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data-driven optimization for drone delivery service planning with online demand

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

作者： Paula, Aditya Levin, Michael W. Waller, S. Travis Rey, David UNSW Sydney Sch Civil & Environm Engn Sydney NSW 2052 Australia Univ Minnesota Dept Civil Environm & Geoengn Minneapolis MN 55455 USA Tech Univ Dresden Fac Transport & Traff Sci Lighthouse Professorship Transport Modelling & Sim Dresden Germany Univ Cote dAzur SKEMA Business Sch Sophia Antipolis France

In this study, we develop an innovative data-driven optimization approach to solve the drone delivery service planning problem with online demand. Drone-based logistics are expected to improve operations by enhancing flexibility and reducing congestion effects induced by last-mile deliveries. With rising digitalization and urbanization, however, logistics service providers are constantly grappling with the challenge of uncertain real-time demand. This study investigates the problem of planning drone delivery service through an urban air traffic network to fulfill dynamic and stochastic demand. Customer requests-if accepted-generate profit and are serviced by individual drone flights as per request origins, destinations and time windows. We cast this stochastic optimization problem as a Markov decision process. We present a novel data-driven optimization approach which generates predictive prescriptions of parameters of a surrogate optimization formulation. Our solution method consists of synthesizing training data via lookahead simulations to train a supervised machine learning model for predicting relative link priority based on the state of the network. This knowledge is then leveraged to selectively create weighted reserve capacity in the network and via a surrogate objective function that controls the trade-off between reserve capacity and profit maximization to maximize the cumulative profit earned. Using numerical experiments based on benchmarking transportation networks, the resulting data-driven optimization policy is shown to outperform a myopic policy. Sensitivity analyses on learning parameters reveal insights into the design of efficient policies for drone delivery service planning with online demand.

关键词： data-driven optimization Online demand Markov decision process Drone delivery Service planning

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data-driven optimization for production planning with multiple demand features

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KYBERNETES 2025年第1期54卷 110-133页

作者： Su, Xiaoli Zeng, Lijun Shao, Bo Lin, Binlong Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China Univ Wisconsin Dept Ind & Syst Engn Madison WI USA

PurposeThe production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained ***/methodology/approachIn this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed data Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total *** results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach;in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service ***/valueCompared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially

关键词： Big data data-driven optimization Production planning Mixed-frequency data

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data-driven optimization for wind farm siting

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 155卷

作者： Arrieta-Prieto, Mario E. Schell, Kristen R. Univ Nacl Colombia Dept Stat Bogota 110131 Colombia Carleton Univ Dept Mech & Aerosp Engn Ottawa ON K1S 5B6 Canada

Both increasing power output and minimizing the variability of that output are key factors in a highly utilized wind farm. Finding a location that offers this type of wind resource requires the use of stochastic methods to explicitly model the uncertainty of wind power output. While the literature is rich with wind speed resource assessments, this work moves the field to wind farm power assessment at undeveloped locations. A wind farm power prediction is achieved with improved spatial process representation. The power prediction results are utilized directly in a wind farm location siting optimization model which endogenously incorporates uncertai n t y in wind power output. The overa l l framework for siting new wind farms, termed the Farm Finder tool, is demonstrated in a case study in the Electric Reliability Counci l of Texas (ERCOT). It is shown that the formu-lation of this model w i l l find an optimal solution to the siting problem on the Pareto front of high power output, with high reliability. Such location decisions improve the overa l l social welfare in the electricity market.

关键词： v-logit random forest Random-effects model data-driven optimization Genetic algorithm Wind power prediction

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data-driven optimization for seismic-resilient power network planning *

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COMPUTERS & OPERATIONS RESEARCH 2024年 166卷

作者： Oneto, Alfredo Lorca, Alvaro Ferrario, Elisa Poulos, Alan De La Llera, Juan Carlos Negrete-Pincetic, Matias Swiss Fed Inst Technol Reliabil & Risk Engn Inst Energy & Proc Engn Zurich Switzerland Pontificia Univ Catolica Chile Dept Ind & Syst Engn Santiago Chile Pontificia Univ Catolica Dept Elect Engn Santiago Chile Pontificia Univ Catolica Chile UC Energy Res Ctr Santiago Chile CIGIDEN Natl Res Ctr Integrated Nat Disaster & Management Macul Chile Pontificia Univ Catolica Chile Dept Struct & Geotech Engn Santiago Chile Stanford Univ Dept Civil & Environm Engn Stanford CA USA

