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prescriptive analytics for freeway traffic state estimation by multi-source data fusion

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 198卷

作者： Huang, Di Zhang, Jinyu Liu, Zhiyuan Liu, Ronghui Southeast Univ Sch Transportat Nanjing Peoples R China Univ Leeds Inst Transport Studies Leeds England

In the context of freeway traffic state estimation, this study introduces prescriptive analytics-also known as "predict-then-optimize"-for integrating data from Electronic Toll Collection (ETC) systems and traffic sensors. Traditional single-method data fusion techniques are constrained by inherent limitations. For instance, optimization-based methods are generally predicated on prior assumptions that may induce systematic biases, whereas machine learning approaches are frequently criticized for their lack of interpretability and their inability to elucidate underlying traffic mechanisms. To address these limitations, a novel "retrieval and matching" algorithm is proposed that integrates machine learning with optimization. First, the concept of the "state gene" is introduced to encapsulate traffic structural knowledge representing frequently occurring traffic patterns. In the retrieval phase, a heterogeneous graph conventional network is employed to predict potential state genes for a given scenario. In the matching phase, the predicted state genes are utilized to minimize the discrepancy with the current traffic state. This integration not only enhances the interpretability of the estimation process but also endows the optimization component with reverse inference capability through the incorporation of machine learning. Validation using real-world data from the G92 Freeway in Zhejiang, China, demonstrates high accuracy, yielding Mean Absolute Percentage Errors (MAPE) of 1.12-1.65 % during peak periods and 1.28-1.67 % during off-peak periods.

关键词： Traffic state estimation Data fusion prescriptive analytics Predict-then-optimize State genes

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prescriptive analytics for dynamic multi-criterion decision making considering learned knowledge of alternatives

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Yang, Shanshan Liao, Huchang Wu, Xingli Sichuan Univ Business Sch Chengdu 610064 Peoples R China

Dynamic multi-criterion decision making (DMCDM) has advantages in dealing with decision problems since it takes into account multi-stage decision-making information that may change over time. Previous studies have integrated performance predictions into DMCDM by mining historical trends, but faced limitations in identifying nonlinear features in historical data. Additionally, criteria interactions were less considered when determining criteria weights and the characteristics of decision matrix at each stage were insufficiently utilized when determining time weights. To address these issues, this study develops a prescriptive analytic framework by combining machine learning and DMCDM methods to predict future performance of alternatives and rank them, in which criteria interactions are considered when determining criteria weights and a grey relation coefficient is introduced in an optimization model to measure time weights. The proposed model is validated by a case study of selecting hotels from ***. The comparative analysis shows the superiority of considering the future performance of alternatives predicted by machine learning methods in DMCDM and the effectiveness of the time weighting model with the introduction of the grey relational coefficient. The results provide forward-looking decision-making suggestions for tourism agencies to select hotels as partners.

关键词： Multi-criterion analysis Dynamic decision making prescriptive analytics Machine learning Fuzzy cognitive map

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prescriptive analytics motivating distance learning students to take remedial action: A case study of a student-facing dashboard 25

Prescriptive analytics motivating distance learning students...

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15th International Conference on Learning analytics and Knowledge

作者： Herodotou, Christothea Carr, Jessica Shrestha, Sagun Comfort, Catherine Bayer, Vaclav Maguire, Claire Lee, John Mulholland, Paul Fernandez, Miriam Open Univ Milton Keynes Bucks England

ISBN: (纸本)9798400707018

Student-facing learning analytics dashboards aim to help students to monitor their study progress, achieve learning goals and develop self-regulation skills. Only few of them present personalised data visualisations and aim to develop agentic students who take remedial action to improve their study habits, learning and performance. In this paper, a student-facing dashboard, designed following principles of participatory research, was tested with 30 undergraduate students, who engaged with it over a period of 4 to 15 weeks and while studying an online course. This is one of the few dashboards available that presents all different types of analytics to students: descriptive, predictive and prescriptive. A mixed methods approach was used to assess its usefulness and impact on motivation to study and take remedial action to support learning. Data analysis show-cased that such a dashboard can be "a roadmap to success" by motivating students to study more and improve their performance, in addition to helping with monitoring, planning and reflection. While all dashboard features were perceived as being useful, special value was placed on prescriptive elements, in particular material recommendations and contacting tutors and university support teams, emphasizing the significance of making explicit on a dashboard the actions students should take to improve their performance. Implications for future studies are discussed.

