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prescriptive analytics in public-sector decision-making: A framework and insights from charging infrastructure planning

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期291卷 379-393页

作者： Brandt, Tobias Wagner, Sebastian Neumann, Dirk Erasmus Univ Rotterdam Sch Management POB 1738 NL-3000 DR Rotterdam Netherlands Geospin GmbH Konrad Goldmann Str 5b D-79100 Freiburg Germany Univ Freiburg Pl Alten Synagoge D-79098 Freiburg Germany

In this work, we investigate the challenges public-sector organizations face when seeking to leverage prescriptive analytics and provide insights into the public value such data-driven tools and methods can provide. Using the strategic triangle of value, legitimacy, and operational capacity as a starting point, we derive a framework to assess public-sector prescriptive analytics initiatives, along with six guiding questions that structure the assessment process. We present a case study applying prescriptive analytics to the placement of charge points in urban areas, a critical challenge many municipalities are currently facing in the transition towards electric mobility. Reflecting on the analytics application as well as its development and implementation process through the guiding questions, we derive key lessons for public-sector organizations seeking to apply prescriptive analytics. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： Decision support systems Public value prescriptive analytics Smart city Electric mobility

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prescriptive analytics for Intelligent Transportation Systems with Uncertain Demand

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2023年第12期149卷

作者： Wang, Huiwen Yi, Wen Tian, Xuecheng Zhen, Lu Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Hong Kong 999077 Peoples R China Shanghai Univ Sch Management 99 Shang Da Rd Shanghai 200444 Peoples R China

Data-driven traffic modeling is revolutionizing transportation systems and provides numerous opportunities for achieving high-quality transportation services. A major challenge in optimizing transportation systems is uncertain transportation demand. With the availability of historical data on transportation demand, the uncertain transportation demand can be better modeled, and thereby practitioners can formulate well-informed transportation scheduling decisions. In this paper, we propose three effective and economical transport scheduling strategies using mathematical programming, leveraging big data to extract useful contextual information. Additionally, a perfect-foresight optimization model is proposed to evaluate our proposed data-driven strategies. Results show a negligible optimality gap (i.e., 0.47%) between the optimal solution derived by the perfect-foresight model and the scheduling plans derived by our data-driven strategies. Overall, this paper contributes to the field of transportation engineering by innovatively applying data science, mathematical modeling, and optimization techniques.

关键词： Data-driven transportation modeling prescriptive analytics Large-scale optimization Uncertain demand

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Measuring the effects of customized targeted promotions on retailer profits: prescriptive analytics using basket-level econometric analysis

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JOURNAL OF MARKETING analytics 2024年第4期12卷 1028-1051页

作者： Chaudhry, Alexander Heilman, Carrie Seetharaman, P. B. Southern Utah Univ Leavitt Sch Business 351 W Univ Blvd Cedar UT 84720 USA Univ Virginia McIntire Sch Commerce 125 Ruppel Dr Charlottesville VA 22903 USA Washington Univ St Louis Olin Business Sch One Brookings Dr St St Louis MO 63130 USA

This study empirically estimates the expected basket-level demand effects, as well as the expected store profit effects, of three different customization levels of retailer promotions. Using data from a national grocery retailer in the U.S., we estimate a household's contemporaneous purchase incidence outcomes in 28 frequently shopped categories. Estimating the cross-category dependencies in purchase incidence as a function of exposure to levels of customized promotions, allows us to measure the effect of each campaign on expected retailer profit and implement prescriptive analytics to identify the appropriate multi-level coupon mix for maximizing profits. We find all three levels of coupon customization result in per-customer returns, but that medium customization leads to the highest incremental expected profit, while high customization generates the highest expected profit. The results provide insights to retailers about investing in more customized promotional efforts, with a detailed cross-category perspective into where such value is gained.

关键词： Customized coupons Targeted promotions Retail profitability Basket-level demand modeling prescriptive analytics

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

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Procedia Computer Science 2023年 225卷 676-685页

作者： Martin Haug Chiara Bordin Sambeet Mishra Julien Moisan

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.