Many regions of the planet are exposed to seismic hazards that can cause devastating consequences on power systems. Due to these systems' crucial role, the evaluation and planning for their safe and reliable operation are paramount. This paper develops a novel data -driven optimization framework to assess the power network's seismic resilience and plan cost-effective investments for its enhancement. Under a robust optimization scheme, an earthquake attacker-defender model finds the worst -case realization of random earthquake network contingencies within an uncertainty set defined with a large number of scenarios generated by state-of-the-art engineering methods. Moreover, data -driven stochastic -robust optimization is employed in a two -stage seismic -resilient power network planning model, leveraging multiple seismic sources' distributional information. Transmission line expansions and siting and sizing of battery energy storage systems are decided in the first stage, while the second stage decides operational variables. Experiments on a 281 -node Chilean power system provide insights for seismic -resilient planning and demonstrate the efficiency of the proposed approach.

关键词： OR in energy data-driven optimization Robust optimization Power systems resilience Seismic hazards

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data-driven optimization of coastal sea level monitoring: Leveraging known patterns for enhanced reconstruction

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REGIONAL STUDIES IN MARINE SCIENCE 2024年 80卷

作者： Kartal, Elif Altunkaynak, Abdusselam Celik, Anil Istanbul Tech Univ Fac Civil Engn Hydraul & Water Resource Engn Div TR-34469 Istanbul Turkiye

Efficiently configuring sea level monitoring stations is crucial for obtaining accurate spatiotemporal data while managing operational and maintenance costs and addressing the challenges posed by missing data. This study focuses on optimizing the selection of stations within Turkey's coastal sea level monitoring network by leveraging the inherent lower dimensionality in data. The network consists of 18 stations distributed along Turkey's coastline. To identify dominant patterns in historical sea level data, Empirical Orthogonal Function (EOF) analysis was employed, followed by the application of the QR decomposition with column pivoting algorithm. Model performance is assessed using the Nash-Sutcliffe coefficient of efficiency (CE) and root mean square error (RMSE). Remarkably, the results demonstrated that reconstructing the entire dataset, encompassing all 18 stations was possible with a CE of 0.94 and an RMSE of 0.06. Even utilizing data from two or three stations alone achieves acceptable reconstruction accuracy. The effectiveness of EOF analysis combined with QR with column pivoting algorithm suggests promising applications in various scientific fields.

关键词： Sea level surface Station network optimization Empirical orthogonal function QR with column pivoting data-driven optimization

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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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data-driven optimization for last-mile delivery

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COMPLEX & INTELLIGENT SYSTEMS 2023年第3期9卷 2271-2284页

作者： Chu, Hongrui Zhang, Wensi Bai, Pengfei Chen, Yahong Capital Univ Econ & Business Sch Management & Engn Beijing 100070 Peoples R China Ocean Univ China Sch Econ Qingdao 266100 Shandong Peoples R China Beijing Wuzi Univ Beijing Intelligent Logist Syst Collaborat Innova Beijing 101149 Peoples R China

This paper considers how an online food delivery platform can improve last-mile delivery services' performance using multi-source data. The delivery time is one critical but uncertain factor for online platforms that also regarded as the main challenges in order assignment and routing service. To tackle this challenge, we propose a data-driven optimization approach that combines machine learning techniques with capacitated vehicle routing optimization. Machine learning methods can provide more accurate predictions and have received increasing attention in the operations research field. However, different from the traditional predict-then-optimize paradigm, we use a new smart predict-then-optimize framework, whose prediction objective is constructed by decision error instead of prediction error when implementing machine learning. Using this type of prediction, we can obtain a more accurate decision in the following optimization step. Efficient mini-batching gradient and heuristic algorithms are designed to solve the joint order assignment and routing problem of last-mile delivery service. Besides, this paper considers the mutual effect between routing decision and delivery time, and provides the corresponding solution algorithm. In addition, this paper conducts a computational study and finds that the proposed method's performance has an approximate 5% improvement compared with other methods.

