balghiti, Othman Elmachtoub, Adam N. Grigas, Paul Tewari, Ambuj Columbia Univ Dept Ind Engn & Operat Res New York NY 10027 USA Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA

The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters in contrast to the prediction error of the parameters. This loss function is referred to as the smart predict then-optimize (SPO) loss. In this work, we seek to provide bounds on how well the performance of a prediction model fit on training data generalizes out of sample in the context of the SPO loss. Because the SPO loss is nonconvex and non-Lipschitz, standard results for deriving generalization bounds do not apply. We first derive bounds based on the Natarajan dimension that, in the case of a polyhedral feasible region, scale at most logarithmically in the number of extreme points but, in the case of a general convex feasible region, have linear dependence on the decision dimension. By exploiting the structure of the SPO loss function and a key property of the feasible region, which we denote as the strength property, we can dramatically improve the dependence on the decision and feature dimensions. Our approach and analysis rely on placing a margin around problematic predictions that do not yield unique optimal solutions and then providing generalization bounds in the context of a modified margin SPO loss function that is Lipschitz continuous. Finally, we characterize the strength property and show that the modified SPO loss can be computed efficiently for both strongly convex bodies and polytopes with an explicit extreme point representation.

关键词： generalization bounds prescriptive analytics regression predict-then-optimize

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Analytical Problem Solving Based on Causal, Correlational and Deductive Models

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AMERICAN STATISTICIAN 2023年第1期77卷 51-61页

作者： de Mast, Jeroen Steiner, Stefan H. Nuijten, Wim P. M. Kapitan, Daniel Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON Canada Jheronimus Acad Data Sci Den Bosch Netherlands Eindhoven Univ Technol Eindhoven Artificial Intelligence Syst Inst Eindhoven Netherlands

Many approaches for solving problems in business and industry are based on analytics and statistical modeling. Analytical problem solving is driven by the modeling of relationships between dependent (Y) and independent (X) variables, and we discuss three frameworks for modeling such relationships: cause-and-effect modeling, popular in applied statistics and beyond, correlational predictive modeling, popular in machine learning, and deductive (first-principles) modeling, popular in business analytics and operations research. We aim to explain the differences between these types of models, and flesh out the implications of these differences for study design, for discovering potential X/Y relationships, and for the types of solution patterns that each type of modeling could support. We use our account to clarify the popular descriptive-diagnostic-predictive-prescriptive analytics framework, but extend it to offer a more complete model of the process of analytical problem solving, reflecting the essential differences between causal, correlational, and deductive models.

关键词： Business analytics Causal inference Machine learning Operations research Predictive modeling prescriptive analytics Problem solving

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Leveraging machine learning and optimization models for enhanced seaport efficiency

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MARITIME ECONOMICS & LOGISTICS 2025年 1-42页

作者： Jahangard, Mahdi Xie, Ying Feng, Yuanjun Anglia Rusk Univ Cambridge England Cranfield Univ Sch Management Cranfield MK43 0AL England Univ Liverpool Management Sch Dept Operat & Supply Chain Management Liverpool England

This study provides an overview of the application of predictive and prescriptive analytics in seaport operations and explore the potential of integrating predictive outputs into prescriptive analytics to advance research in this field. A systematic review of 124 papers was performed to identify and classify key topics based on application areas, types of applications, and employed techniques. Our findings show a growing interest in developing either predictive or prescriptive analytics models to improve seaport operational efficiency. However, there is limited research combining predictive outputs with prescriptive analytics for data-driven decision-making. Additionally, the hybridization of machine learning and operations research techniques remains underexplored. One promising area is applying machine learning models, such as reinforcement learning, to solve optimization problems. Predictive maintenance and data-enabled operational control measures for port equipment and facilities are also highlighted as interesting future research areas.

关键词： Seaport operations Port efficiency Predictive analytics prescriptive analytics Machine learning Optimization Systematic literature review

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