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

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prescriptive analytics for a maritime routing problem

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OCEAN & COASTAL MANAGEMENT 2023年第1期242卷

作者： Tian, Xuecheng Yan, Ran Wang, Shuaian Laporte, Gilbert Hong Kong Polytech Univ Fac Business Dept Logist & Maritime Studies Hung Hom Hong Kong Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore Singapore HEC Montreal Dept Decis Sci Montreal PQ Canada Univ Bath Sch Management Bath England

Port state control (PSC) serves as the final defense against substandard ships in maritime transportation. The port state control officer (PSCO) routing problem involves selecting ships for inspection and determining the inspection sequence for available PSCOs, aiming to identify the highest number of deficiencies. Port authorities face this problem daily, making decisions without prior knowledge of ship conditions. Traditionally, a predictthen-optimize framework is employed, but its machine learning (ML) models' loss function fails to account for the impact of predictions on the downstream optimization problem, potentially resulting in suboptimal decisions. We adopt a decision-focused learning framework, integrating the PSCO routing problem into the ML models' training process. However, as the PSCO routing problem is NP-hard and plugging it into the training process of ML models requires that it be solved numerous times, computational complexity and scalability present significant challenges. To address these issues, we first convert the PSCO routing problem into a compact model using undominated inspection templates, enhancing the model's solution efficiency. Next, we employ a family of surrogate loss functions based on noise-contrastive estimation (NCE) for the ML model, requiring a solution pool treating suboptimal solutions as noise samples. This pool represents a convex hull of feasible solutions, avoiding frequent reoptimizations during the ML model's training process. Through computational experiments, we compare the predictive and prescriptive qualities of both the two-stage framework and the decision-focused learning framework under varying instance sizes. Our findings suggest that accurate predictions do not guarantee good decisions;the decision-focused learning framework's performance may depend on the optimization problem size and the training dataset size;and using a solution pool containing noise samples strikes a balance between training efficiency a

关键词： prescriptive analytics Predict -then -optimize Decision -focused learning Port state control (PSC) inspection Maritime routing

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Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期298卷 801-820页

作者： Soeffker, Ninja Ulmer, Marlin W. Mattfeld, Dirk C. Univ Vienna Dept Business Decis & Analyt Kolingasse 14-16 A-1090 Vienna Austria Tech Univ Munich Sch Management Arcisstr 21 D-80333 Munich Germany Tech Univ Carolo Wilhelmina Braunschweig Decis Support Grp Muhlenpfordtstr 23 D-38106 Braunschweig Germany

Stochastic dynamic vehicle routing problems have become an essential part of logistics and mobility ser-vices. In such problems, a sequence of vehicle routing decisions has to be made in reaction and anticipa-tion of newly revealed stochastic information. To this end, a variety of computational operations research methods has emerged in the literature, increasingly integrating potential future information in their de-cision making. The integration of information models into decision models via computational methods is known as prescriptive analytics, the most recent advance of business analytics. In this paper, we explore the existing work and future potential of prescriptive analytics for stochastic dynamic vehicle routing. We identify the characteristics of decision models and information models unique in stochastic dynamic ve-hicle routing and analyze how different methodology meets the characteristics' requirements. We use the insights to derive recommendations about promising methodology when approaching specific stochastic dynamic vehicle routing problems. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Routing Survey Stochastic dynamic vehicle routing prescriptive analytics

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prescriptive analytics - A Smart Manufacturing System for First-Time-Right Printing in Wire Arc Additive Manufacturing using a Digital Twin

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Procedia CIRP 2023年 118卷 759-764页

作者： Raven T. Reisch Lucas Janisch Joaquin Tresselt Tobias Kamps Alois Knoll Siemens AG Otto-Hahn-Ring 6 Munich Germany Technical University of Munich Boltzmannstraße 4 Munich Germany

First-time-right printing is needed for extensive industrialization of Wire Arc Additive Manufacturing. However, due to process instabilities defects can occur even if suitable process parameter were chosen, resulting in production scrap due to an insufficient part quality. In this paper, we propose a smart manufacturing system which enables the compensation of previously created defects by means of a Digital Twin. We predict the future position of the welding torch, analyze its spatial context and adapt the process parameter if needed accordingly to compensate defects. Using this approach, a fault-tolerant manufacturing process is enabled, resulting in a first-time-right process.

