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

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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Rollout-based routing strategies with embedded prediction: A fish trawling application

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COMPUTERS & OPERATIONS RESEARCH 2023年 150卷

作者： Cakir, Fahrettin Thomas, Barrett W. Street, W. Nick Istanbul Sabahattin Zaim Univ Dept Business & Management Sci Istanbul Turkiye Univ Iowa Dept Business Analyt Iowa City IA USA

In an effort to replenish stocks after years of overfishing, the European Union has been implementing policies to restrict the catch in all fisheries including in the Baltic Sea. These regulations incentivize fishermen to seek operational efficiency to counter lower catch limits. In this study, we address the fish trawling problem as a stochastic dynamic orienteering problem and propose rollout-based routing policies. Using a prescriptive analytic approach, we propose several prediction models and combine them with dynamic routing policies, the combination of which forms a prescriptive model. We evaluate and validate the effectiveness of these prescriptive models using a Baltic dataset on the fish species cod (Gadus). We propose a spatiotemporal cross-validation procedure to fairly assess different prescriptive models. Our findings show that reoptimization-based rollout strategies coupled with simple prediction methods such as nearest neighbors perform better than prescriptive models that use complex spatiotemporal smoothing techniques.

关键词： Baltic cod fishery Cross validation Effort allocation Fishing behavior Fisheries management Kriging prescriptive analytics Rollout policies Stochastic dynamic orienteering

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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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A smart predict-then-optimize method for targeted and cost-effective maritime transportation

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2023年第1期172卷 32-52页

作者： Tian, Xuecheng Yan, Ran Liu, Yannick Wang, Shuaian Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Hong Kong Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore Singapore Hong Kong Polytech Univ Fac Business Hung Hom Hong Kong Peoples R China

In maritime transportation, port state control (PSC) is the last line of defense against substandard ships. During a PSC inspection, PSC officers (PSCOs) identify ship deficiencies that lead to a ship's detention, which can cause severe economic and reputational losses to the ship operator. Therefore, this study innovatively uses PSC inspection data to design ship maintenance plans for ship operators to minimize overall operational costs. We identify three types of operational costs associated with each deficiency code: inspection cost, repair cost, and risk cost in the ship operators' decision-making process. The risk cost of a deficiency code is strongly related to the detention contribution of the deficiency items under a deficiency code, as indicated by the feature importance of that code in the random forest (RF) model used to predict detention outcomes. To design ship maintenance plans, the sequential predict-then-optimize (PO) method typically solves the optimization problem using input parameters, including the predicted probabilities of having deficiency items under each code and the three types of operational costs. However, the loss function in this two-stage framework does not consider the effect of predictions on the downstream decisions. Hence, we use a smart predict-then-optimize (SPO) method using an ensemble of SPO trees (SPOTs). Each SPOT uses an SPO loss function that measures the sub-optimality of the decisions derived from the predicted parameters. By exploiting the structural properties of the optimization problem analyzed in this study, we demonstrate that training an SPOT for this problem can be simplified tremendously by using the relative class frequency of true labels within a leaf node to yield a minimum SPO loss. Computational experiments show that the SPO-based ship maintenance scheme is superior to other schemes and can reduce a ship's total operating expenses by approximately 1% over the PO-based scheme and by at least 3% over schemes

关键词： prescriptive analytics Data -driven optimization Port state control (PSC) inspection Ship maintenance planning

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Fuzzy optimisation for business analytics: A bibliometric analysis

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 2615-2630页

作者： Cisneros, Luis Rivera, Gilberto Florencia, Rogelio Patricia Sanchez-Solis, J. Univ Autonoma Ciudad Juarez Div Multidisciplinaria Ciudad Univ Ciudad Juarez Chihuahua Mexico

Business analytics refers to the application of sophisticated tools to obtain valuable information from a large dataset that is generated by a company. Among these tools, fuzzy optimisation stands out because it helps decision-makers to solve optimisation problems considering the uncertainty that commonly occurs in application domains. This paper presents a bibliometric analysis following the PRISMA statement on the Dimensions database to obtain publications related to fuzzy optimisation applied to business domains. The purpose of this analysis is to gather useful information that can help researchers in this area. Atotal of 2,983 publications were analysed usingVOSviewer to identify the trend in the number of publications per year, relationships in terms in both the title and abstract of these publications, most influential publications, and relationships among journals, authors, and institutions.

关键词： PRISMA statement VOSviewer bibliometric insights scientific landscape fuzzy optimisation prescriptive analytics

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Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial

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MEDICAL DECISION MAKING 2023年第7-8期43卷 760-773页

作者： Leung, K. H. Benjamin Yousefi, Nasrin Chan, Timothy C. Y. Bayoumi, Ahmed M. M. Univ Toronto Dept Mech & Ind Engn 5 Kings Coll Rd Room RS308 Toronto ON M5S 3G8 Canada Scottish Ambulance Serv Edinburgh Scotland Queens Univ Smith Sch Business Kingston ON Canada St Michaels Hosp MAP Ctr Urban Hlth Solut Unity Hlth Toronto Toronto ON Canada Univ Toronto Dept Med Toronto ON Canada Univ Toronto Div Gen Internal Med Toronto ON Canada Univ Toronto Inst Hlth Policy Management & Evaluat Toronto ON Canada

Constrained optimization can be used to make decisions aimed at maximizing some quantity in the face of fixed limits, such as resource allocation problems in health where tradeoffs between alternatives are inherent, and has been applied in a variety of health-related applications. This tutorial guides the reader through the process of mathematically formulating a constrained optimization problem, providing intuitive explanations for each component within the problem. We discuss how constrained optimization problems can be implemented using software and provide instructions on how to set up a solution environment using Python and the Gurobi solver engine. We present 2 examples from the existing literature that illustrate different constrained optimization problems in health and provide the reader with Python code used to solve these problems as well as a discussion of results and sensitivity analyses. This tutorial can be used to help readers formulate constrained optimization problems in their own application domains.

关键词： constrained optimization Python tutorial prescriptive analytics resource allocation

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Smart agriculture: a literature review

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Journal of Management analytics 2023年第2期10卷 359-415页

作者： Disha Garg Mansaf Alam Department of Computer Science Jamia Millia IslamiaNew DelhiIndia

Industry 4.0 brings revolutionary changes to farming businesses by integrating emerging technologies such as the Internet of things(IoT),big data analytics(BDA),cloud computing(CC),and artificial intelligence(AI).These Emerging technologies are the potential enablers of data-driven smart *** the importance of data-driven agriculture,we provide a complete picture of current literature in smart agriculture by using a review classification framework divided into four categories:(i)Smart Farming Activities,(ii)BDA Levels,(iii)BDA Models,and(iv)BDA *** work uses the preferred reporting items for systematic reviews(PRISMA)methodology to review the current literature on intelligent farming.A total of 90 papers have been identified,and content analysis was conducted to mine knowledge in the domain for 2011–*** primary intention of this review is to clarify the most prominent farming activity,level of analytics,BDA models,and techniques in smart ***,the findings of our review analysis are discussed,and work suggestions are addressed for further research.

关键词： IoT BDA farming activities predictive analytics prescriptive analytics Industry 4.0 classiﬁcation framework

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