关键词： data-driven optimization Last-mile delivery Machine learning Prediction Routing behavior

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data-driven optimization for Dynamic Shortest Path Problem Considering Traffic Safety

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第10期23卷 18237-18252页

作者： Jiang, Shan Zhang, Yilun Liu, Ran Jafari, Mohsen Kharbeche, Mohamed Johnson & Johnson Supply Chain Bridgewater NJ 08807 USA Shanghai Jiao Tong Univ Dept Ind Engn Shanghai 200240 Peoples R China Rutgers Univ New Brunswick Dept Ind & Syst Engn Piscataway NJ 08854 USA Qatar Univ Qatar Transportat & Traff Safety Ctr Doha Qatar

Traffic congestion is an inescapable problem that frustrates drivers in megacities. Although there is hardly a way to eliminate the congestion, it is possible to mitigate the impact through predictive methods. This paper develops a data-driven optimization approach for the dynamic shortest path problems (DSPP), considering traffic safety for urban navigations. The dynamic risk scores and travel times at different times and locations are estimated by the Safe Route Mapping (SRM) methodology and Long Short-Term Memory (LSTM) with Autoencoder, respectively, where possible variations in the future are considered. The DSPP is formulated as a mixed-integer linear programming problem under risk constraints to minimize the total travel cost, defined as the weighted sum of distance and travel time. To improve the efficiency of the DSPP, we design an improved tabu search with alternative initial-solution algorithms to accommodate various problem scales. Moreover, subgraph and self-adaptive insertion techniques are adopted as acceleration strategies to enhance computational efficiency further. Numerical experiments investigate the computational performance and the solution quality of our algorithm. The result shows satisfactory solution quality and computational efficiency with the proposed acceleration strategies compared to the CPLEX solver, a label-setting algorithm, and a state-of-the-art algorithm. Our algorithm can also compete with Google Maps regarding the travel cost in a real network in Manhattan, NY, USA, which is promising for Urban Navigations.

关键词： Navigation Routing Safety Heuristic algorithms Shortest path problem Real-time systems Stochastic processes data-driven optimization urban navigation dynamic shortest path problem tabu search risk prediction travel time prediction

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data-driven optimization Framework for Voltage Regulation in Distribution Systems

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IEEE TRANSACTIONS ON POWER DELIVERY 2022年第2期37卷 1344-1347页

作者： Hong, Tianqi Zhang, Yichen Argonne Natl Lab Energy Syst Div Lemont IL 60439 USA

This letter proposes a data-driven optimization framework for voltage regulation problems to address the challenge of model inaccuracy and parameter varying. To achieve online voltage optimization, the recursive kernel regression and interior point methods are integrated. The IEEE 123-Bus system and EPRI Ckt5 feeder are selected to validate the effectiveness of the proposed data-driven optimization framework. The proposed method is also compared with a linear function based method.

关键词： Voltage control optimization Kernel Mathematical models Load flow Voltage measurement Linear programming Recursive kernel regression data-driven optimization multiphase distribution system voltage control

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data-driven optimization: A Reproducing Kernel Hilbert Space Approach

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OPERATIONS RESEARCH 2022年第1期70卷 454-471页

作者： Bertsimas, Dimitris Kodur, Nihal MIT Sloan Sch Management 77 Massachusetts Ave Cambridge MA 02139 USA MIT Ctr Operat Res Cambridge MA 02139 USA

We present two methods, based on regression in reproducing kernel Hilbert spaces, for solving an optimization problem with uncertain parameters for which we have historical data, including auxiliary data. The first method approximates the objective function and the second approximates the optimizer. We provide finite sample guarantees and prove asymptotic optimality for both methods. Computational experiments suggest that at least the second method overcomes a curse of dimensionality that afflicts existing methods, extrapolates better to unseen data, and achieves a many-fold decrease in sample complexity even for small dimensions.

关键词： data-driven optimization prescriptive analytics reproducing kernel Hilbert space regression optimality

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