关键词： Digital Twin Wire Arc Additive Manufacturing Smart Manufacturing prescriptive analytics Process Control First-Time-Right Printing

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Leveraging prescriptive analytics for Strategic Business Optimization: Real-World Applications and Future Prospects 2

Leveraging Prescriptive Analytics for Strategic Business Opt...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Arputharaj, Arjun Roshan Agrawal, Chinmay Beeraka, Krishna Chaitanya Balani, Simran Vishwakarma Institute of Technology Department of Mechanical Engineering Maharashtra Pune India Firenix Technologies Pvt Ltd. Department of Projects Maharashtra Pune India Department of Insurance Lti Mindtree Tamil Nadu Chennai India Sharekhan Department of Finance Maharashtra Pune India

ISBN: (纸本)9798350369960

The application of analytics in business has shown meteoric growth during the past several years. Businesses on every continent are turning to various methods of data analysis to improve their comprehension of the requirements and preferences of their clientele. As more businesses realise that predictive analytics helps them to lower risks, make informed decisions, and offer differentiated customer experiences, the issue of predictive analytics has become an increasingly popular one in the landscape of analytics. As a direct consequence of this, the deployment of predictive analytics is gaining steam. However, the adoption rate is modest, and businesses are just beginning to scratch the surface when it comes to the possible uses of this technology. The benefits to the company might be enormous if the implementation is done correctly. However, there are several potential strategic dangers that should be taken into consideration. This study's primary objective is to investigate the shifting dimensions of analytics, emphasise the significance of predictive analytics, pinpoint the factors that determine the success of implementation, and discuss some of the potential advantages that could be gained from using this technology. Additionally, case studies of GE Aviation and Google about the application of predictive analysis were taken into consideration for this study. According to the findings of the research, technologies for predictive analytics enable organisations to forecast trends and act on opportunities before they become visible to the public. The Industrial Internet of Things (IIoT) and prescriptive analytics, which encompass supply chain optimisation, sustainability and emission control, and increased operational efficiency, are two areas in which GE continues to be an innovator. Workforce planning, learning and development, and employee well-being are future predictions made for Google using prescriptive analytics. © 2023 IEEE.

关键词： Big Data Business Intelligence (BI) prescriptive analytics Real-World Applications Strategic Business Optimization

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Classification and regression in prescriptive analytics: Development of hybrid models and an example of ship inspection by port state control

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COMPUTERS & OPERATIONS RESEARCH 2024年 163卷

作者： Yan, Ran Wang, Shuaian Zhen, Lu Jiang, Shuo Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Hong Kong Polytech Univ Fac Business Dept Logist & Maritime Studies Hung HomKowloon Hong Kong 999077 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

In prescriptive analytics, unknown quantities are involved in practical decision-making problems, and these unknown quantities need to be predicted using auxiliary data. A classic approach is to develop machine learning (ML) models to generate point estimates, which are then input to the decision making problem in a deterministic manner to prescribe the optimal decision. However, the limited quantity and inevitable errors in the auxiliary data lead to inaccurate predictions and thus sub-optimal decisions. One viable approach to addressing the above issue is to consider the uncertainties in data by inputting the conditional distributions of the unknown quantities on the auxiliary data to the optimization problem on hand, and the distributions are predicted by regression ML models. Meanwhile, it is observed that the quantitative target in some problems are discrete, and these properties are analogous to categorical targets in classification problems. Considering the fact that describing and estimating the distribution of categorical variables are much easier than quantitative variables, this study innovatively develops random forest (RF) models with regression and classification features to generate the distribution of quantitative targets that are discrete. Especially, nodes splitting criteria in the RF models is in a regression manner, while the outputs of individual decision trees and the whole RF model is in a classification manner. Numerical experiments using real port state control (PSC) inspection records and settings at the Hong Kong port are conducted to validate and compare the above prescriptive analytics approaches. The superiority of applying the newly proposed RF model into the development of prescriptive analytics approaches is also demonstrated.

关键词： Data-driven decision making prescriptive analytics Machine learning Ordinal classification

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