关键词： learning analytics dashboards student-facing dashboards prescriptive analytics

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Data-driven inventory control for large product portfolios: A practical application of prescriptive analytics

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期322卷 254-269页

作者： Schmidt, Felix G. Pibernik, Richard Julius Maximilians Univ Wurzburg Chair Logist & Quant Methods Sanderring 2 D-97070 Wurzburg Germany Zaragoza Logist Ctr Ave Ranillas 5 Edificio 5A Zaragoza 50018 Spain

Motivated by the real-world inventory management problem of a large network of pharmacies, this paper proposes and studies a practically relevant prescriptive analytics approach for data-driven dynamic inventory control of large portfolios of interrelated products. We extend existing research on weighted Sample Average Approximation by integrating a 'global learning' model that effectively exploits cross-learning opportunities within the product portfolio. The results of an extensive numerical evaluation on real-world data suggest that our approach outperforms relevant benchmarks-in particular, models that rely on 'local learning' strategies where weight functions are trained separately for each product. The numerical results also allow us to derive important practical and structural insights regarding the value of contextual information in our global learning framework.

关键词： Dynamic inventory control prescriptive analytics Machine learning Cross-learning

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Predictive and prescriptive analytics for robust airport gate assignment planning in airside operations under uncertainty

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 195卷

作者： Zhang, Chenliang Jin, Zhongyi Ng, Kam K. H. Tang, Tie-Qiao Zhang, Fangni Liu, Wei Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hung Hom Hong Kong Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Univ Hong Kong Dept Data & Syst Engn Pok Fu Lam Hong Kong Peoples R China

With the increasing demand for air transport, numerous airports have exceeded their available capacity, resulting in more frequent congestion and disruptions. Therefore, airport gate assignment plans must prioritise robustness to alleviate congestion, absorb disruptions, and maintain high service levels. Given the uncertainties in airside operations, providing robust decisions is challenging. To address this issue, we employ two prescriptive analytics approaches to develop airport gate assignment plans. These approaches leverage historical data, auxiliary data, and machine learning (ML) methods to enhance decision effectiveness and robustness. Initially, we adopt a predict-then-optimise approach, utilising ML methods to predict aircraft arrival times. These predictions are then used as input for a deterministic model of the airport gate assignment problem (AGAP). Subsequently, we explore an estimate-then-optimise approach. In this approach, we first estimate the distribution of uncertain aircraft arrival times using ML methods. Then, we solve the two-stage stochastic programming model for the AGAP based on the estimated distribution. Given the complexity of the estimate-then- optimise approach, we develop an effective scenario selection strategy, the cluster-based scenario reduction (CSR) method, to maintain tractability while ensuring decision performance. Concurrently, we develop an efficient exact solution method, the Benders-based branch-and- cut (BBC) method, to effectively handle larger and more complex test instances. Numerical experiments using real-world data from Xiamen Gaoqi International Airport demonstrate the effectiveness of the CSR and BBC methods. The CSR method performs better with a smaller sample size, while the BBC method significantly enhances computational performance compared to commercial solvers. These proposed methods improve the tractability and scalability of the estimate-then-optimise approach. Notably, the estimate-then-optimise approa

关键词： Airport gate assignment problem prescriptive analytics Machine learning Stochastic programming Benders-based branch-and-cut

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Identifying enablers for the successful deployment of prescriptive analytics - a multiple case study

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EUROPEAN BUSINESS REVIEW 2025年第2期37卷 245-277页

作者： Hirvonen, Marjut Kauppi, Katri Liesio, Juuso Aalto Univ Sch Business Dept Informat & Serv Management Espoo Finland

PurposeAlthough it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive ***/methodology/approachThe authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top *** findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational ***/valueThe results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

关键词： Decision support system implementation prescriptive analytics Case study Interviews

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prescriptive analytics for Flexible Capacity Management

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MANAGEMENT SCIENCE 2022年第3期68卷 1756-1775页

作者： Notz, Pascal M. Pibernik, Richard Julius Maximilian Univ Wurzburg Dept Logist & Quantitat Methods Business Adm D-97070 Wurzburg Germany Zaragoza Logist Ctr Zaragoza 50018 Spain

Motivated by the real-world problem of a logistics company, this paper proposes a novel distribution-free prescriptive analytics approach-termed kemelized empirical risk minimization (kemelized ERM)-to solve a complex two-stage capacity planning problem with multivariate demand and vector-valued capacity decisions and compares this approach both theoretically and numerically with an extension of the well-known sample average approximation (SAA) approach termed weighted SAA. Both approaches use integrated machine learning algorithms to prescribe capacities directly from historical demand and numerous features (covariates) without having to make assumptions about the underlying multivariate demand distribution. We provide extensive analytical insights into both approaches. Most important, we prove the universal approximation property for the kemelized ERM approach when using a universal (data-independent) kernel and show how out-of-sample guarantees can be derived for various kernels. We demonstrate the applicability of both approaches to a real-world planning problem and evaluate their performance relative to traditional parametric approaches that first estimate a multivariate demand distribution and then solve a stochastic optimization problem and a nonparametric approach (SAA). Our results suggest that the two prescriptive analytics approaches can result in substantial performance improvements of up to 58% compared with traditional approaches. Additional numerical analyses shed light on the behavior and performance drivers of the various approaches and demonstrate that in our case, the prescriptive approaches are much more robust to variations of exogenous cost parameters than traditional approaches.

关键词： prescriptive analytics machine learning data-driven operations management capacity management

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prescriptive analytics for a multi-shift staffing problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期305卷 887-901页

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

Motivated by the work with an industry partner, this paper proposes and examines novel data-driven approaches to solve a certain type of capacity-sizing problem, which we term the multi-shift staffing problem (MSSP). In our MSSP, a company has to staff multiple shifts for each workday in the presence of uncertain arrival rates that vary throughout the day and patient "customers" that do not abandon the queue while waiting for a service, but who must be served by some pre-defined time. Drawing on estab-lished methods in both capacity management and prescriptive analytics, we propose to use fluid and sta-tionary approximations of the demand arrival process to apply tailored prescriptive analytics approaches to determine staffing levels for multiple interrelated shifts. The prescriptive analytics approaches rely on machine learning techniques that incorporate a detailed representation of the non-stationary structure of arrivals and leverage extensive auxiliary data. In particular, we adapt established prescriptive analyt-ics approaches-weighted sample average approximation and kernelized empirical risk minimization-and propose a new optimization prediction approach to solving the multi-shift staffing problem. Using a case study that is based on extensive data from our project partner, the maintenance service provider, we demonstrate the applicability of these approaches, highlight their benefits over traditional "estimate then optimize" approaches, and shed light on their structural properties and performance drivers.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Decision support systems prescriptive analytics Machine learning Data-driven OM Queuing

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prescriptive analytics for inventory management in health

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2021年第10期72卷 2211-2224页

作者： Galli, Leonardo Levato, Tommaso Schoen, Fabio Tigli, Luca Univ Firenze Florence Italy

In this paper we propose an innovative optimization method for drug replenishment in a hospital ward. Although hospital logistics has in general shifted towards a zero local inventory policy, still most wards keep an inventory of drugs necessary for short time periods (between one and three days). The model we propose makes use of machine learning coupled with stochastic optimization in order to take into consideration both the historical usage patterns of drugs and the ward's current situation to minimize inventory levels as well as the necessity for emergency replenishments. Differently from generic inventory models, drugs are associated with patients who tend to consume a similar amount of drugs every day during their stay. Our aim in this paper was to show that, with suitably defined ward features, a machine learning method is capable of selecting a set of scenarios over which a stochastic optimization approach is capable of delivering good decisions, both in terms of small order quantities and of service quality (avoiding emergency replenishment orders). An ad hoc feature engineering procedure has been designed to exploit ward features, easily collected every day in the ward. Moreover, we introduce a new validation metric tailored for selecting hyperparameters to balance between optimality and robustness. Our results, based on a dataset composed of one year of drug administrations in an Italian ward, support the superiority of the proposed approach versus traditional ones.

关键词： prescriptive analytics machine learning inventory optimization stochastic optimization K-nearest neighbors decision trees random forest XGBoost

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prescriptive analytics models for vessel inspection planning in maritime transportation

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Yang, Ying Yan, Ran Wang, Shuaian Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Hong Kong Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore Singapore

Port state control (PSC) inspections are crucial for maritime safety and pollution reduction. The inspection process involves identifying high -risk vessels, allocating surveyors, and conducting onboard checks. This study aims to optimize the selection and assignment process through a two -stage framework, balancing the benefits of identifying deficiencies against the costs of inspection delays. Initially, we employ a predict -thenoptimize approach, predicting the number of vessel deficiencies using a k -nearest neighbor (kNN) model, which informs the inspection decisions. However, due to the nonlinear nature of the optimization in relation to predicted values, we also explore an estimate -then -optimize framework that estimates distributions of potential deficiencies. We enhance two prescriptive analytics models and introduce an advanced global model with a pre-processing algorithm for better distribution estimation. A case study using data from the Hong Kong port demonstrates that the estimate -then -optimize models surpass the predict -then -optimize approach, offering solutions closer to the optimal policy. Furthermore, our improved model outperforms existing methods, proving more effective in practical applications.

关键词： Maritime transportation Vessel inspection K nearest neighbor Estimate-then-optimize prescriptive analytics

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